Sample Preparation for Trace Element Analysis

COMPREHENSIVE ANALYTICAL CHEMISTRY ELSEVIER B.V. Sara Burgerhartstraat 25 P.O. Box 211, 1000 AE Amsterdam, The Nether...

Author: Zoltan Mester | Ralph E. Sturgeon

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COMPREHENSIVE ANALYTICAL CHEMISTRY

ELSEVIER B.V. Sara Burgerhartstraat 25 P.O. Box 211, 1000 AE Amsterdam, The Netherlands q 2003 Elsevier B.V. All rights reserved. This work is protected under copyright by Elsevier Science, and the following terms and conditions apply to its use: Photocopying Single photocopies of single chapters may be made for personal use as allowed by national copyright laws. Permission of the Publisher and payment of a fee is required for all other photocopying, including multiple or systematic copying, copying for advertising or promotional purposes, resale, and all forms of document delivery. Special rates are available for educational institutions that wish to make photocopies for non-profit educational classroom use. Permissions may be sought directly from Elsevier’s Science & Technology Rights Department in Oxford, UK; phone: (+44) 1865 843830, fax: (+44) 1865 853333, e-mail: [email protected]. You may also complete your request on-line via the Elsevier Science homepage (http://www.elsevier.com), by selecting ‘Customer Support’ and then ‘Obtaining Permissions’. In the USA, users may clear permissions and make payments through the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, USA; phone: (+1) (978) 7508400, fax: (+1) (978) 7504744, and in the UK through the Copyright Licensing Agency Rapid Clearance Service (CLARCS), 90 Tottenham Court Road, London W1P 0LP, UK; phone: (+44) 207 631 5555; fax: (+44) 207 631 5500. Other countries may have a local reprographic rights agency for payments. Derivative Works Tables of contents may be reproduced for internal circulation, but permission of Elsevier Science is required for external resale or distribution of such material. Permission of the Publisher is required for the other derivative works, including compilations and translations. Electronic Storage or Usage Permission of the Publisher is required to store or use electronically any material contained in this work, including any chapter or part of a chapter. Except as outlined above, no part of this work may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without prior written permission of the Publisher. Address permissions requests to: Elsevier Science Global Rights Department, at the mail, fax and e-mail addresses noted above. Notice No responsibility is assumed by the Publisher for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions or ideas contained in the material herein. Because of rapid advances in the medical sciences, in particular, independent verification of diagnoses and drug dosages should be made. First edition 2003 Library of Congress Cataloging in Publication Data / edited by Zoltań Mester and Ralph Sturgeon p. cm. -- (Comprehensive analytical chemistry ; v. 41) Includes bibliographical references and index. ISBN 0-444-51101-6 (pbk. : alk. paper) -- ISBN 0-444-51101-6 (hardbound : alk. paper) 1. xxxx 2. xxxx 3. xxxx I. Mester, Zoltań and Sturgeon, Ralph II. Series. QD75. W75 v. 41 [QD75.4.S24] 5430 .02--dc21

2002072248

British Library Cataloguing in Publication Data A catalogue record from the British Library has been applied for. ISBN: 0-444-51101-6 ISSN: 0166-526X 1 The paper used in this publication meets the requirements of ANSI/NISO Z39.48-1992 (Permanence of Paper). * Printed in The Netherlands.

COMPREHENSIVE ANALYTICAL CHEMISTRY ADVISORY BOARD

Professor A.M. Bond Monash University, Clayton, Victoria, Australia Dr T.W. Collette US Environmental Protection Agency, Athens, GA, U.S.A. Professor M. Grasserbauer Director of the Environment Institute, European Commission’ Joint Research Centre, Ispra, Italy Professor M.-C. Hennion Ecole Supe´rieure de Physique et de Chimie Industrielles, Paris, France Professor G. M. Hieftje Indiana University, Bloomington, IN, U.S.A. Professor G. Marko-Varga AstraZeneca, Lund, Sweden Professor D.L. Massart Vrije Universiteit, Brussels, Belgium Professor M.E. Meyerhoff University of Michigan, Ann Arbor, MI, U.S.A.

Wilson & Wilson’s COMPREHENSIVE ANALYTICAL CHEMISTRY

Edited by ´ D. BARCELO Research Professor Department of Environmental Chemistry IIQAB-CSIC Jordi Girona 18-26 08034 Barcelona Spain

Wilson & Wilson’s COMPREHENSIVE ANALYTICAL CHEMISTRY

VOLUME XLI

SAMPLE PREPARATION FOR TRACE ELEMENT ANALYSIS Edited by Z. MESTER R. STURGEON Institute for National Measurement Standards National Research Council 1500, Montreal Rd Ottawa, ON, KIA 0R6, Canada

2003 ELSEVIER AMSTERDAM – BOSTON – HEIDELBERG – LONDON – NEW YORK – OXFORD – PARIS – SAN DIEGO SAN FRANCISCO – SINGAPORE – SYDNEY – TOKYO

CONTRIBUTORS TO VOLUME XLI Freddy C. Adams Department of Chemistry, University of Antwerpen, Universiteitsplein 1, B-2610 Antwerpen, Belgium. [email protected] Roberto Alzaga Environmental Chemistry Department, IIQAB-CID-CSIC, Jordi Girona, 18, E-08034 Barcelona, Spain. Scott Anderson Air Liquide – Balazs Analytical Services, 46409 Landing Pky, Frement, CA 94538, USA. Kevin Ashley U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, 4676 Columbia Parkway, Mailstop R-7, Cincinnati, OH 45226-1998, USA. [email protected] David P. Baldwin Ames Laboratory, Iowa State University, Ames, IA 50011,USA David Barclay CEM Corporation, 3100 Smith Farm Road, P.O. Box 200, Matthews, North Carolina 28106-0200, USA. [email protected] Ronald R. Barefoot Department of Geology, Earth Sciences Centre, 22 Russell Street, Toronto, Ontario, M5S 3B1, Canada. Douglas C. Baxter Division of Chemistry, Lulea˚ University of Technology, SE-971 87 Lulea˚, Sweden. [email protected] Josep M. Bayona Environmental Chemistry Department, IIQAB-CID-CSIC, Jordi Girona, 18, E-08034 Barcelona, Spain. [email protected] Maria Betti European Commission, JRC-ITU, P.O. Box 2340, 76125 Karlsruhe, Germany vi

Contributors to volume XLI

Robert I. Botto Analytical Services Laboratory, Baytown Chemical Plant Laboratory, 4500 Bayway Dr. Baytown, TX 77520, USA. [email protected] Brice Bouyssiere CNRS UMR 5034 Helioparc 2, av. Pr. Angot F-64053 PAU, France. Yong Cai Department of Chemistry and Southeast Environmental Research Center, Florida International University, Miami, Florida 33199, USA. cai@fiu.edu Carmen Camara Departamento de Quimica Analitica Facultad de Ciencias Quimicas, Universidad Complutense de Madrid 28040 Madrid, Spain. [email protected] Vale´rie Camel Institut National Agronomique Paris-Grignon, Laboratoire de Chimie Analytique, 16 rue Claude Bernard, 75231 Paris Cedex 05, France. [email protected] Joseph A. Caruso Department of Chemistry, University of Cincinnati, P.O. Box 210172, Cincinnati, OH 45221-0172, USA. [email protected] Maria Dolores Luque de Castro Department of Analytical Chemistry Annex C-3, Campus of Rabanales, University of Cordoba, E-14071 Cordoba (Spain). [email protected]. [email protected] Fernand Claisse 2780 Bd de Monaco, Quebec QC, Canada G1P3H2. [email protected] Ray Clement Ministry of the Environment, 125 Resources Road, Etobicoke, Ontario, Canada M9P 3V6. [email protected] Alberto de Diego Kimika Analitikoa Saila; Euskal Herriko Unibertsitatea; 644 P. K.; 48080, Bilbao, Spain. [email protected]

vii


Sergi Dıéz Environmental Chemistry Department, IIQAB-CID-CSIC, Jordi Girona, 18, E-08034 Barcelona, Spain. Olivier F.X. Donard CNRS, Laboratoire de Chimie Analytique Bio-inorganique et Environnement, He`lioparc, 2 avenue du President Angot, F-64000 Pau, France. [email protected] Peter Drouin Spectroscopy Section, Laboratory Services Branch, Ontario Ministry of the Environment, Ontario, Canada M9P 3V6. [email protected] Les Ebdon School of Environmental Sciences, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK. [email protected] John Ezzell Dionex Corporation, 1515 West 2200 South, Suite A, Salt Lake City, UT 84119-7209, USA [email protected] Zhao-Lun Fang Research Center for Analytical Sciences, Northeastern University, Chemistry Building, Box 332, Shenyang 110006, P.R. China. [email protected] Jo¨rg Feldmann Department of Chemistry, University of Aberdeen, Meston Walk, Old Aberdeen AB24 3UE, Scotland, UK. [email protected] Andrew S. Fisher School of Environmental Sciences, University of Phymouth, Drake Circus, Phymouth PL4 8AA, UK. a.fi[email protected] Jose Luis Luque Garcıá Department of Analytical Chemistry Annex C-3, Campus of Rabanales, University of Cordoba, E- 4071 Cordoba (Spain). [email protected] Walter Goessler Institute of Chemistry, Analytical Chemistry, Universita¨tsplatz 1, 8010 Graz, Austria. [email protected] viii


Miguel de La Guardia Department of Analytical Chemistry, Faculty of Chemistry, University of Valencia, Dr Moliner St. 50. Burjassot, 46100-Valencia, Spain. [email protected] Monika Heisterkamp Mettler – Toledo GmbH, D-35396 Giessen, Germany. [email protected] Holger Hintelmann Department of Chemistry, Trent University, Peterborough, Ontario, K9J 7B8, Canada. [email protected] Jo´zsef Hlavay University of Veszpre´m, Department of Earth and Environmental Sciences, H-8200 Veszpre´m, Egyetem str. 10, P.O. Box 158 Hungary. [email protected] Michel Hoenig Centre for Veterinary and Agrochemical Research (CERVA), Leuvensesteenweg 17, B-3080 Tervuren, Belgium. [email protected] Milan Ihnat Pacific Agri-Food Research Centre, Agriculture and Agri-Food Canada Summerland, BC, Canada, V0H 1Z0. [email protected] Hideyuki Itabashi Department of Applied Chemistry, Faculty of Engineering, Gunma University, Kiryu, Gunma 376-8515, Japan. [email protected] ˚ ke Jo¨nsson Jan A Analytical Chemistry, Lund University, P.O. Box 124, S-221 00 Lund, Sweden. [email protected] Katsu Kawabata PerkinElmer Instruments, 71 Four Valley Drive, Concord, Ontario, Canada, L4K 4V8. [email protected] Edward E. King CEM Corporation, 3100 Smith Farm Road, P.O. Box 200, Matthews, North Carolina 28106-0200, USA. [email protected] Yoko Kishi PerkinElmer Instruments, 71 Four Valley Drive, Concord, Ontario, Canada, L4K 4V8. ix


Gunter Knapp Graz University of Technology, A-8010 Graz, Technikerstrasße 4, Graz, Austria. [email protected] Byron G. Kratochvil Department of Chemistry, University of Alberta, Edmonton, Alberta, Canada T6G 2G2. [email protected] Eva Krupp CNRS, Laboratoire de Chimie Analytique Bio-inorganique et Environnement, He`lioparc, 2 avenue du President Angot, F-64000 Pau, France. Doris Kuehnelt Institute of Chemistry, Analytical Chemistry, Universita¨tsplatz 1, 8010 Graz, Austria. [email protected] Claudia Ponce de Leon Department of Chemistry, University of Cincinnati, P.O. Box 210172, Cincinnati, OH 45221-0172, USA. Maurice Leroy European Commission, JRC-170, P.O. Box 2340, 76125 Karlsruhe, Germany Fuhe Li Air Liquide – Balazs Analytical Services, 46409 Landing Pky, Frement, CA 94538, USA. Ryszard Łobin´ski CNRS UMR 5034 Helioparc 2, av. Pr. Angot F-64053 PAU, France. [email protected] Yolanda Madrid Departamento de Quimica Analitica Facultad de Ciencias Quimicas, Universidad Complutense de Madrid 28040 Madrid, Spain. Lennart Mathiasson Analytical Chemistry, Lund University, P.O. Box 124, S-221 00 Lund, Sweden. [email protected] Henryk Matusiewicz Politechnika Poznan´ska, Department of Analytical Chemistry, 60-965, Poznan´, Poland. [email protected] Zoltań Mester NRC/INMS, 1500, Montreal Rd, Ottawa, ON, K1A 0R6, Canada. [email protected] x


Roberto Morabito ENEA, UTS PROT, SP Anguillarese 301, IT-00060 S. Maria di Galeria (Rome), Italy. [email protected] Angel Morales – Rubio Department of Analytical Chemistry, Faculty of Chemistry, University of Valencia, Dr Moliner St. 50 Burjassot, 46100 –Valencia, Spain. [email protected] Taketoshi Nakahara Department of Applied Chemistry, Graduate School of Engineering, Osaka Prefecture University, Sakai, Osaka 599-8531, Japan. [email protected] Marie-Pierre Pavageau CNRS, Laboratoire de Chimie Analytique Bio-inorganique et Environnement, He`lioparc, 2 avenue du President Angot, F-64000 Pau, France. Christophe Pećheyran CNRS, Laboratoire de Chimie Analytique Bio-inorganique et Environnement, He`lioparc, 2 avenue du President Angot, F-64000 Pau, France. Philip J. Potts Department of Earth Sciences, The Open University, Walton Hall, Milton Keynes MK7 6AA, UK. [email protected] Philippe Quevauviller European Commission, rue de la Loi 200, B-1049 Brussels, Belgium. [email protected] Gemma Rauret Departament de Quimica Analitica, Universitat de Barcelona, Martı´ i Franque`s 1-11, 3a Planta, 08028 Barcelona, Spain. [email protected] Philip Robinson School of Earth Sciences-Centre for Ore Deposit Research, University of Tasmania, Hobart, Tasmania 7001, Australia. [email protected] Ilia Rodushkin Analytica AB, Aurorum 10, SE-977 75 Lulea˚, Sweden. [email protected]

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Richard E. Russo Lawrence Berkeley National Lab, 1 Cyclotron Road, Berkeley, CA 94720, USA. [email protected] Angels Sahuquillo Departament de Quimica Analitica, Universitat de Barcelona, Martı´ i Franque`s 1-11, 3a Planta, 08028 Barcelona, Spain. [email protected] Peter Schramel GSF-Forschungszentrum Institut fu¨r Oekologische Chemie AG, Spurenelementanalytik und Metallspeziation, Postfach 1129 (P.O. Box 1129), D-85758 Neuherberg. [email protected] Ralph Sturgeon NRC/INMS, 1500, Montreal Rd, Ottawa, ON, K1A 0R6, Canada. [email protected] Joanna Szpunar CNRS UMR 5034 He`lioparc 2, av. Pr. Angot F-64053 PAU, France. Anne P. Vonderheide Department of Chemistry, University of Cincinnati, P.O. Box 210172, Cincinnati, OH 45221-0172, USA. Scott Willie NRC/INMS 1500 Montreal Road, Ottawa, ON, K1A 0R6, Canada. [email protected]

xii

WILSON AND WILSON’S

COMPREHENSIVE ANALYTICAL CHEMISTRY VOLUMES IN THE SERIES Vol. IA

Vol. IB Vol. IC Vol. IIA

Vol. IIB

Vol. IIC

Vol. IID Vol. III

Vol. IV

Vol. V

Vol. VI Vol. VII Vol. VIII

Vol. IX

Analytical Processes Gas Analysis Inorganic Qualitative Analysis Organic Qualitative Analysis Inorganic Gravimetric Analysis Inorganic Titrimetric Analysis Organic Quantitative Analysis Analytical Chemistry of the Elements Electrochemical Analysis Electrodeposition Potentiometric Titrations Conductometric Titrations High-Frequency Titrations Liquid Chromatography in Columns Gas Chromatography Ion Exchangers Distillation Paper and Thin Layer Chromatography Radiochemical Methods Nuclear Magnetic Resonance and Electron Spin Resonance Methods X-Ray Spectrometry Coulometric Analysis Elemental Analysis with Minute Sample Standards and Standardization Separation by Liquid Amalgams Vacuum Fusion Analysis of Gases in Metals Electroanalysis in Molten Salts Instrumentation for Spectroscopy Atomic Absorption and Fluorescence Spectroscopy Diffuse Reflectance Spectroscopy Emission Spectroscopy Analytical Microwave Spectroscopy Analytical Applications of Electron Microscopy Analytical Infrared Spectroscopy Thermal Methods in Analytical Chemistry Substoichiometric Analytical Methods Enzyme Electrodes in Analytical Chemistry Molecular Fluorescence Spectroscopy Photometric Titrations Analytical Applications of Interferometry Ultraviolet Photoelectron and Photoion Spectroscopy Auger Electron Spectroscopy Plasma Excitation in Spectrochemical Analysis

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Volumes in the series Vol. X Vol. XI

Vol. XII

Vol. XIII

Vol. XIV Vol. XV Vol. XVI Vol. XVII Vol. XVIII Vol. Vol. Vol. Vol. Vol.

XIX XX XXI XXII XXIII

Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol.

XXIV XXV XXVI XXVII XXVIII XXIX XXX XXXI XXXII XXXIII XXXIV

Vol. Vol. Vol. Vol.

XXXV XXXVI XXXVII XXXVIII

Vol. XXXIX Vol. XL

xiv

Organic Spot Tests Analysis The History of Analytical Chemistry The Application of Mathematical Statistics in Analytical Chemistry Mass Spectrometry Ion Selective Electrodes Thermal Analysis Part A. Simultaneous Thermoanalytical Examination by Means of the Derivatograph Part B. Biochemical and Clinical Applications of Thermometric and Thermal Analysis Part C. Emanation Thermal Analysis and other Radiometric Emanation Methods Part D. Thermophysical Properties of Solids Part E. Pulse Method of Measuring Thermophysical Parameters Analysis of Complex Hydrocarbons Part A. Separation Methods Part B. Group Analysis and Detailed Analysis Ion-Exchangers in Analytical Chemistry Methods of Organic Analysis Chemical Microscopy Thermomicroscopy of Organic Compounds Gas and Liquid Analysers Kinetic Methods in Chemical Analysis Application of Computers in Analytical Chemistry Analytical Visible and Ultraviolet Spectrometry Photometric Methods in Inorganic Trace Analysis New Developments in Conductometric and Oscillometric Analysis Titrimetric Analysis in Organic Solvents Analytical and Biomedical Applications of Ion-Selective Field-Effect Transistors Energy Dispersive X-Ray Fluorescence Analysis Preconcentration of Trace Elements Radionuclide X-Ray Fluorescence Analysis Voltammetry Analysis of Substances in the Gaseous Phase Chemiluminescence Immunoassay Spectrochemical Trace Analysis for Metals and Metalloids Surfactants in Analytical Chemistry Environmental Analytical Chemistry Elemental Speciation – New Approaches for Trace Element Analysis Discrete Sample Introduction Techniques for Inductively Coupled Plasma Mass Spectrometry Modern Fourier Transform Infrared Spectroscopy Chemical Test Methods of Analysis Sampling and Sample Preparation for Field and Laboratory Countercurrent Chromatography: The Support-Free Iiquid Stationary Phase Integrated Analytical Systems Analysis and Fate of Surfactants in the Aquatic Environment

Contents Contributors to Vol XLI Volumes in the Series . Series Editor’s Preface. Preface. . . . . . . . . Acronyms . . . . . . .

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Chapter 1. Sampling and sample preservation for trace element analysis . . . . . . . . . . . . . . . . . . . . . . Byron Kratochvil 1.1 Introduction. . . . . . . . . . . . . . . . . . . . . 1.2 Preliminary considerations . . . . . . . . . . . . . 1.2.1 Sampling variability . . . . . . . . . . . . . . . . . . . 1.2.2 Sampling strategies . . . . . . . . . . . . . 1.2.3 Uncertainties in sampling . . . . . . . . . 1.3 Types of samples . . . . . . . . . . . . . . . . . . 1.3.1 Judgment samples . . . . . . . . . . . . . 1.3.2 Random samples . . . . . . . . . . . . . . 1.3.3 Systematic samples . . . . . . . . . . . . . 1.3.4 Subsamples . . . . . . . . . . . . . . . . . 1.3.5 Composite samples . . . . . . . . . . . . . 1.4 Planning the sampling operation . . . . . . . . . . 1.4.1 Defining goals . . . . . . . . . . . . . . . . 1.4.2 Sampling plans . . . . . . . . . . . . . . . 1.5 Statistical sampling . . . . . . . . . . . . . . . . . 1.5.1 Introduction. . . . . . . . . . . . . . . . . 1.5.2 Minimum number of increments . . . . . . 1.5.3 Minimum size of increments in well-mixed particulate populations . . . . . . . . . . . 1.5.4 Sample increment size in segregated populations . . . . . . . . . . . . . . . . . 1.5.5 From where should increments be taken? . 1.5.6 Model-based sampling . . . . . . . . . . .

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Contents

1.5.7

Balancing economic factors and purpose of data collection against sample quality . . . . . . . . 1.6 Sample handling and preservation during collection, transport, and storage . . . . . . . . . . . . . . . . . 1.6.1 Handling and storage of samples . . . . . . . . 1.6.2 Sampling equipment . . . . . . . . . . . . . . 1.6.3 Sample containers . . . . . . . . . . . . . . . 1.7 Quality assurance in sampling [24,25] . . . . . . . . . 1.7.1 Overall objectives . . . . . . . . . . . . . . . . 1.7.2 Quality control . . . . . . . . . . . . . . . . . 1.7.3 Quality assessment . . . . . . . . . . . . . . . 1.8 Glossary. . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . Selected bibliography . . . . . . . . . . . . . . . . . . . . . Chapter 2. Sources of analyte contamination and loss during the analytical process . . . . . . . . . . . . . . . . . . . . . . . Gunter Knapp and Peter Schramel 2.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . 2.2 Contamination . . . . . . . . . . . . . . . . . . . . . 2.2.1 Materials . . . . . . . . . . . . . . . . . . . . 2.2.2 Reagents . . . . . . . . . . . . . . . . . . . . 2.2.3 Airborne particles . . . . . . . . . . . . . . . . 2.3 Losses . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Sampling . . . . . . . . . . . . . . . . . . . . . . . . 2.5 Storage . . . . . . . . . . . . . . . . . . . . . . . . . 2.6 Drying and homogenisation. . . . . . . . . . . . . . . 2.7 Dilution, dissolution and digestion . . . . . . . . . . . 2.8 Separation and preconcentration . . . . . . . . . . . . 2.9 Element measurement . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 3. Calibration approaches for trace element determination Douglas C. Baxter and Ilia Rodushkin 3.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . 3.2 Basic assumptions and some terminology . . . . . . . 3.3 Selection of the calibration approach . . . . . . . . . . 3.4 Statistical evaluation of recovery data . . . . . . . . .

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3.5

Linear 3.5.1 3.5.2 3.5.3

regression . . . . . . . . . . . . . . . . . . . . Ordinary linear regression . . . . . . . . . . . Weighted linear regression . . . . . . . . . . . Linear regression for data with uncertainties in both variables . . . . . . . . . . . . . . . . . . 3.6 External calibration. . . . . . . . . . . . . . . . . . . 3.6.1 Estimating uncertainty . . . . . . . . . . . . . 3.6.2 Optimizing precision . . . . . . . . . . . . . . 3.6.3 Accounting for non-constant sensitivity . . . . 3.7 Method of standard additions . . . . . . . . . . . . . . 3.7.1 Estimating uncertainty . . . . . . . . . . . . . 3.7.2 Optimizing precision . . . . . . . . . . . . . . 3.7.3 Accounting for non-constant sensitivity . . . . 3.8 Internal standardization . . . . . . . . . . . . . . . . 3.8.1 Estimating uncertainty . . . . . . . . . . . . . 3.8.2 Optimizing precision . . . . . . . . . . . . . . 3.9 Isotope dilution . . . . . . . . . . . . . . . . . . . . . 3.9.1 Mass discrimination and detector dead time . . 3.9.2 Estimating uncertainty . . . . . . . . . . . . . 3.9.3 Optimizing precision . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . .

55 56 59 64 67 68 70 72 74 75 76 78 79 80 81 81 85 86 88 90 90

Chapter 4. Stated references for ensuring traceability of trace element analysis . . . . . . . . . . . . . . . . . . . . . . . . 93 Philippe Quevauviller 4.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . 93 4.2 Meaning of traceability for chemical measurements . . 94 4.3 SI units . . . . . . . . . . . . . . . . . . . . . . . . . 96 4.4 Documented standards . . . . . . . . . . . . . . . . . 96 4.5 Reference methods . . . . . . . . . . . . . . . . . . . 97 4.6 Reference materials . . . . . . . . . . . . . . . . . . . 99 4.6.1 The various categories of materials and related requirements . . . . . . . . . . . . . . . . . . 99 4.6.2 Production . . . . . . . . . . . . . . . . . . . 100 4.6.3 Methods used for material characterisation or certification . . . . . . . . . . . . . . . . . . . 102

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Contents

4.6.4 Use of reference materials . . . . . . . . . . 4.6.5 Traceability of reference materials . . . . . . 4.7 Specimen banking . . . . . . . . . . . . . . . . . . 4.8 Proficiency testing . . . . . . . . . . . . . . . . . . 4.9 Real-case achievement of traceability of trace element analysis . . . . . . . . . . . . . . . . . . . 4.9.1 Total trace element determinations . . . . . . 4.9.2 Operationally defined trace element determinations . . . . . . . . . . . . . . . . 4.9.3 Determinations of chemical forms of elements 4.10 Conclusions . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . .

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Chapter 5. Detection methods for the quantitation of trace elements . . . . . . . . . . . . . . . . . . . . . . . . . . . . Les Ebdon, Andrew S. Fisher, Maria Betti and Maurice Leroy 5.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . 5.2 Classical methods . . . . . . . . . . . . . . . . . . . . 5.3 Flame spectrometry . . . . . . . . . . . . . . . . . . . 5.3.1 Introduction. . . . . . . . . . . . . . . . . . . 5.3.2 Theory . . . . . . . . . . . . . . . . . . . . . 5.3.3 Instrumentation . . . . . . . . . . . . . . . . 5.3.4 Interferences and background correction techniques. . . . . . . . . . . . . . . . . . . . 5.3.5 Conventional nebulisation . . . . . . . . . . . 5.3.6 Alternative methods of sample introduction . . 5.4 Electrothermal AAS. . . . . . . . . . . . . . . . . . . 5.4.1 Introduction. . . . . . . . . . . . . . . . . . . 5.4.2 Conventional ET-AAS . . . . . . . . . . . . . . 5.4.3 Multi-element ET-AAS . . . . . . . . . . . . . 5.4.4 Chemical vapour generation – ET-AAS . . . . . 5.4.5 Speciation . . . . . . . . . . . . . . . . . . . . 5.5 Inductively coupled plasma-atomic emission spectrometry . . . . . . . . . . . . . . . . . . . . . . 5.5.1 Introduction. . . . . . . . . . . . . . . . . . . 5.5.2 Theory and interferences . . . . . . . . . . . .

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5.5.3 Instrumentation . . . . . . . . . . . . . . . . 5.5.4 Figures of merit . . . . . . . . . . . . . . . . . 5.6 Inductively coupled plasma-mass spectrometry . . . . 5.6.1 Introduction. . . . . . . . . . . . . . . . . . . 5.6.2 Theory . . . . . . . . . . . . . . . . . . . . . 5.6.3 Instrumentation . . . . . . . . . . . . . . . . 5.6.4 Different types of analysis . . . . . . . . . . . 5.6.5 Interferences . . . . . . . . . . . . . . . . . . 5.6.6 Sample introduction techniques . . . . . . . . 5.6.7 Figures of merit . . . . . . . . . . . . . . . . . 5.7 Atomic fluorescence spectrometry. . . . . . . . . . . . 5.7.1 Introduction. . . . . . . . . . . . . . . . . . . 5.7.2 Theory . . . . . . . . . . . . . . . . . . . . . 5.7.3 Instrumentation . . . . . . . . . . . . . . . . 5.7.4 Sample introduction . . . . . . . . . . . . . . 5.7.5 Interferences . . . . . . . . . . . . . . . . . . 5.7.6 Figures of merit . . . . . . . . . . . . . . . . . 5.8 Other atomic absorption, emission and fluorescence methods of detection . . . . . . . . . . . . . . . . . . 5.8.1 Microwave induced plasma . . . . . . . . . . . 5.8.2 Direct current plasma. . . . . . . . . . . . . . 5.9 Secondary ion mass spectrometry. . . . . . . . . . . . 5.9.1 Introduction. . . . . . . . . . . . . . . . . . . 5.9.2 Practical principles . . . . . . . . . . . . . . . 5.9.3 Sensitivity and quantification. . . . . . . . . . 5.10 Glow discharge mass spectrometry . . . . . . . . . . . 5.10.1 Introduction. . . . . . . . . . . . . . . . . . . 5.10.2 Glow discharge processes . . . . . . . . . . . . 5.10.3 Applications to trace element analysis . . . . . 5.11 X-ray fluorescence spectrometry . . . . . . . . . . . . 5.11.1 Introduction. . . . . . . . . . . . . . . . . . . 5.11.2 Instrumentation . . . . . . . . . . . . . . . . 5.11.3 Matrix effects . . . . . . . . . . . . . . . . . . 5.11.4 Quantitative and trace analysis . . . . . . . . 5.12 UV/Visible spectrophotometric and chemiluminescence techniques. . . . . . . . . . . . . . . . . . . . . . . . 5.12.1 UV/Visible spectrophotometric techniques . . .

143 152 152 152 153 154 156 156 158 160 160 160 162 162 163 163 163 164 164 165 165 165 167 168 171 171 173 175 176 176 177 177 178 179 179

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5.12.2 Molecular fluorescence and chemiluminescence detection . . . . . . . . . . 5.13 Electrochemical methods . . . . . . . . . . . . . . . . 5.13.1 Differential pulse anodic stripping voltammetry 5.13.2 Cathodic and adsorptive stripping voltammetry 5.13.3 Ion selective electrodes . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . .

181 183 183 184 185 186

DIGESTION AND EXTRACTION APPROACHES

Chapter 6. Wet digestion methods . . . . . . . . . . . . . . . . . Henryk Matusiewicz 6.1 Introduction and brief history . . . . . . . . . . . . . 6.2 Nomenclature . . . . . . . . . . . . . . . . . . . . . . 6.3 Bibliography . . . . . . . . . . . . . . . . . . . . . . 6.4 Reagents and vessel materials for wet digestion procedures . . . . . . . . . . . . . . . . . . . . . . . 6.5 Wet acid digestion (decomposition and dissolution) procedures . . . . . . . . . . . . . . . . . . . . . . . 6.5.1 Open systems . . . . . . . . . . . . . . . . . . 6.5.2 Closed systems . . . . . . . . . . . . . . . . . 6.5.3 Flow systems . . . . . . . . . . . . . . . . . . 6.5.4 Vapor-phase acid digestion (gas-phase reactions) 6.5.5 Efficiency of wet digestion (decomposition and dissolution) procedures . . . . . . . . . . . . . 6.5.6 Comparison of wet digestion techniques . . . . 6.5.7 Digestion systems (instrumentation, equipment, automation) . . . . . . . . . . . . 6.5.8 Safety of acid digestions (sample acid digestion safety). . . . . . . . . . . . . . . . . 6.6 Conclusions and future trends . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 7. Dry ashing . . . . . . . . . . . . . . . Michel Hoenig 7.1 General considerations . . . . . . . . . 7.2 Why dry ashing? . . . . . . . . . . . . 7.3 Oxidation process and dissolution of the

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7.3.1 Particular case of plant matrices . . . . . . . Methodology . . . . . . . . . . . . . . . . . . . . . 7.4.1 Heating devices . . . . . . . . . . . . . . . . 7.4.2 Ashing vessels . . . . . . . . . . . . . . . . 7.4.3 Influence of the sample composition . . . . . 7.4.4 Operating modes for environmental samples . 7.5 Particular cases of arsenic and selenium . . . . . . . 7.5.1 Ashing aids . . . . . . . . . . . . . . . . . . 7.5.2 What to do? . . . . . . . . . . . . . . . . . . 7.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4

Chapter 8. Microwave based extraction . . . . . . . . . Edward E. King and David Barclay 8.1 Introduction. . . . . . . . . . . . . . . . . . 8.2 Brief history of industrial microwave devices. 8.3 Microwave theory . . . . . . . . . . . . . . . 8.4 Microwave laboratory equipment . . . . . . . 8.4.1 Magnetron . . . . . . . . . . . . . . 8.4.2 Power application . . . . . . . . . . . 8.4.3 Waveguide . . . . . . . . . . . . . . 8.4.4 Microwave cavity . . . . . . . . . . . 8.4.5 Reflected energy . . . . . . . . . . . 8.4.6 Mode stirrer and turntables . . . . . 8.4.7 Microwave compatible materials . . . 8.5 Vessels . . . . . . . . . . . . . . . . . . . . 8.5.1 Materials . . . . . . . . . . . . . . . 8.5.2 Structural components . . . . . . . . 8.5.3 Safety . . . . . . . . . . . . . . . . . 8.5.4 Closed vessels . . . . . . . . . . . . . 8.5.5 Vent and reseal vessels . . . . . . . . 8.5.6 Open vessels . . . . . . . . . . . . . 8.6 Control systems . . . . . . . . . . . . . . . . 8.6.1 Power/time . . . . . . . . . . . . . . 8.6.2 Pressure. . . . . . . . . . . . . . . . 8.6.3 Temperature . . . . . . . . . . . . .

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243 244 244 245 246 246 248 250 251 253 254

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8.6.4 Power optimization feedback . . . . . Methodology . . . . . . . . . . . . . . . . . 8.7.1 Pressurized closed vessel extractions . 8.7.2 Atmospheric open vessel extractions . 8.8 Sample types . . . . . . . . . . . . . . . . . 8.8.1 Inorganic . . . . . . . . . . . . . . . 8.8.2 Leaches and other partial extractions 8.8.3 Complete dissolutions . . . . . . . . . 8.8.4 High-temperature extractions . . . . 8.8.5 Complex sequential extractions . . . . 8.8.6 Organic . . . . . . . . . . . . . . . . 8.8.7 Carbohydrates . . . . . . . . . . . . 8.8.8 Proteins . . . . . . . . . . . . . . . . 8.8.9 Fats, oils, and waxes . . . . . . . . . 8.9 Advanced applications . . . . . . . . . . . . 8.9.1 Clean chemistry. . . . . . . . . . . . 8.9.2 Concentration/evaporation . . . . . . 8.10 Conclusions . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . 8.7

Chapter 9. Fusion and fluxes . . . . . . . . . . . . . Fernand Claisse 9.1 Introduction. . . . . . . . . . . . . . . . . 9.2 Fusion in lithium borates . . . . . . . . . . 9.2.1 General . . . . . . . . . . . . . . . 9.3 The key to successful fusion beads . . . . . 9.3.1 The concept of “neutrality” . . . . . 9.3.2 The optimal flux and crystallization 9.3.3 Cracking of fused beads . . . . . . . 9.3.4 Loss and retention of sulfur. . . . . 9.4 Application to trace element analysis . . . . 9.4.1 Maximizing X-ray intensities . . . . 9.4.2 Minimizing background . . . . . . . 9.5 Features of fusion for trace elements . . . . References . . . . . . . . . . . . . . . . . . . . .

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Chapter 10. Supercritical fluid extraction . . . . . . . . . Roberto Alzaga, Sergi Dıéz and Josep M. Bayona 10.1 Properties of supercritical fluids . . . . . . . . 10.2 Instrumentation . . . . . . . . . . . . . . . . 10.2.1 Experimental solubility measurements . 10.3 SFE of trace elements. . . . . . . . . . . . . . 10.3.1 Ligand solubility in SFs. . . . . . . . . 10.3.2 Complex –SF solubility . . . . . . . . . 10.3.3 SFE process . . . . . . . . . . . . . . . 10.4 Organometallic compounds . . . . . . . . . . . 10.4.1 Organotin compounds. . . . . . . . . . 10.4.2 Organomercury compounds . . . . . . . 10.4.3 Organolead compounds . . . . . . . . . 10.4.4 Arsenic compounds . . . . . . . . . . . 10.5 Conclusion . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . .

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Chapter 11. Accelerated solvent extraction of organometallic and inorganic compounds . . . . . . . . . . . . . . . . John L. Ezzell 11.1 Accelerated solvent extraction as a sample preparation technique . . . . . . . . . . . . . . 11.1.1 Introduction. . . . . . . . . . . . . . . . 11.1.2 Basic principles of ASE operation. . . . . 11.1.3 ASE instrumentation . . . . . . . . . . . 11.1.4 ASE methods development . . . . . . . . 11.1.5 Application areas . . . . . . . . . . . . . 11.1.6 Summary . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . Chapter 12. Sonication as a sample preparation method elemental analysis . . . . . . . . . . . . . . . . . . Kevin Ashley 12.1 Introduction. . . . . . . . . . . . . . . . . . 12.2 Methodological considerations . . . . . . . . 12.3 Historical background . . . . . . . . . . . .

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12.4 Applications—sonication and sample preparation 12.4.1 Environmental analysis. . . . . . . . . . 12.4.2 Industrial hygiene . . . . . . . . . . . . 12.4.3 Biological tissues and fluids. . . . . . . . 12.4.4 Other applications . . . . . . . . . . . . 12.5 Summary . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . .

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Chapter 13. Solid phase microextraction as a tool for trace element determination . . . . . . . . . . . . . . . . . . . . Zoltań Mester and Ralph Sturgeon 13.1 Introduction. . . . . . . . . . . . . . . . . . . . . . 13.2 General description of solid phase microextraction . . 13.2.1 Extraction modes . . . . . . . . . . . . . . . 13.2.2 Coatings. . . . . . . . . . . . . . . . . . . . 13.3 Solid phase microextraction: step-by-step method development. . . . . . . . . . . . . . . . . . . . . . 13.3.1 Extraction mode selection. . . . . . . . . . . 13.3.2 Fiber coating selection . . . . . . . . . . . . 13.3.3 Derivatization method selection . . . . . . . 13.3.4 Optimization of desorption conditions . . . . 13.3.5 Sample volume optimization . . . . . . . . . 13.3.6 Optimization of the extraction time. . . . . . 13.3.7 Optimization of extraction conditions. . . . . 13.3.8 Determination of the linear dynamic range. . 13.3.9 Selection of the calibration method . . . . . . 13.3.10 Precision of the method . . . . . . . . . . . . 13.3.11 Automation of the method . . . . . . . . . . 13.4 Solid phase microextraction for speciation analysis . 13.4.1 Volatile metal species—gas chromatographic determination . . . . . . . . . . . . . . . . . 13.5 Solid phase microextraction as an investigative tool . 13.6 Limitations of solid phase microextraction . . . . . . 13.7 Isotope dilution calibration in combination with solid phase microextraction. . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . .

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Chapter 14. Solid-phase extraction . . . . . . . . . . . . . Vale´rie Camel 14.1 Introduction. . . . . . . . . . . . . . . . . . . . 14.2 Theory . . . . . . . . . . . . . . . . . . . . . . 14.2.1 Presentation of the technique . . . . . . . 14.2.2 Operation . . . . . . . . . . . . . . . . . 14.2.3 Advantages of the technique . . . . . . . 14.3 Step-by-step method development guide . . . . . 14.3.1 Selection of solid sorbent . . . . . . . . . 14.3.2 Influential parameters . . . . . . . . . . 14.4 Applications of SPE to the determination of some trace elements . . . . . . . . . . . . . . . . . . 14.4.1 Chromium. . . . . . . . . . . . . . . . . 14.4.2 Iron . . . . . . . . . . . . . . . . . . . . 14.4.3 Mercury . . . . . . . . . . . . . . . . . . 14.4.4 Selenium . . . . . . . . . . . . . . . . . 14.4.5 Tin . . . . . . . . . . . . . . . . . . . . 14.5 Conclusion . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . .

Chapter 15. Chelation solvent extraction for separation of metal ions . . . . . . . . . . . . . . . . . . . . . . . . Hideyuki Itabashi and Taketoshi Nakahara 15.1 Introduction. . . . . . . . . . . . . . . . . . . . 15.2 Theoretical considerations . . . . . . . . . . . . 15.2.1 General principles . . . . . . . . . . . . 15.2.2 Preconcentration of metal ions . . . . . . 15.2.3 Mutual separation of metal ions . . . . . 15.2.4 Speciation of metal ions in natural water. 15.3 Adsorption of metal ions using chelating resins . 15.3.1 General principles . . . . . . . . . . . . 15.3.2 Features of some chelating resins . . . . 15.4 Application of chelation to sample preparation for trace metal analysis. . . . . . . . . . . . . . . . 15.4.1 Procedure for the extraction of metal ions from natural waters. . . . . . . . . . . .

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459 460 460 465 467 472 474 475 475

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15.4.2 Procedure for the extraction of metal ions from high-purity materials and inorganic solid samples . . . . . . . . . . . . . . . . . . 15.4.3 Procedure for the extraction of metal ions from biological samples . . . . . . . . . . . . . 15.4.4 Procedure for the speciation of metal ions in natural waters . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . .

488 490 491 492

Chapter 16. Cryogenic trapping for speciation analysis . . . . . Marie-Pierre Pavageau, Eva Krupp, Alberto de Diego, Christophe Pećheyran and Olivier F.X. Donard 16.1 Introduction. . . . . . . . . . . . . . . . . . . . . . 16.2 Definition of volatile species . . . . . . . . . . . . . 16.3 Physico-chemical principles and processes associated with cryofocusing . . . . . . . . . . . . . . . . . . . 16.4 Analytical constraints. . . . . . . . . . . . . . . . . 16.4.1 Removal of CO2 . . . . . . . . . . . . . . . . 16.4.2 Water removal . . . . . . . . . . . . . . . . 16.5 Sample preservation and stability . . . . . . . . . . 16.6 Instrumentation for cryogenic trapping and selected applications. . . . . . . . . . . . . . . . . . . . . . 16.6.1 Cryosampler for determination of industrial and environmental VMCs . . . . . . . . . . 16.6.2 Cryogenic trapping for speciation analysis . . References . . . . . . . . . . . . . . . . . . . . . . . . . .

. 520 . 523 . 529

Chapter 17. Biotrapping as an alternative to metal preconcentration and speciation . . . . . . . . . . . . Yolanda Madrid and Carmen Ca´mara 17.1 Introduction. . . . . . . . . . . . . . . . . . . 17.2 General characteristics of biological substrates. 17.2.1 Algae . . . . . . . . . . . . . . . . . . 17.2.2 Bacteria . . . . . . . . . . . . . . . . . 17.2.3 Fungi . . . . . . . . . . . . . . . . . .

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17.3 Uptake mechanisms . . . . . . . 17.4 Working procedures . . . . . . . . 17.4.1 Immobilisation . . . . . . 17.5 Applications . . . . . . . . . . . . 17.5.1 Analytical applications . . 17.5.2 Technological applications 17.6 Conclusions . . . . . . . . . . . . References . . . . . . . . . . . . . . . .

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Chapter18 Membrane extraction . . . . . . . . . . . . . . . . . . . . ˚ ke Jo¨nsson and Lennart Mathiasson Jan A 18.1 Introduction. . . . . . . . . . . . . . . . . . . . . . 18.2 Membrane extraction techniques . . . . . . . . . . . 18.2.1 Supported liquid membrane extraction (SLM) 18.2.2 Microporous membrane liquid liquid extraction (MMLLE) . . . . . . . . . . . . . 18.3 Chemical principles for metal extraction . . . . . . . 18.4 Properties of membrane extraction . . . . . . . . . . 18.4.1 Clean-up and selectivity . . . . . . . . . . . 18.4.2 Enrichment . . . . . . . . . . . . . . . . . . 18.4.3 Automation and unattended operation . . . . 18.4.4 Solvent consumption . . . . . . . . . . . . . 18.5 Experimental set-up . . . . . . . . . . . . . . . . . 18.5.1 Flow systems for membrane extraction . . . . 18.5.2 How to set up a membrane extraction experiment for metal ions. . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 19. Derivatization and vapor generation trace element analysis and speciation . . . . Yong Cai 19.1 Introduction. . . . . . . . . . . . . . 19.2 Theory . . . . . . . . . . . . . . . . 19.2.1 Grignard reactions . . . . . . 19.2.2 Hydride generation . . . . . .

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19.2.3 Aqueous derivatization (aryl)borates . . . . . 19.3 Method development . . . . . 19.3.1 Grignard reactions . . 19.3.2 Aqueous derivatization 19.4 Applications . . . . . . . . . . Acknowledgements . . . . . . . . . References . . . . . . . . . . . . . .

with tetraalkyl . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Chapter 20. Laser ablation sampling . . . . Richard E. Russo and David P. Baldwin 20.1 Introduction. . . . . . . . . . . . 20.2 Experimental system . . . . . . . 20.3 Ablation detection systems . . . . 20.4 Calibration . . . . . . . . . . . . 20.5 Fractionation . . . . . . . . . . . 20.6 Conclusion . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . References . . . . . . . . . . . . . . . .

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580 583 583 584 585 590 590

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593 594 599 601 603 604 606 606

Chapter 21. Flow injection techniques for sample pretreatment . . Zhao-Lun Fang 21.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . 21.1.1 General . . . . . . . . . . . . . . . . . . . . . 21.1.2 General features of flow injection on-line sample pretreatment systems. . . . . . . . . . 21.1.3 Classification of FI sample pretreatment systems . . . . . . . . . . . . . . . . . . . . . 21.1.4 Principles and general guidelines for the development of FI systems . . . . . . . . . . . 21.1.5 Practical hints for manipulation of FI equipment 21.2 FI liquid – liquid extraction systems. . . . . . . . . . . 21.2.1 Introduction. . . . . . . . . . . . . . . . . . . 21.2.2 Apparatus for FI liquid – liquid extraction . . . 21.2.3 Guidelines for the development of FI liquid – liquid extraction systems . . . . . . . . 21.2.4 Typical manifolds for FI liquid –liquid extraction 21.3 FI solid phase extraction systems. . . . . . . . . . . .

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611 611 612 612 613 614 615 615 617 623 624 626

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21.3.1 Introduction. . . . . . . . . . . . . . . . . . . 626 21.3.2 Sorption media for FI solid phase extraction . . 627 21.3.3 Guidelines for the development of FI solid phase extraction systems . . . . . . . . . . . . 629 21.3.4 Typical manifolds for FI solid phase extraction . 630 21.4 FI vapor generation systems . . . . . . . . . . . . . . 635 21.4.1 Introduction. . . . . . . . . . . . . . . . . . . 635 21.4.2 Gas – liquid separators for FI vapor generation . 635 21.4.3 Guidelines for development of FI vapor generation systems . . . . . . . . . . . . . . . 636 21.4.4 Typical FI manifolds for VG-AAS . . . . . . . . 638 21.5 FI gas diffusion systems . . . . . . . . . . . . . . . . 641 21.5.1 General . . . . . . . . . . . . . . . . . . . . . 641 21.5.2 Gas-diffusion separators . . . . . . . . . . . . 641 21.5.3 Typical FI manifolds for gas-diffusion separation and preconcentration . . . . . . . . . . . . . . 642 21.6 FI on-line sample digestion . . . . . . . . . . . . . . . 643 21.6.1 Introduction. . . . . . . . . . . . . . . . . . . 643 21.6.2 FI on-line sample digestion systems for AAS . . 644 21.6.3 FI digestion systems coupled to VG-AAS . . . . 644 21.6.4 FI systems for digestion of solid samples in AAS 645 21.6.5 FI pretreatment systems with on-line photo-oxidation by UV irradiation . . . . . . . 646 References . . . . . . . . . . . . . . . . . . . . . . . . . . . 646

Chapter 22. Automation of sample preparation . . . . . . . . . . Maria Dolores Luque de Castro and Jose Luis Luque Garcıá 22.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . 22.1.1 Generalities . . . . . . . . . . . . . . . . . . . 22.1.2 Principal shortcomings in automating sample preparation . . . . . . . . . . . . . . . . . . . 22.1.3 Batch versus serial approaches to automated sample preparation . . . . . . . . . . . . . . . 22.1.4 Bar codes: a necessary tool in automating routine analyses . . . . . . . . . . . . . . . . 22.2 Automation of liquid sample preparation . . . . . . . .

649 649 649 650 651 652 653

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Contents

22.2.1 Continuous systems. . . . . . . . . . . . . . . 22.2.2 Discontinuous approaches . . . . . . . . . . . 22.3 Automation of solid sample preparation . . . . . . . . 22.3.1 One-step approaches to automation and acceleration of solid sample preparation . . . . 22.3.2 Direct solid sampling . . . . . . . . . . . . . . 22.4 Robotics . . . . . . . . . . . . . . . . . . . . . . . . . 22.4.1 Workstations, robots, modules and peripherals . 22.4.2 The role of robots in the analytical process . . . 22.4.3 Analytical scope of robotics for sample preparation . . . . . . . . . . . . . . . . . . . 22.5 Advantages and disadvantages of automation of sample preparation . . . . . . . . . . . . . . . . . . 22.6 Future prospects . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . .

653 656 659 659 660 660 662 670 674 676 677 678

MATRICES

Chapter 23. Sample preparation for crude oil, petroleum products and polymers . . . . . . . . . . . . . . . . . . . . . . . . . Robert I. Botto 23.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . 23.1.1 Nature of petroleum crude, products and polymers . . . . . . . . . . . . . . . . . . . . 23.1.2 Element context and species in petroleum crude, products and polymers. . . . . . . . . . 23.1.3 Sample preparation challenges for trace element analysis . . . . . . . . . . . . . . . . 23.2 Sample preparation techniques and instrumentation . 23.2.1 Ashing techniques . . . . . . . . . . . . . . . 23.2.2 Acid mineralization techniques . . . . . . . . . 23.2.3 Oxygen combustion . . . . . . . . . . . . . . . 23.2.4 Sample component separations/extractive sample preparation . . . . . . . . . . . . . . . 23.2.5 Organic sample dilutions and dissolutions . . . 23.2.6 Stable emulsions . . . . . . . . . . . . . . . . 23.2.7 Scrubber sampling for C1 – C4 hydrocarbons and gases . . . . . . . . . . . . . . . . . . . .

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683 683 683 686 691 693 693 697 704 706 707 709 711

Contents

23.3 Cleanliness and quality assurance . . . 23.3.1 Equipment cleaning. . . . . . . 23.3.2 Clean techniques and disposable 23.3.3 Quality assurance. . . . . . . . Acknowledgements . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . equipment . . . . . . . . . . . . . . . . . .

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713 713 714 715 718 719

Chapter 24. Sample preparation of geological samples, soils and sediments . . . . . . . . . . . . . . . . . . . . . . . . . 723 Philip J. Potts and Philip Robinson 24.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . 723 24.2 Sample preparation . . . . . . . . . . . . . . . . . . . 723 24.2.1 Sample collection . . . . . . . . . . . . . . . . 724 24.2.2 Crushing and grinding . . . . . . . . . . . . . 726 24.2.3 Selecting an appropriate test portion . . . . . . 727 24.3 Choice of approach . . . . . . . . . . . . . . . . . . . 728 24.3.1 Fitness-for-purpose . . . . . . . . . . . . . . . 728 24.3.2 Choice of sample preparation procedure based on choice of technique. . . . . . . . . . . . . . 728 24.3.3 Choice of sample preparation based on the characteristics of elements . . . . . . . . . . . 729 24.4 Methods that do not require any sample digestion— in situ methods of analysis . . . . . . . . . . . . . . . 730 24.4.1 Portable X-ray fluorescence . . . . . . . . . . . 730 24.4.2 Gamma spectrometry . . . . . . . . . . . . . . 732 24.4.3 Laser ablation techniques and other microprobe/ microanalytical techniques . . . . . . . . . . . 732 24.5 Methods based on solid samples . . . . . . . . . . . . 733 24.5.1 Direct determinations on powders . . . . . . . 733 24.5.2 Powder pellet for XRF . . . . . . . . . . . . . 734 24.5.3 Glass disks for XRF trace determinations . . . 734 24.6 Dissolution methods based on acid attack . . . . . . . 735 24.6.1 Properties of acids used in the decomposition of geological materials . . . . . . . . . . . . . . . 736 24.6.2 Open vessel and low-pressure acid digestion . . 738

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24.6.3 HF – HNO3 decomposition method in Savillexw screw top vials . . . . . . . . . . . . . . . . . 740 24.6.4 Closed vessel high pressure acid digestion . . . 740 24.6.5 HF/H2SO4 decomposition method in closed, high pressure vessels . . . . . . . . . . . . . . 742 24.6.6 HF/HClO4 – HCl decomposition method in closed, high pressure vessels . . . . . . . . . . . . . . 742 24.6.7 Microwave oven digestion. . . . . . . . . . . . 743 24.6.8 Partial acid attack . . . . . . . . . . . . . . . 744 24.6.9 Difficult minerals . . . . . . . . . . . . . . . . 745 24.7 Decomposition by molten salt fusion . . . . . . . . . . 750 24.7.1 Total fusion . . . . . . . . . . . . . . . . . . . 750 24.7.2 A LiBO2 fusion procedure . . . . . . . . . . . 752 24.7.3 Sintering . . . . . . . . . . . . . . . . . . . . 752 24.7.4 Fire assay . . . . . . . . . . . . . . . . . . . . 752 24.8 Pre-concentration and separation procedures . . . . . 753 24.8.1 Ion exchange . . . . . . . . . . . . . . . . . . 753 24.8.2 Solvent extraction and co-precipitation . . . . . 754 24.8.3 Vapour generation . . . . . . . . . . . . . . . 755 24.9 Sequential extractions and dissolutions . . . . . . . . 755 24.9.1 Procedure of Tessier et al. . . . . . . . . . . . 756 24.9.2 The “BCR” method . . . . . . . . . . . . . . . 757 24.9.3 Selective extractions for geochemical exploration . . . . . . . . . . . . . . . . . . . 757 24.10 Summary and conclusions . . . . . . . . . . . . . . . 758 References . . . . . . . . . . . . . . . . . . . . . . . . . . . 758 Chapter 25. Sample preparation for food analysis. . . . . . . . Milan Ihnat 25.1 Introduction. . . . . . . . . . . . . . . . . . . . . . 25.2 Literature . . . . . . . . . . . . . . . . . . . . . . . 25.2.1 Books on sample treatment, decomposition . . 25.2.2 Books including chapters, sections on or discussing sample treatment, decomposition . 25.2.3 Reviews on sample treatment, decomposition 25.2.4 Other reviews including coverage of sample treatment, decomposition . . . . . . . . . .

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. 765 . 765 . 766 . 767 . 767 . 767 . 768

Contents

25.2.5 Papers, publications, containing (major) writing on sample treatment . . . . . . . . . . . . . . 25.3 Pretreatment . . . . . . . . . . . . . . . . . . . . . . 25.4 Classification of sample treatment methods . . . . . . 25.5 Compilation of sample treatment methods for foods . . 25.5.1 No treatment . . . . . . . . . . . . . . . . . . 25.5.2 Dry ashing . . . . . . . . . . . . . . . . . . . 25.5.3 Wet digestion—conventional . . . . . . . . . . 25.5.4 Wet digestion—microwave-assisted . . . . . . . 25.5.5 Slurry sample preparation . . . . . . . . . . . 25.6 Specific cases: methods, elements, matrices . . . . . . 25.6.1 Analytical method. . . . . . . . . . . . . . . . 25.6.2 Elements . . . . . . . . . . . . . . . . . . . . 25.6.3 Matrix and constituents . . . . . . . . . . . . 25.7 Examples of specific, recommended sample treatment procedures . . . . . . . . . . . . . . . . . . . . . . . 25.7.1 Conventional wet digestion with nitric and perchloric acids . . . . . . . . . . . . . . . . . 25.7.2 Dry ashing with or without ashing aid . . . . . 25.7.3 Microwave-assisted wet digestion. . . . . . . . 25.8 Closing remarks. . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . .

Chapter 26. The determination of trace elements in water . . Scott N. Willie 26.1 Direct methods of determination . . . . . . . . . . 26.2 Preconcentration techniques—multielement . . . . 26.3 Preconcentration—individual elements . . . . . . . 26.4 Determination of trace elements as volatile species 26.5 Mercury . . . . . . . . . . . . . . . . . . . . . . . 26.6 Luminescence . . . . . . . . . . . . . . . . . . . . 26.7 Voltammetry . . . . . . . . . . . . . . . . . . . . 26.8 Total-reflection X-Ray fluorescence spectrometry . . 26.9 Conclusions . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . .

768 768 770 770 833 834 836 838 838 839 839 841 842 843 843 845 845 846 847

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857 860 865 865 879 880 885 891 894 894

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Contents

Chapter 27. Aerosol sampling and sample preparation for elemental analysis . . . . . . . . . . . . . . . . . . . . . Jo´zsef Hlavay 27.1 Introduction. . . . . . . . . . . . . . . . . . . . . 27.1.1 Objectives of monitoring . . . . . . . . . . 27.2 Sampling of aerosols . . . . . . . . . . . . . . . . 27.2.1 General considerations . . . . . . . . . . . 27.2.2 Sampling of aerosol by impactors . . . . . . 27.2.3 Ambient sampling for the respirable fraction . . . . . . . . . . . . . . . . . . . 27.2.4 High-volume aerosol samplers . . . . . . . 27.2.5 Speciation aerosol sampling system . . . . 27.2.6 Passive samplers . . . . . . . . . . . . . . 27.3 Sequential extraction schemes for aerosol samples . 27.4 Discussion. . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . General terms used in sampling. . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 28. Sample preparation for industrial waste analysis Peter Drouin and Ray E. Clement 28.1 Types of industrial waste . . . . . . . . . . . . . . 28.2 Safety considerations for industrial waste analysis. 28.3 Sample characteristics and industrial waste sampling . . . . . . . . . . . . . . . . . . . . . . 28.4 Digestions . . . . . . . . . . . . . . . . . . . . . . 28.4.1 Aqueous sample types—US EPA methods . 28.4.2 Solid sample types – US EPA methods . . . 28.4.3 Reported studies—hot plate digestion . . . 28.4.4 Reported studies—microwave digestion methods . . . . . . . . . . . . . . . . . . . 28.4.5 Ultrasound-assisted extractions . . . . . . 28.4.6 Alkaline digestions . . . . . . . . . . . . . 28.4.7 Laboratory safety . . . . . . . . . . . . . . 28.5 Leach procedures . . . . . . . . . . . . . . . . . . 28.5.1 Toxicity characteristic leaching procedure . 28.5.2 TCLP regulatory limits . . . . . . . . . . .

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936 938 939 942 943

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945 949 949 950 950 951 951

Contents

28.5.3 TCLP method summary. . . . . . 28.5.4 TCLP applications . . . . . . . . 28.5.5 TCLP and sequential extractions . 28.5.6 TCLP limitations . . . . . . . . . 28.6 Certified reference materials . . . . . . . 28.7 Summary and future developments. . . . 28.8 Useful World Wide Websites . . . . . . . Acknowledgements . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . .

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Chapter 29. Sample preparation for semiconductor materials. . Katsu Kawabata, Yoko Kishi, Fuhe Li and Scott Anderson 29.1 Introduction. . . . . . . . . . . . . . . . . . . . . . 29.2 Contamination control . . . . . . . . . . . . . . . . 29.2.1 Clean room . . . . . . . . . . . . . . . . . . 29.2.2 Equipments, reagents and standards . . . . . 29.3 Sample preparation . . . . . . . . . . . . . . . . . . 29.3.1 Preparation and analysis of samples . . . . . 29.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . .

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952 953 954 956 957 957 960 961 961

. 965 . . . . . . . .

965 969 969 971 976 978 986 986

TRACE ELEMENT SPECIATION

Chapter 30. Sampling and sample treatment in the analysis of organotin compounds in environmental samples . . . . . . Roberto Morabito 30.1 Introduction. . . . . . . . . . . . . . . . . . . . . . 30.2 Critical steps in organotin analysis . . . . . . . . . . 30.2.1 Sampling . . . . . . . . . . . . . . . . . . . 30.2.2 Storage . . . . . . . . . . . . . . . . . . . . 30.2.3 Sample treatment . . . . . . . . . . . . . . . 30.3 Improving the quality of organotin measurements in Europe . . . . . . . . . . . . . . . . . . . . . . . 30.4 Detailed procedure for the GC – MS determination of organotin compounds in environmental samples . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . .

. 991 . 991 . 994 . 994 . 998 . 1000 . 1005 . 1017 . 1020 . 1021

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Contents

Chapter 31. Sample preparation for arsenic speciation . . . . . . 1027 Walter Goessler and Doris Kuehnelt 31.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . 1027 31.2 Occurrence and distribution of arsenic in the environment . . . . . . . . . . . . . . . . . . . 1028 31.2.1 Marine environment . . . . . . . . . . . . . . 1028 31.2.2 Terrestrial environment . . . . . . . . . . . . 1031 31.2.3 Humans . . . . . . . . . . . . . . . . . . . . . 1031 31.3 Stability of arsenic compounds . . . . . . . . . . . . . 1033 31.3.1 Arsenite and arsenate . . . . . . . . . . . . . 1034 31.3.2 Methylarsonous acid and dimethylarsinous acid . . . . . . . . . . . . . . . . . . . . . . . 1035 31.3.3 Methylarsonic acid and dimethylarsinic acid . . 1036 31.3.4 Arsenobetaine, arsenocholine, trimethylarsine oxide, and the tetramethylarsonium ion . . . . 1037 31.3.5 Arsenosugars . . . . . . . . . . . . . . . . . . 1037 31.4 Extraction of arsenic compounds from environmental samples . . . . . . . . . . . . . . . . . . . . . . . . . 1038 31.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . 1041 References . . . . . . . . . . . . . . . . . . . . . . . . . . . 1041 Chapter 32. Sample preparation for speciation of selenium. . . . . . . . . . . . . . . . . . . . . Claudia Ponce de Leon, Anne P. Vonderheide and Joseph A. Caruso 32.1 Why selenium speciation?. . . . . . . . . . 32.2 General sample preparation . . . . . . . . 32.3 Mammals . . . . . . . . . . . . . . . . . . 32.3.1 Body fluid analysis . . . . . . . . . 32.3.2 Tissue sample analysis . . . . . . . 32.4 Fish/birds . . . . . . . . . . . . . . . . . . 32.5 Plants . . . . . . . . . . . . . . . . . . . . 32.5.1 Leafy plants . . . . . . . . . . . . . 32.5.2 Broccoli . . . . . . . . . . . . . . . 32.5.3 Spices (garlic, onion, white clover) . 32.5.4 Grains. . . . . . . . . . . . . . . . 32.5.5 Nuts. . . . . . . . . . . . . . . . .

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. 1045 . 1045 . 1046 . 1046 . 1049 . 1050 . 1051 . 1052 . 1053 . 1053 . 1054 . 1054

Contents

32.5.6 Mushrooms . . . . . . . 32.6 Microorganisms . . . . . . . . . 32.7 Environmental . . . . . . . . . 32.7.1 Air. . . . . . . . . . . . 32.7.2 Water . . . . . . . . . . 32.7.3 Soil and sediments (solid References . . . . . . . . . . . . . . .

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. 1054 . 1055 . 1055 . 1055 . 1056 . 1058 . 1059

Chapter 33. Sample preparation for mercury speciation . . . . Holger Hintelmann 33.1 Introduction. . . . . . . . . . . . . . . . . . . . . . 33.2 Aqueous solution chemistry of methylmercury . . . . 33.3 Sample collection, preservation and storage . . . . . 33.3.1 Cleaning of sampling and laboratory equipment. . . . . . . . . . . . . . . . . . . 33.3.2 Water sampling . . . . . . . . . . . . . . . . 33.3.3 Preservation and storage of water samples . . 33.3.4 Preservation and storage of tissue and vegetation samples . . . . . . . . . . . . . . 33.3.5 Preservation and storage of soil and sediment samples . . . . . . . . . . . . . . . . . . . . 33.4 Sample preparation . . . . . . . . . . . . . . . . . . 33.4.1 Extraction of methylmercury from water . . . 33.4.2 Extraction of methylmercury from soils, sediments and particles . . . . . . . . . . . . 33.4.3 Extraction of methylmercury from biological tissue . . . . . . . . . . . . . . . . . . . . . 33.4.4 Direct techniques involving no sample preparation . . . . . . . . . . . . . . . . . . 33.4.5 Extraction of mercury species other than methylmercury . . . . . . . . . . . . . . . . 33.5 Quality control . . . . . . . . . . . . . . . . . . . . 33.5.1 Artifactual formation of methylmercury . . . 33.5.2 Spike recoveries. . . . . . . . . . . . . . . . 33.5.3 Reference materials . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . .

. 1063 . 1063 . 1063 . 1065 . 1066 . 1066 . 1067 . 1068 . 1068 . 1069 . 1071 . 1072 . 1073 . 1074 . 1074 . 1075 . 1075 . 1076 . 1077 . 1079 . 1079

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Contents

Chapter 34. Sample preparation for speciation of lead . . . . . . 1081 Freddy C. Adams and Monika Heisterkamp 34.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . 1081 34.2 Toxicity of organolead compounds . . . . . . . . . . . 1083 34.3 The history of leaded gasoline . . . . . . . . . . . . . 1085 34.4 Properties of organolead compounds . . . . . . . . . . 1088 34.5 Synthesis of organolead compounds . . . . . . . . . . 1090 34.6 The biogeochemical cycle of lead . . . . . . . . . . . . 1092 34.7 Analytical techniques for speciation analysis of organolead compounds . . . . . . . . . . . . . . . . . 1092 34.7.1 Hyphenated techniques for organometal determinations . . . . . . . . . . . . . . . . . 1093 34.7.2 Sample preparation . . . . . . . . . . . . . . . 1096 34.7.3 Sample pretreatment using gas chromatographic separation . . . . . . . . . . 1097 34.7.4 Extraction recovery . . . . . . . . . . . . . . . 1100 34.7.5 Separation . . . . . . . . . . . . . . . . . . . 1101 34.7.6 Detection of organolead compounds after chromatography. . . . . . . . . . . . . . . . . 1104 34.7.7 Procedures for the determination of organolead compounds in dust material . . . . . . . . . . 1107 34.7.8 Comparison of the different hyphenated systems . . . . . . . . . . . . . . . . . . . . . 1109 References . . . . . . . . . . . . . . . . . . . . . . . . . . . 1111

Chapter 35. Sample preparation for chromium speciation . . . Miguel de la Guardia and Angel Morales-Rubio 35.1 The element and its reactivity . . . . . . . . . . . . 35.2 The presence of Cr in nature and industrial processes 35.3 Chemical species of Cr present in real samples. . . . 35.4 Analytical methodologies available for determination of Cr . . . . . . . . . . . . . . . . . . . . . . . . . 35.5 Analytical methodologies for Cr speciation in water . 35.5.1 Speciation of chromium in aqueous matrices . 35.5.2 Types of samples analyzed . . . . . . . . . . 35.5.3 Pretreatments and techniques applied . . . .

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. 1115 . 1115 . 1117 . 1118 . 1120 . 1120 . 1121 . 1121 . 1123

Contents

35.5.4 Speciation chromium using atomic spectrometry and MS-based techniques . . . . . . . . . . . . 1124 35.5.5 Determination of chromium speciation using molecular spectrophotometry . . . . . . . . . . 1130 35.6 Analytical methodologies for Cr speciation in biological fluids . . . . . . . . . . . . . . . . . . . . . . . . . . 1136 35.6.1 Speciation of chromium in biological fluids . . . 1136 35.6.2 Types of samples analyzed . . . . . . . . . . . 1137 35.6.3 Pretreatments and techniques applied . . . . . 1137 35.6.4 Chromium speciation using atomic spectrometry detection . . . . . . . . . . . . . 1138 35.6.5 Chromium speciation using molecular spectrophotometry detection . . . . . . . . . . 1143 35.7 Analytical methodologies for speciation of Cr in solid samples . . . . . . . . . . . . . . . . . . . . . . . . . 1145 35.7.1 Speciation of chromium in solid samples . . . . 1145 35.7.2 Types of samples analyzed . . . . . . . . . . . 1145 35.7.3 Solid sample treatments for speciation of chromium . . . . . . . . . . . . . . . . . . . . 1155 35.8 Final considerations . . . . . . . . . . . . . . . . . . 1157 References . . . . . . . . . . . . . . . . . . . . . . . . . . . 1158

Chapter 36. Sample preparation for metal-based drugs . Ronald R. Barefoot 36.1 Introduction. . . . . . . . . . . . . . . . . . 36.2 Platinum-based drugs. . . . . . . . . . . . . 36.2.1 Preparation of biological samples . . . 36.2.2 Cisplatin . . . . . . . . . . . . . . . 36.2.3 Carboplatin . . . . . . . . . . . . . . 36.2.4 Oxaliplatin . . . . . . . . . . . . . . 36.2.5 Ormaplatin . . . . . . . . . . . . . . 36.2.6 Lobaplatin . . . . . . . . . . . . . . 36.2.7 JM216. . . . . . . . . . . . . . . . . 36.2.8 Validated methods of analysis . . . . 36.3 Gold-based drugs . . . . . . . . . . . . . . . 36.3.1 Sample preparation . . . . . . . . . .

. . . . . 1173 . . . . . . . . . . . .

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. 1173 . 1173 . 1175 . 1176 . 1177 . 1177 . 1178 . 1178 . 1178 . 1179 . 1180 . 1175

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Contents

36.4 Mercury . . 36.5 Vanadium . 36.6 Lead. . . . 36.7 Conclusions References . . . .

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Chapter 37. Sample preparation for speciation analysis for metallobiomolecules . . . . . . . . . . . . . . . . . . . . . Joanna Szpunar, Brice Bouyssiere and Ryszard Lobinski 37.1 Introduction. . . . . . . . . . . . . . . . . . . . . . 37.2 Elemental species in biological systems: metallobiomolecules. . . . . . . . . . . . . . . . . . 37.3 Tailoring sample preparation: definition of the analyte moiety . . . . . . . . . . . . . . . . . . 37.4 Homogenization and analyte recovery using unreactive buffers . . . . . . . . . . . . . . . . . . . 37.5 Analyte recovery through partial degradation of sample matrix . . . . . . . . . . . . . . . . . . . 37.5.1 Extraction with protein denaturating reagents . . . . . . . . . . . . . . . . . . . . 37.5.2 Enzymatic extraction of organometallic compounds . . . . . . . . . . . . . . . . . . 37.5.3 Controlled enzymatic degradation prior to speciation of metal complexes. . . . . . . . . 37.5.4 Sequential enzymatic extractions for the evaluation of the bioaccessibility of metals in foodstuffs . . . . . . . . . . . . . . . . . . . 37.6 Fractionation of metal species according to the molecular weight prior to analytical chromatography or capillary electrophoresis . . . . . . . . . . . . . . 37.6.1 Ultrafiltration . . . . . . . . . . . . . . . . . 37.6.2 Gel filtration . . . . . . . . . . . . . . . . . 37.7 Multidimensional LC clean-up procedures prior to characterization of metal species by electrospray MS 37.8 Sample preparation prior to speciation analysis of biological fluids . . . . . . . . . . . . . . . . . . . .

xl

. 1181 . 1181 . 1182 . 1182 . 1182

. 1185 . 1185 . 1186 . 1188 . 1188 . 1190 . 1190 . 1190 . 1191

. 1192

. 1193 . 1194 . 1194 . 1195 . 1196

Contents

37.8.1 Arsenic in urine . . . . . . . . . . . . . . . . . 1196 37.8.2 Selenium in urine . . . . . . . . . . . . . . . . 1197 37.8.3 Metal complexes in biological fluids . . . . . . 1198 37.8.4 Metallodrug metabolites in biological fluids . . 1199 37.9 Sample preparation prior to speciation analysis in solid matrices . . . . . . . . . . . . . . . . . . . . . . 1200 37.9.1 Organoarsenic species in marine biota and foodstuffs . . . . . . . . . . . . . . . . . . . . 1200 37.9.2 Low-molecular organoselenium species in yeast and plants. . . . . . . . . . . . . . . . . . . . 1201 37.9.3 High-molecular selenium species in animal tissues and yeast . . . . . . . . . . . . . . . . 1203 37.9.4 Metal complexes with metallothioneins. . . . . 1203 37.10 Sources of error . . . . . . . . . . . . . . . . . . . . . 1205 References . . . . . . . . . . . . . . . . . . . . . . . . . . . 1206 Chapter 38. Sample preparation for the analysis metals species . . . . . . . . . . . . . . . . Jo¨rg Feldmann 38.1 Introduction. . . . . . . . . . . . . . 38.2 Species of interest. . . . . . . . . . . 38.3 Characterization of gas samples . . . 38.4 Sample preparation strategies . . . . 38.4.1 Sampling . . . . . . . . . . . 38.4.2 Preconcentration . . . . . . . 38.5 Specific procedures . . . . . . . . . . 38.5.1 Cryotrapping methods . . . . 38.5.2 Solid phase micro extraction . 38.5.3 Adsorption method . . . . . . 38.6 Problems and future studies . . . . . References . . . . . . . . . . . . . . . . . .

of volatile . . . . . . . . . 1211 . . . . . . . . . . . .

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. 1211 . 1212 . 1216 . 1216 . 1217 . 1217 . 1222 . 1223 . 1227 . 1227 . 1229 . 1230

Chapter 39. Sequential extraction . . . . . . . . . . . . . . . . . 1233 Angels Sahuquillo and Gemma Rauret 39.1 Sequential extraction procedures: a special case of sample dissolution . . . . . . . . . . . . . . . . . . . 1233

xli

Contents

39.1.1 A brief historical introduction . . . . . . . . 39.1.2 Definition of sequential extraction procedures 39.2 Types, uses and limitations of SEPs . . . . . . . . . 39.2.1 Characteristics of the extraction agents . . . 39.2.2 SEPs more widely used . . . . . . . . . . . . 39.2.3 Types of matrices and elements analysed. . . 39.2.4 Use of the information obtained . . . . . . . 39.2.5 Limitations of SEPs . . . . . . . . . . . . . . 39.3 Sample pre-treatment for SEPs. . . . . . . . . . . . 39.3.1 Drying. . . . . . . . . . . . . . . . . . . . . 39.3.2 Grinding and sieving steps . . . . . . . . . . 39.3.3 Use of inert atmosphere . . . . . . . . . . . 39.3.4 Recommendations. . . . . . . . . . . . . . . 39.4 Application of other extraction techniques to SEPs. . 39.4.1 Microwave . . . . . . . . . . . . . . . . . . 39.4.2 Ultrasound . . . . . . . . . . . . . . . . . . 39.4.3 Other alternatives . . . . . . . . . . . . . . 39.4.4 Conclusions . . . . . . . . . . . . . . . . . . 39.5 Quality control for SEPs . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . .

. 1233 . 1234 . 1235 . 1235 . 1235 . 1239 . 1241 . 1242 . 1246 . 1246 . 1248 . 1249 . 1250 . 1250 . 1250 . 1251 . 1251 . 1251 . 1252 . 1253

Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1257

xlii

Series Editor’s Preface This book on Sample Preparation for Trace Element Analysis, edited by Zoltan Mester and Ralph Sturgeon, is a useful addition to the Comprehensive Analytical Chemistry series. The impressive number of pages indicates the importance of sample preparation in the area of trace element determination. In a way, it follows the philosophy of a previous book in the series edited by Janusz Pawliszyn (Sampling and Sample Preparation for Field and Laboratory, vol XXXVII), and devoted to organic analysis. In that work, the two editors of this volume contributed a chapter on sample preparation for trace element speciation. It is a pleasure for me to introduce such a comprehensive book with a total of 39 chapters divided in four sections, including several introductory chapters on sampling, calibration, traceability and detection methods. These are followed by 17 chapters dealing with approaches to sample digestion and extraction. This is obviously one of the key issues in sample preparation, and for this reason a variety of chapters that include most of the methods in use – microwaves, solid phase microextraction, membrane extraction, laser ablation, flow injection etc. – are presented. The final 10 chapters cover specific applications to trace element speciation, dealing with different species and matrices, e.g. organotin, mercury, arsenic, metal-based drugs, chromium and also sequential extraction. The book includes a long list of recognised experts. In addition, many of them are previous contributors to books in this series dealing with speciation. In this respect, the present book is complementary to two previous volumes in the series – vol XXXIII on Elemental Speciation edited by Joe Caruso et al. and vol XXXIV on Discrete Sample Introduction Techniques for Inductively Coupled Plasma Mass Spectrometry by Diane Beauchemin and co-authors. With the publication of these three books the Comprehensive Analytical Chemistry series has extensively covered the area of elemental analysis, speciation and the very important bottleneck of sample preparation. I am sure that all three volumes will be a valuable reference for all researchers working in these fields. Finally I would like to thank not only the editors of the book but also the various authors for their contributions towards such a comprehensive, unique book on sample preparation for trace element analysis. Professor D. Barcelo´ Dept. of Environmental Chemistry IIQAB-CSIC Barcelona, Spain

xliii

Preface Two years ago we were asked to write a short review on sample preparation for trace metal speciation as a contribution to a book dealing with general sample preparation issues. Over the course of this work, we realized that this short review was rather an extended table of contents for a future project. We were also acutely aware that there was no comprehensive book devoted to sample preparation on the market dealing with the analysis of samples for trace elements. The stage was thus set. Following the collection of a sample, every analytical chemist will agree that its subsequent preservation and processing are of paramount importance. The availability of high performance analytical instrumentation has not diminished this need for careful selection of appropriate pretreatment methodologies, intelligently designed to synergistically elicit optimum function from these powerful measurement tools. These were the objectives of this book, to present, in a concise and comprehensive volume, an account of the state-of-the art of this subject matter. When considering the need for publication of a body of work such as this, it is wise to invest time appraising current literature; with the high cost of books, there can be no defense for simply making yet another one available. From our perspective, Sample Preparation for Trace Element Analysis was conceived because we believe there was no modern, comprehensive treatise at hand to satisfy the varied needs of the practicing analytical chemist. Without doubt, many of the subject areas targeted in this book have already received in-depth treatment by appropriate monographs. Assembling this knowledge into a single source proves advantageous to the user only if it is accomplished concisely and comprehensively. We hope the reader will vindicate our conclusions. This book is a multiauthor work, reflecting the diverse expertise arising from its highly qualified contributors. Efforts have been made to maintain a uniformity of style and diction, but readers will agree that the advantages which accrue from the talents of these individuals outweigh that arising from the simple uniformity gained with a single-author treatise. The cooperation of all the contributors in providing material for this book is thus deeply appreciated. The 39 chapters are authored by international leaders of their fields. The first five chapters deal with general issues related to the determination of trace metals in varied matrices, such as sampling, contamination control, reference materials, calibration and detection techniques. The second part of

xliv

Preface

the book deals with extraction and sampling technologies (totaling 15 chapters), providing theoretical and practical hints for the users on how to perform specific extractions. Subsequent chapters overview seven major representative matrices and the sample preparation involved in their characterization. This portion of the book is heavily based on the preceding chapters dealing with extraction technologies. The last ten chapters are dedicated to sample preparation for trace element speciation. Dating from the original discussions with the Publisher, this book has been realised in record time, requiring less than two years to advance from concept to fruition, thanks to excellent work of the over 70 contributing authors and the efforts of the Publisher. The editors and authors hope that readers will find this book useful and instructive and that it will be consulted frequently as a source of information which will make sample preparation less challenging for both the novice and seasoned expert alike. We wish to acknowledge the support of our home organization: the Institute for National Measurement Standards of the National Research Council of Canada, a stimulating environment and center of excellence for analytical chemistry research. Finally, we wish to thank the contributing authors for the privilege to work with them on this project and our families their patience and love for having forgone our company on many occasions. Zoltań Mester Ralph E. Sturgeon

xlv

Acronyms 2-MBT 8-HQ AAS ACN ACP AED AFM AFS ANOVA ARC AsB AsC ASE ASTM ASV BEC CCD CCFA CCP CE CEA CGC CL CPG CPX CRM CSV CTF CV CV-AAS CZE-UV DAD DAL DBT DC arc emission

xlvi

2-mercaptobenzothiazole 8-hydroxyquinoline atomic absorption spectrometry acetonitrile alternating current plasma atomic emission detection atomic force microscopy atomic fluorescence spectrometry analysis of variance anti-reflective coating arsenobetaine arsenocholine accelerated solvent extraction American Society for Testing and Materials anodic stripping voltammetry background equivalent concentration charge coupled device completely continuous flow analysis capacitively coupled plasma capillary electrophoresis combustion elemental analysis capillary gas chromatography chemiluminescence controlled pore glass complexation certified reference material cathodic stripping voltammetry centrifugation coefficient of variation cold vapour atomic absorption spectrometry capillary zone electrophoresis ultraviolet spectrophotometry diode array detector dialkyllead dibutyltin direct current arc emission spectrometry

Acronyms

DCP DCP-OES DE DEL DESe DIN DIW DLF-AAS DMA DMA(III) DMDSe DML DMSe DOM DP-ASV DPCSV DPhT DRC-ICP-MS DSI DTA DZ ECD ED-XRF EI-MS EL ESI Et ET-AAS: EtOH ETV EXT F-AAS FAB F-AES FIA FID FLD FPD FT-ICR-MS FTT GC(EC): GCB

direct current plasma direct coupled plasma optical emission spectrometry diatomaceous earth diethyllead diethyl selenide direct injection nebulizer deionized water diode laser flame atomic absorption spectrometry. dimethylarsinic acid dimethylarsinous acid dimethyl diselenide dimethyllead dimethyl selenide dissolved organic material differential pulse anodic stripping voltammetry differential pulse cathodic stripping voltammetry diphenyltin dynamic reaction cell ICP-MS direct sample insertion diethylenetriamine dithizone electron capture detection energy dispersive X ray fluorescence electron impact ionization mass spectrometry ethyl lactate electrospray ionization ethyl electrothermal (graphite furnace) atomic absorption spectrometry ethanol electrothermal vaporization liquid extraction flame atomic absorption spectrometry fast atom bombardment flame atomic emission spectrometry flow injection analysis flame ionization detection fluorimetric detection flame photometric detection fourier transform ion cyclotron resonance mass spectrometry filtration gas chromatography with electron capture detection graphitized carbon black

xlvii

Acronyms

GC-MS GD-MS GF-AAS GLP GTF HEPA HG HMDE HMW HPA HPLC HRGC HR-ICP-MS HSAB HT18C6TO HTA IAEA IBMK IC ICP ICP-MS ICP-OES ICP-QMS ICP-RC-MS ICP-TOF-MS ID-MS ID-ICP-MS ID-TIMS INAA IP IR IRMM ISE ISO ITRS IUPAC KR LAS LC LEAF(S)

xlviii

gas chromatography-mass spectrometry glow discharge mass spectrometry graphite furnace atomic absorption spectrometry good laboratory practice glucose tolerance factor high efficiency particulate air hydride generation hanging mercury drop electrode high molecular weight high pressure ashing high performance liquid chromatography high resolution gas chromatography high resolution (sector field) ICP-MS hard-soft acid-base hexathia-18-crown-6-tetraone high temperature ash International Atomic Energy Agency isobutyl methyl ketone ion chromatography inductively coupled plasma inductively coupled plasma mass spectrometry inductively coupled plasma (atomic) optical emission spectrometry ICP-quadrupole MS ICP-reaction cell-MS ICP-time of flight-MS isotope dilution mass spectrometry isotope dilution inductively coupled plasma mass spectrometry isotope dilution thermal ionization mass spectrometry instrumental neutron activation analysis (NAA) ion pair infra-red Institute for Reference Materials and Measurements ion selective electrode International Organization for Standardization International Technology Roadmap for Semiconductors International Union of Pure and Applied Chemistry knotted reactor light absorption spectrometry (molecular UV-visible absorption) liquid chromatography laser excited atomic fluorescence (spectrometry)

Acronyms

LIBS LiM LIMS LiT LLE LMW LOD LOV LRM LSASV LSE LTA MA MA(III) MALDI MBE MBT Me MEKC MeOH MIP MLS MMA MMLLE MOCVD MPD MPhT MPT MS MT MW NAA NCH NIES NIOSH NIST Nl NMR NRCC NSOM NWA ODETA OES OLR

laser induced breakdown spectrometry lithium metaborate, LiBO2 laboratory information management system lithium tetraborate, Li2B4O7 liquid-liquid extraction low molecular weight limit of detection lab-on-valve laboratory reference material linear sweep anodic stripping voltammetry liquid-solid extraction low temperature ashing methylarsonic acid methylarsonous acid matrix assisted laser desorption mass spectrometry molecular beam epitaxy monobutyltin methyl micellar electrokinetic chromatography methanol microwave induced plasma master laboratory station monomethyarsonic acid microporous membrane liquid-liquid extraction molecular organic compound vapor deposition microwave-induced plasma detector monophenyltin microwave plasma torch mass spectrometry metallothionein microwave neutron activation analysis neocuproine National Institute for Environmental Studies National Institute of Occupational Safety and Health National Institute of Standards and Technology measured gas volume in liter at 08C. nuclear magnetic resonance National Research Council of Canada near-field scanning optical microscopy non-wetting agents 4-(N-octyl)diethylenetriamine optical emission spectrometry ordinary linear regression

xlix

Acronyms

OXI PA PAA PAAM PADMAP PAH PAR PBMS PDMS PE PEC PGC Ph PIXE PP PR PS-MS PTFE PTV PUF PVC P-XRF QA QCM QF-AAS QMS QTA QZ RCC REE RM RNAA ROMP RP RSD RTD SA SDS SEC SE-FLR SEP SF SFE SF-ICP-MS

l

oxidation polyacrylate photon activation analysis piconilic acid amide 2-(2-pyridylazo)-5-dimethylaminophenol polyaromatic hydrocarbon 4-(2-pyridylazo)-porphyrin performance based measurement system polydimethyl siloxane polyethylene power and event controller porous graphitized carbon phenyl proton induced x-ray emission spectrometry polypropylene photoresist plasma source mass spectrometry polytetrafluoroethylene programmed temperature vaporization polyurethane foam polyvinylchloride portable XRF quality assurance quality control material quartz furnace atomic absorption spectrometry quadrupole mass filters (heated) quartz tube atomizer quartz residual carbon content rare earth element reference material radiochemical separation neutron activation analysis ring-opening metathesis polymerization reverse phase relative standard deviation resistance temperature detector salicylic acid sodium dodecyl sulfonate size exclusion chromatography solvent extraction fluorometry (molecular) sequential extraction procedure supercritical fluid supercritical fluid extraction sector field ICP-MS

Acronyms

SGBM SI SI SIA SIM SIMS SLM SPE SPME SPS SRM SS-MS STAT T4BPP TAL TBT TCD TCLP TD TeAL TeEL TEL TeML TFA THET-AAS: THF TIMS TMAB TMAO TML TMOS TOF-MS TPB TPhT TprT TS-FF-AAS T-XRF UE ULPA UPW US US EPA UV-VIS

silica gel bound macrocycles Syste´me International sequential injection sequential injection analysis selected ion monitoring secondary ion mass spectrometry supported liquid membrane extraction solid phase extraction solid phase microextraction solid phase spectrophotometry standard reference material spark source mass spectrometry slotted tube atom trap tetra-(4-bromophenyl)-porphyrin trialkyllead tributyltin thermal conductivity toxicity characteristic leaching procedure thermodesorption tetraalkyllead tetraethyllead triethyllead tetramethyllead trifluoroacetylacetone transverse heated graphite atomizer ET-AAS (THGA: transverse heated graphite atomizer) tetrahydrofuran thermal ionization mass spectrometry tetramethylammonium bromide trimethylarsine oxide trimethyllead tetramethoxy silane time of flight mass spectrometry tetraphenylborate triphenyltin tripropyltin thermospray flame-furnace AAS total reflection XRF ultrasonic extraction ultra low penetration air ultrapure water ultrasound United States Environmental Protection Agency ultraviolet visible spectrometry

li

Acronyms

VG VMC VOCs VOL VPD WHO WLR XRA XRF ZHE

lii

vapor generation volatile metal(loid) compound volatile organic compounds volumetry (titrimetry) vapor phase deposition World Health Organization weighted linear regression X-ray absorption X-ray fluorescence spectrometry zero headspace extraction

Chapter 1

Sampling and sample preservation for trace element analysis Byron Kratochvil

1.1

INTRODUCTION

Modern analytical methods and instrumentation make possible the measurement of increasingly smaller concentrations of even the most complex molecules and species in complex matrices. This has increased the importance of collecting, storing, and processing samples for analysis in a manner that keeps them as unaltered and contamination free as possible. In addition, improved measurement techniques and tools allow, or often require, the use of smaller analytical test portions to determine analyte concentrations. Small test portions mean more difficulty in achieving representativeness of the population, especially when analyzing for trace components. First of all, the quality of any analytical result depends on sample representativeness and integrity. Although many sources of error in an analysis can be controlled through use of blanks, standards, or reference samples, neither blank nor standard can repair the damage caused by an invalid sample. Keith [1], in the preface of a book on environmental sampling, says: “The logic is simple. If the right kinds of samples are not collected from the right areas at a site and then preserved, prepared, and analyzed correctly, wrong answers will be obtained. They may be precise and accurate answers, but they will be wrong in that they will not represent the condition of the site with respect to the absence, presence, or representative concentrations of the pollutants of interest.” Keith’s statement applies with equal validity to all analytical sampling operations regardless of analyte, concentration, or matrix. This chapter outlines some general principles of sampling design and sample preservation. Specific sampling and sample preparation procedures for various matrices and individual elements are treated in subsequent chapters. A brief bibliography and glossary of selected sampling terms are provided at the end of this chapter. Comprehensive Analytical Chemistry XLI Mester and Sturgeon (Eds.) q 2003 Elsevier B.V. All rights reserved

1

B. Kratochvil

1.2 1.2.1

PRELIMINARY CONSIDERATIONS Sampling variability

When obtaining an estimate of the uncertainty in an analytical result, the uncertainty in the sampling step is often significant, and frequently far larger than the measurement uncertainty. For random errors, the overall standard deviation, so, is related to the standard deviation for the sampling operation, ss, and to that for the remaining analytical operations, sa, by: s2o ¼ s2a þ s2s

ð1:1Þ

Measurements should be designed insofar as possible to allow the separate evaluation of sample and measurement variability. For measurements in a state of statistical control, sa can be determined by the analysis of reference materials or standards. Then, ss can be obtained from Eq. (1.1), because so is obtained by analysis of a series of samples. Alternatively, a set of replicate measurements on samples may be designed to evaluate both sa and ss. Youden [2] noted that further reduction in the analytical uncertainty is unimportant once it is one-third or less of the sampling uncertainty. So, if the sampling uncertainty is large, use of a rapid, approximate analytical method may be faster, simpler, and permit more samples to be tested, thereby reducing overall uncertainty without increasing time or effort. 1.2.2

Sampling strategies

Sampling strategies may be classified as judgmental (intuitive), statistical, or systematic. Judgment sampling relies on general knowledge gained by experience with the population (or similar ones). Therefore, any conclusions drawn from the resulting data are necessarily intuitive, in part. Statistical sampling is based on all parts of the population having an equal chance of being selected. With a statistical sampling strategy, conclusions may be drawn based on statistical probabilities. In systematic sampling, the sample increments are collected in a regular pattern throughout the population. It has the advantage that execution is usually more straightforward and less expensive. Protocol sampling is a form of sampling specified in defined circumstances, often by regulatory agencies or by groups, such as the American Society for Testing and Materials (ASTM), as a basis for decision-making in legal and commercial matters. For example, regulations may specify detailed sampling procedures, which, if not followed, could make the sample invalid for the intended purpose. The sampling procedure may be intuitive, statistical, or a combination, but must be followed explicitly.

2

Sampling and sample preservation for trace element analysis

1.2.3

Uncertainties in sampling

Sampling uncertainties may arise either from the properties of the population, and therefore inherent to any sample taken from it, or from the sampling operation itself. These uncertainties may be reduced, but never completely eliminated, by careful execution of a properly designed sampling plan that incorporates identification of the population and sampling sites, along with the procedures required to deliver an uncontaminated, representative sample to the analytical laboratory. An important source of sampling uncertainty is systematic, non-random, bias caused by exclusion or inclusion in the sample of some components of the population over others owing to differences in size, mass, location, stickiness, and so on. Another is sample contamination or change during collection, transport, storage, or preparation for analysis (this topic is discussed in Section 1.6). Poor design or improper use of sampling equipment may also introduce bias, as may the omission of collateral measurements, such as flow rate or pressures, that affect results. 1.3 1.3.1

TYPES OF SAMPLES Judgment samples

Judgment samples are samples collected from a population on the basis of experience, intuition, and knowledge of the history or properties of the population (or related ones). Sometimes, the goal is to obtain a single sample that may be termed “representative” to connote that it is expected to exhibit the average properties of the population. Collection of a single sample may have validity in situations where the population is essentially homogeneous or made so prior to sample collection. It may also be legitimate when random sampling is difficult or impossible owing to safety or cost considerations. Under these conditions, however, the shortcomings of the sampling operation and the limitations in data treatment should be clearly stated. Generally, a plan based on at least some elements of random sampling is recommended. Judgment sampling requires assumptions about the degree to which the samples may be considered representative. Because the validity of the assumptions depends on the experience of the one making them, it is difficult to know the degree to which they are acceptable for a given application. A major advantage of judgment sampling is that it is usually less costly than rigorous random sampling. For regulatory or legal purposes, however, personal bias should be reduced or eliminated as much as possible. Often a combination of judgment and random sampling provides the best compromise between unacceptable costs and data quality.

3

B. Kratochvil

1.3.2

Random samples

Analyses are almost always performed to obtain information about a population that is larger than the test portions being measured. If the samples under examination are biased, inferences made from them will be biased. The difference between the target population to which conclusions are applied, and the samples from which the test portions for analyses are drawn, may be minimized by selecting samples in a manner that gives each part of the population an equal chance of selection. This process, called random sampling, allows the user of the resulting analytical data to make statistical generalizations based on mathematical probabilities. Selecting truly random samples is difficult; random in this context does not mean haphazard. A recommended method for a population consisting of units such as pharmaceutical tablets is to use random numbers to select units for analysis. Each unit is assigned a number, and units are selected by use of a random number generator.1 Bulk materials may be divided into a number of real or imaginary segments; the segments may be areas on a two-dimensional surface or volumes for a three-dimensional population. Data obtained by measurements on random samples can be analyzed by statistical methods to identify whether systematic relations among results exist due to trends or biases in the measurements. 1.3.3

Systematic samples

Because of its simplicity, sampling at evenly spaced intervals over a population is often used in place of random sampling. The criterion that all parts of the population have an equal chance of selection may be satisfied for evenly spaced sampling by imposing a random start time or sampling location on the process. This allows the application of classical statistical tests to the data. A potential problem with systematic sampling is that results may be biased if the analyte of interest is distributed in a periodic fashion within the population. It is also sometimes useful to collect samples in a systematic manner to reflect or test a hypothesis, such as the presence of systematic changes in population composition with time, temperature, or spatial location. Under specified conditions, each sample may be considered as a separate discrete population but the results may still be statistically tested for the significance of apparent differences. 1

Random numbers may be obtained from several sources on the Internet. A good example is http://www.fourmilab.ch/hotbits/, which generates sequences of random numbers based on radioactive decay of krypton-85. A Geiger– Muller tube is interfaced to a computer and the times between successive pairs of radioactive decays measured and provided as bytes. Once the bytes are delivered, they are discarded.

4


1.3.4

Subsamples

Field samples are typically placed in containers and sent to the laboratory for further processing. Sometimes, transport of all the field increments to the laboratory is deemed too inefficient or costly. In this case, the increments may be homogenized, after crushing or grinding if needed, and subsampled on site prior to transport. The work needed to reduce particle size, blend, or otherwise to process a bulk field sample before withdrawing subsamples for analysis depends on the variability in composition of the material constituting the original sample and on the extent of mixing required. Care must be taken to avoid contamination or loss that may introduce bias. Generally, processing and subsampling in a clean, controlled environment whenever possible provide better quality control. When subsampling is done in the field, the sampling plan, discussed in Section 1.4.2, should specify that the sampler have sufficient training and knowledge of sampling theory to subsample properly. Also, the analyst should be provided with all available information on prior subsampling and homogenizing operations. 1.3.5

Composite samples

Sometimes, increments are combined to produce a laboratory sample that is defined as representative. Advantages of compositing include reduced sample handling and analytical effort. It provides an estimate of the average concentration of the analyte, but not of its distribution. A variety of sampling systems and mixing procedures have been developed to produce composites from both liquid and solid materials. Compositing of increments is attractive when costs of analytical measurements are greater than the costs of sampling. But potentially useful information, such as the presence of hot spots, may be lost. Analysis of individual increments allows not only estimation of the distribution of the analyte within the population, but also evaluation of apparent differences within and among samples. Garner et al. [3] discuss the advantages and limitations of composite sampling for environmental monitoring. 1.4 1.4.1

PLANNING THE SAMPLING OPERATION Defining goals

Several key decisions should be made before sampling is initiated. These include defining the population to be studied, the substance(s) to be measured, the precision required in the result, and the extent to which speciation and distribution within the population is needed. Any assumptions about the population should be clearly identified. Decision-makers should preferably

5

B. Kratochvil

Fig. 1.1. Elements of the overall analytical process.

include the client for the data, sampling personnel, the person responsible for the analytical work, and someone knowledgeable about statistics. Decisions made at this point establish the goals of the work, and are the first step in the overall analytical process (Fig. 1.1). With this information in hand, a viable sampling plan can be drafted. 1.4.2

Sampling plans

The wide variety of populations sampled for chemical analysis makes the establishment of a single overall protocol impossible; accordingly, each matrix requires its own sampling plan. Often, regulatory agencies issue documents covering analytical methodologies that include sampling procedures. Examples include the US Environmental Protection Agency (US EPA), the International Organization for Standardization (ISO), and the ASTM. In addition, many specialty groups, such as the American Water Works Association, provide information on sampling protocols, tools, and techniques applicable to specific matrices. Where the analytical data may involve potential legal issues regarding compliance with environmental regulation, with workplace safety, or with commercial contract agreements, protocols recommended by recognized associations or agencies should be used whenever possible. All valid sampling plans and protocols have basic elements in common. These elements include specification of the size, number, and location of sample increments, the extent of compositing where warranted, and steps for subsampling (after particle size reduction, if applicable, and mixing) of the initial increments to produce laboratory samples and test portions. The plan should be in the form of a written protocol that includes procedures for all steps, from initial sample collection to final preparation of test portions for analysis. The protocol should tell when, where, and how to collect sample increments. It should include criteria for rejection of material not part of the population, as for example stones above a defined size in a soil sample being analyzed for available trace nutrients. It should also specify who performs the sampling, sample logging and chain of custody procedures, the type and size of containers to be used, cleaning procedures for equipment and containers, preservatives, conditions of sample storage and, as appropriate, auxiliary information such as temperature or flow velocity in a stream. It should also list the qualifications and training required of the personnel carrying out the operations. A checklist, adapted from Ref. [4], is provided in Table 1.1.

6

Sampling and sample preservation for trace element analysis TABLE 1.1 Checklist for elements of a sampling protocol (after Ref. [4]) Apparatus and equipment checklist Sampling tools and apparatus Sample containers of appropriate type, material, and size Cleaning supplies for tools, equipment, and containers Preservatives, including provision for cooling of samples if necessary Labels, tape, waterproof pens, packaging materials Chain of custody forms, sample seals, log books Safety equipment, including protective clothing

Instructions checklist for presampling Recording of observations at sampling sites Cleaning of apparatus before and after sampling Calibration of apparatus Cleaning and handling of sample containers Safety procedures Procedure if problems prevent strict adherence to protocol

Instructions checklist for sampling Number, type, and size of exploratory, regular, and quality assurance samples Number, type, and size of sample increments Procedure for identifying locations from which increments are to be collected Procedure for operation of apparatus and collection of increments Special sampling precautions or conditions of collection, including criteria for rejection of foreign material Procedure for compositing, if applicable Use of preservatives

Instructions checklist for postsampling Completion of auxiliary information on sample labels and in logbooks Chain of custody forms Sample packaging, transport, and conditions for travel and storage, including maximum holding time for samples prior to analysis

General Information on analytical methods, limits of detection, interferences

Once the sampling plan is drafted, it is worthwhile to have it reviewed by independent experts. This is especially important when assumptions have been made, or when all or part of the plan is based on judgment. For populations whose characteristics are little known, time and effort may be saved by collecting and analyzing a preliminary set of samples, using experience and intuition as a guide to make them as representative as possible. On the basis of this information, a more efficient and cost-effective plan can be prepared.

7

B. Kratochvil

Where feasible, it is useful to have the analyst perform or supervise the sampling operation. Otherwise he or she should, in addition to helping prepare the written protocol, ensure that the sample collectors are well trained and understand the importance of each step so that bias and contamination are minimized. The training should emphasize the importance of accurate sample labeling and logging, and of a chain of custody to ensure sample integrity from collection to measurement. For bulk materials, local homogeneity affects sample size. Increments should be large enough to not be biased with respect to the different sizes and types of particles present in the material. Where available sampling equipment precludes collection of larger increments, two or more smaller ones may be taken adjacent to each other. These may be composited or analyzed separately. (Separate analysis can provide information on the extent of local heterogeneity.) When sampling a material whose properties are unknown, a good approach is to collect a small number of exploratory samples, using experience and judgment to make them as representative as possible, and analyze them for the substance of interest. From this preliminary information, a more refined sampling plan can be developed.

1.5 1.5.1

STATISTICAL SAMPLING Introduction

Statistics provides a number of useful tools to assist in determining how many sample increments to take from a population, how large they should be, and from where they should be taken in order to hold the sampling uncertainty to some specified level with a given level of confidence. Most statistical sampling theory is based on the population having a normal (Gaussian) distribution, but other distributions, such as lognormal, do occur in nature. 1.5.2

Minimum number of increments

Unless a population is known to be homogeneous, a valid sampling plan requires collection of increments from multiple locations. Assuming, for the moment, negligible measurement uncertainty relative to that for sampling, Provost [5] describes the minimum number of increments, ns, needed to hold the sampling uncertainty, Es, to a given level of confidence by the relation: ns ¼ ðzss =Es Þ2

ð1:2Þ

where z is a stated level of confidence, say 95%. In most applications, ss is either known from past history of the population or can be estimated from measurements on a set of preliminary samples to obtain values of ss and X.

8


(Remember that if measurement uncertainties are not negligible relative to those of the sampling operations, then ss should be calculated by Eq. (1.1).) Since p m ¼ 2X ^ ðts= n 1:3Þ where t is obtained from statistical tables as an estimate of z from n measurements, the maximum acceptable sampling uncertainty, Es, can be defined by: p Es ¼ lm 2 Xl ¼ ts= ns ð1:4Þ Rearranging, ns ¼ ðtss =Es Þ2

ð1:5Þ

Initially, t can be set at 1.96 for 95% confidence limits and a first estimate of n can be calculated. The t-value for this n is then substituted and the system iterated to constant n. 1.5.3 Minimum size of increments in well-mixed particulate populations When sampling well-mixed populations of heterogeneous particles, as is often encountered in the subsampling of laboratory samples, Ingamells and Switzer [6] showed the relation: WR2 ¼ Ks

ð1:6Þ

to be applicable. Here W is the weight of sample analyzed, R is the relative standard deviation of sample composition in percent, and Ks is a constant equal to the weight of sample required to limit the sampling uncertainty to 1% relative with 68% confidence. In practice, Ks is determined by estimating ss from a series of samples of weight W. Once Ks is known, the minimum sample weight, W, required for any maximum relative standard deviation can be calculated. For poorly mixed or stratified materials, the calculated value of Ks increases as W increases. This provides a way of testing the homogeneity of the population. When sampling a mixture of particles, it is important to collect enough of each particle type to ensure representativeness. In some cases, where the element under test is present as only a small fraction of the particles (as in elemental gold or diamond deposits), quite large bulk samples must be taken, and particle size reduction and thorough mixing must be conducted before subsampling. For such populations the sampling standard deviation, s(g1), may be calculated using the Johnson equation [7]: n hX io1=2 fi ð2ri Þ3 ð1:7Þ sðg1 Þ ¼ ðpd1 g1 =6Þ

9

B. Kratochvil

where g1 is the mass and d1 is the density of the sample particles containing the trace component, fi is the fraction by mass of the trace element in particle size class i, and ri is the radius of particles containing the trace element. If the element of interest is present in each of a mixture of two types of particles but the fraction of one type is small, Zheng and Kratochvil [8] have shown that a combination of the Johnson equation with one developed by Bennedetti-Pichler [9] is applicable. Here the standard deviation, sP, expressed in percent, is given by: n hX i o1=2 sP ¼ ½ðP1 2 P2 Þ=g ðpd1 =6Þ fi ð2ri Þ3 g1 ð1:8Þ where P1 and P2 are the percentages of the trace element in each of the two types of particles in the mixture, g is the mass of sample, g1 is the mass of the fraction of type 1 particles, and d1 is the density of the type 1 sample particles. The remaining terms are as defined in Eq. (1.7). Equations (1.7) and (1.8) show that the sampling standard deviation varies as the square root of the sample mass and number of particles. This means that for every 10-fold decrease in the percentage of sought for substance, testportion size must increase 100-fold for a given level of sampling error and particle size. It is therefore especially important that laboratory samples for trace analysis are adequately ground and mixed prior to removal of test portions for trace analysis. The general approach described in this section has been extended by Gao and Kratochvil [10] to the calculation of sampling uncertainty for well-mixed materials containing more than two types of particles. 1.5.4

Sample increment size in segregated populations

Visman [11] demonstrated that for some segregated materials the variance of sampling could be expressed by:

s2s ¼ ðA=wnÞ þ ðB=n

1:9Þ

The constant A is related to Ingamells’ subsampling constant K and the average composition of the analyte, xav, by A ¼ 104xav. The constant B is related to the degree of segregation of the population. Values of A and B must be obtained experimentally from the bulk population. This can be done in two ways. In the first, two sets of sample increments are collected, one with w as small as, and the other as large as, feasible. The two sets are analyzed, the sampling variances calculated and substituted into Eq. (1.9) to give values for A and B. In the second, arising out of published discussions by Duncan and Visman [12], Visman proposed collection of a set of increment pairs, each pair of increments being of the same weight and taken from adjacent sites in the population. From the analytical data on the increments, an intraclass correlation coefficient, r, is calculated, either directly or by ANOVA [13]. Values for A and B are then

10


calculated from Eq. (1.9) and the relation r ¼ Bm=A; where m is the average particle mass. Increasing either W or N will reduce uncertainty due to random variability, but only increasing the number of increments, n, will reduce uncertainty due to segregation. All the sampling equations discussed in this section have been derived for normally distributed populations. As mentioned earlier, not all populations follow a Gaussian distribution. Procedures to test data for normality and for dealing with non-normality by data transformation or use of other procedures or distribution functions are available in the statistical literature. Problems may arise when small regions of a population contain analyte in much higher concentrations than elsewhere. This so-called “nugget” or “hot spot” effect is often encountered when sampling populations such as gold ores or contaminated industrial sites, but it can also be a factor in less obvious situations. An example is microanalytical investigation of surfaces using current sophisticated microtechniques. In situ analytical measurements on heterogeneous surfaces with a probe only a few micrometers in diameter may produce significant errors if areas of unusually high or low concentration are missed or oversampled. There is also the danger that an unusually high result from a hot spot may be rejected as an anomalous outlier. The sampling plan should take into account the possibility of encountering hot spots and their potential effect on the goals of the sampling program. 1.5.5

From where should increments be taken?

The variety of populations of analytical, and therefore sampling, interest encompasses every part of nature and human activity. To ensure that all parts of a population have an equal chance of being selected for analysis requires a random element in the sampling strategy (see Section 1.3.2). Several strategies have been proposed to meet this requirement. These include, in addition to simple random sampling, systematic grid sampling with a random initial start point or with random sampling within individual grid areas or volumes. To improve sampling efficiency, other sampling schemes, including stratified, cluster, and two-stage sampling, have been developed. In simple random sampling, the target population is divided on paper into a set of units and a defined number of the units are randomly selected for sampling. The units may be one dimensional, as a drill core or objects on a production line; two dimensional, as an agricultural field or a surface film coating on a manufactured product; or three dimensional, as a lake, railway tank car, or the atmosphere in an industrial plant. In systematic grid sampling, the population is divided into a two- or threedimensional grid and samples are collected from within each grid area or volume. Systematic sampling is often used to increase the probability of locating possible hot spots in a population. It has little inherent bias but may require more samples to be as effective as random sampling.

11

B. Kratochvil

In two-stage sampling, primary blocks or units are randomly selected within the population and two or more sample increments taken from locations within each unit. The locations may be selected systematically or randomly. Stratified random sampling involves division of the population into sections called strata. The number, size, and shape of strata are important to the design of an efficient and cost-effective sampling plan. If the goal is to estimate more precisely the average analyte concentration in the population, then each stratum should be as uniform in the elements of interest as possible. This reduces the number of sample increments needed to define analyte distribution within each stratum. If analyte distribution among separate strata is of interest, then the sampling plan may involve judgment as to size and location of the strata. In cluster sampling, a number of increments are collected from one or more small sections of the population. This method is used when specific sections have been identified, either through judgment or by previous sampling, to be likely to contain more of the substance of interest. 1.5.6

Model-based sampling

The sampling equations discussed in previous sections are all based on classical sampling theory, such as described by Cochran [14] and others. This approach, sometimes called design-based sampling, makes no assumptions about the population other than that it is fixed. Many sampling methodologies and statistical tools have been developed to handle various population distributions within this classical framework. A second approach, termed model-based sampling, employs one of several types of models to describe variability within a population. This methodology is most developed in the area of geostatistics. Borgman et al. [15] propose that, since the model-based approach views randomness as a property of a population, pure random sampling is no longer required and, in fact, may not be desirable because regularly spaced observations usually provide the best information about the degree of randomness present. A drawback is that the model must include information on expected patterns of variability within the population, though these patterns need not be completely understood to achieve reliable results. The biggest applications of model-based sampling have been for geostatistical estimations of underground ore reserves, but the method has also been applied to environmental studies [16]. A widely used form, called kriging, assumes a linear trend in concentration of the sought-for element. A sampling approach that includes elements of model design has been developed by Gy [17]. Although Gy employs classical random sampling statistics, he systematically considers all possible errors that might be encountered in the collection of a valid sample, including population variability, prior to sampling. In effect, Gy recommends incorporation of all uncertainties

12


that may affect representativeness of samples into the sampling design rather than assuming that randomness is the only source of variability. 1.5.7 Balancing economic factors and purpose of data collection against sample quality Sampling is often costly, especially in terms of time commitment by trained personnel. Therefore, the sampling plan should consider ways of minimizing the cost and variance of the sampling operation. Suppose a stratified sampling design is formulated consisting of n1 strata with n2 samples taken from each stratum and n3 analytical measurements on each sample. For strata equal in size and variance, the cost of determining a population mean to within a desired variance may be minimized as follows. The total cost of the operation, c, is equal to the sum of the cost of selecting the strata c1, sampling within the strata c2, and performing the analysis c3: c ¼ n1 c1 þ n1 n2 c2 þ n1 n2 n3 c3

ð1:10Þ

The overall variance for the population may be expressed as the sum of the variance contributions from the two stages of sampling and analyses: s2 ¼

s21 s22 s23 þ þ n1 n1 n2 n1 n2 n3

ð1:11Þ

Bennett and Franklin [18] show that to minimize the total cost for a preselected overall variance, the values of n1, n2, and n3 may be found from: qffiffiffiffiffiffiffiffi s21 =c1 qffiffiffiffiffiffi qffiffiffiffiffiffi qffiffiffiffiffiffi ð1:12Þ s21 c1 þ s22 c2 þ s23 c3 n1 ¼ s2 sffiffiffiffiffiffiffiffi s22 c1 ð1:13Þ n2 ¼ s21 c2 sffiffiffiffiffiffiffiffi s23 c2 ð1:14Þ n3 ¼ s22 c3 Note that the optimum allocation of sampling effort after the first stage is independent of the desired overall variance. This means that when the goal is reduction in overall variance at minimum cost, one should increase the number of strata sampled and hold the other steps constant. Similarly, for a fixed total cost; it was shown by Marcuse [19] that the optimum value for n1 is given by: qffiffiffiffiffiffiffiffi c s21 =c1 ð1:15Þ n1 ¼ qffiffiffiffiffiffi qffiffiffiffiffiffi qffiffiffiffiffiffi s21 c1 þ s22 c2 þ s23 c3

13

B. Kratochvil

while the optimum values for n2 and n3 continue to be given by Eqs. (1.13) and (1.14). Thus, the optimum allocation beyond the first stage is the same for fixed total cost as for fixed total variance. The same principles can be applied to any number of stages in a nested sampling design. If strata are not equal in size or in distribution of the analyte, appropriate weighting factors must be incorporated into these expressions.

1.6

1.6.1

SAMPLE HANDLING AND PRESERVATION DURING COLLECTION, TRANSPORT, AND STORAGE Handling and storage of samples

Samples may undergo a variety of chemical or physical changes during collection, transport, storage, and preparation for analysis. Changes may include loss of sample through volatilization, chemical reactions among components of the sample, or reaction of sample components with sampling tools, sample containers, or transfer lines. Other sources of change include reactions of sample components with external agents such as oxygen, carbon dioxide, or water in the atmosphere, or with sampling equipment or containers. Decomposition during transport or storage may occur as a result of high temperatures or microbial action. Errors from these sources can be minimized by protecting samples from exposure to external agents, and by reducing rates of reaction through addition of preservatives and/or maintaining samples at low temperatures. Preservatives reduce decomposition by altering pH, redox conditions, or solubility; by converting species of interest into more stable forms; by blanketing or coating samples to prevent reaction; or by acting as biocides. Care must be taken that preservatives do not interfere with subsequent analytical measurements. In fact, the best preservation method is storage at temperatures that are as low as possible. Most materials may be stored without change for years at liquid nitrogen temperature (2 1968C), though this method is costly and often difficult to implement. Since samples may begin to change from the time they are taken, analysis should ideally be done immediately after collection. Where the analysis involves digestion or extraction, consideration should be given to implementing this step promptly after collection, then storing the processed sample until measurement can be made. Procedures for sample collection, preservation, and storage are available from a variety of sources, such as the US Environmental Agency, for sampling of the environment, and the ASTM and ISO for industrial and commercial materials. An example of some of the recommendations provided by the US EPA for the evaluation of inland water and sediments is given in Table 1.2.

14

Suggested sample preservation and storage conditions for selected analyses of sediments and water [20] Analyte

Sample

In sediment Metals 100 g In water Metals

500 –2000 ml

Container

Preservation

Storage

Maximum holding time

Precleaned polyethylene jar

Refrigeration: dry ice or freezer for extended storage

#48C

Hg—28 days; others—6 months

pH , 2 with HNO3; refrigeration

28C

Zinc acetate, NaOH to pH . 9; refrigeration

48C

Hg—28 days; others—6 months 7 days

48C

28 days

Sulfide

250 ml

Acid-rinsed polyethylene or glass jar Plastic bottle

Fluoride

100 ml

Plastic bottle


TABLE 1.2

15

B. Kratochvil

1.6.2

Sampling equipment

A key component in any sampling operation is the quality of the apparatus used for the collection of sample increments and for splitting, grinding, or otherwise processing samples to obtain representative test portions for analysis. Koerner [21] discusses the selection and composition of equipment for environmental sampling. Good sampling equipment should be as simple as possible for ease of sample removal, maintenance, and use by different operators. It should protect the integrity of the sample by minimizing bias or loss of components during collection. The material of construction should be non-contaminating, durable, and readily cleaned. Plastics, especially Teflon or polyvinyl chlorides (PVCs), are often preferred over glass or metal for durability, ease of cleaning, and lower cost, but when strength and resistance to abrasion are needed, stainless steel is a good alternative. Several different stainless steel alloys are available. For example, type 316, which contains chromium, nickel, and molybdenum, has good resistance to corrosion and to sulfur compounds such as H2SO4. It will corrode with time when exposed to water containing iron-oxidizing microorganisms, however, especially at welds. Under these conditions, it is better to replace welds with threaded joints where possible. When sampling aqueous solutions for trace element determinations, sorption of analyte onto container walls must be considered. Parker et al. [22], in a study of the uptake of chromium, lead, and arsenic from groundwater by various materials, found that sorption was highest on types 304 and 316 stainless steel, followed by PVC. Teflon sorbed the least. 1.6.3

Sample containers

The purpose of a sample container is to protect the sample from interaction with its surroundings during transport and storage. Changes that may occur include loss of components to or contamination from the surroundings, as well as reaction with atmospheric components, especially oxygen, water, or carbon dioxide. Therefore, containers should have closures that seal completely, and do not introduce contamination into the sample. A study by Moody and Lindstrom [23] showed that, for most trace element analysis, containers of Teflon or linear polyethylene introduce the least contamination, but should be carefully cleaned with HCl and HNO3 before use. Of course, careful work requires that the purity of the acids and water used for cleaning and rinsing be considered too. Borosilicate glass is widely used for sample containers because it is relatively inexpensive, non-reactive to most organic compounds, and impermeable to gases. Though it sorbs a variety of molecules and ions, this is normally a problem only when the analyte is present in very low concentrations. Closures for borosilicate containers are usually screw caps with liners of Teflon or other inert plastic.

16


Most conventional plastics are sufficiently permeable to gases that they should not be used for the collection of gas samples or samples in which gas diffusion is undesirable. Fluorocarbons such as Teflon or Tedlar, however, have quite low permeability to gases. For this reason, and because of low sorption properties, they are often used as sample container materials when trace components are to be determined. 1.7

QUALITY ASSURANCE IN SAMPLING [24,25]

1.7.1

Overall objectives

The purpose of a quality assurance program in chemical measurements is to identify the nature and sources of errors in the overall analytical process, from sampling to data treatment, and to set up ways of assessing and minimizing those errors. The principal steps in reaching these goals are to: (1) assess the limits of error in the analytical data obtained from a sampled population; (2) reduce these errors to acceptable levels; (3) reduce the work required to obtain reliable data; and (4) provide, to the extent possible, a statistical basis for use of the data in decision-making. These objectives may be achieved through application of two concepts, quality control and quality assessment, which together comprise a quality assurance program. 1.7.2

Quality control

The goal of quality control is to attain a level of data quality that is adequate for the purpose, dependable, and economical. Quality control involves a system of testing and corrective actions that allows, through inspection, an estimate of the quality of the results. The system should specify whether changes are needed and, if they are, what measures should be taken to maintain a predetermined level of quality. Factors important to improved quality control in sampling include: † † † †

trained, knowledgeable samplers (who preferably have been involved in planning the sampling operations); a clear, complete, and detailed sampling protocol (including well-defined criteria for rejecting foreign material); clean, well-maintained, and appropriate sampling tools and sampling containers; a sample management system that protects sample quality and integrity from collection through analysis.

1.7.3

Quality assessment

Quality assessment is a system of activities designed to ensure that the quality control job is being done effectively. It involves continuing evaluation of

17

B. Kratochvil

the quality control program. For analytical measurements, this may be done through test samples, interlaboratory comparisons, control charts, and so on, but for sampling it is not so simple. A sampling program may require that multiple increments be collected from both adjacent and widely spaced sites across the population to ensure representativeness and provide backup in the event of sample loss. It may also require the use of field blanks and spiking of samples in the field to detect bias from sample contamination, loss, or alteration. Sample integrity must be maintained through appropriate use of preservatives, containers, storage conditions, labeling, and logging. A good quality assessment program could include the following activities on a scheduled basis: † † † †

collection and comparison of data on replicate samples; external audit of sampling procedures and their execution in the field, including appropriate safety precautions; review of sampling protocols, sample documentation procedures, and record keeping; thorough and objective feedback to all involved in sampling operations.

The overall aims of quality assurance in sampling are to provide a mechanism to reduce sampling errors to acceptable limits, the means to assure that the mechanism is operative, and the means to assure that the samples have a high probability of acceptable quality. Achieving these aims requires constant attention and maintenance, but with regular monitoring and review, a well-designed and implemented QA program can ensure quality of sampling operations indefinitely.

1.8

GLOSSARY

Bulk sampling: sampling of a population that does not consist of discrete, identifiable, constant units, but rather of arbitrary, irregular units. Bulk sample: (also called gross sample, lot sample) one or more increments of material taken from a population for analysis or record purposes. Composite sample: a sample composed of two or more increments collected from different locations within a population or from the same location more than one time. Grab sample: (also called discrete sample) a single increment collected from a population at a specific time and location. Homogeneity: the degree to which a property or substance is randomly distributed throughout a material. Homogeneity depends on the size of the units under consideration. Thus, a mixture of two minerals may be inhomogeneous at the molecular or atomic level, but homogeneous at the particulate level.

18


Hot spot: a localized part of the population in which the analyte is present in significantly higher concentration than elsewhere. Increment: a portion of material, collected by a single operation of a sampling device, from parts of a lot or population separated in time or space. Increments may be either analyzed individually or combined and tested as a composite sample. Individuals: conceivable constituent parts of the population. Laboratory sample: a sample, intended for testing or analysis, usually prepared from a bulk sample in one or more subsampling steps. The laboratory sample must retain the composition of the bulk sample. Reduction in particle size and mixing is typically necessary during its preparation. Lot: a quantity of units or bulk material of similar composition whose properties are under study. Population: a generic term denoting a collection of bulk material, individual items or events in the broadest concept; an aggregate determined by some property that distinguishes individuals that do and do not belong. Protocol: a detailed written description of the steps and procedures to be followed for the collection of valid samples. Reduction: the process of preparing one or more subsamples from a sample. Replicate samples: two or more samples collected from a population in an identical manner at the same time and place. Representative sample: a sample collected from a population in a manner that ensures, to the extent possible, that it accurately represents the population, or subset of the population, from which it was taken. Sample: a portion of a population or lot. It may consist of an individual or groups of individuals. Segment: a specifically demarked portion of a population, either actual or hypothetical. Spiked sample: a sample to which has been added a known quantity of the analyte to test the extent of interference by the matrix with the analytical measurement. Split sample: a sample divided into two or more representative parts for independent analysis. Strata: segments of a population that may vary with respect to the property under study. Subsample: a portion taken from a sample. A laboratory sample may be a subsample of a bulk sample; similarly, a test portion may be a subsample of a laboratory sample. Test portion: (also called specimen, test specimen, test unit, aliquot) that quantity of a material of proper size for measurement of the property of interest. Test portions may be taken from the bulk sample directly, but often preliminary operations such as mixing or further reduction in particle size are necessary.

19

B. Kratochvil

REFERENCES 1 2 3

4 5 6 7 8 9 10 11 12 13 14 15

16 17 18 19 20 21 22 23 24 25

20

L.H. Keith, in: L.H. Keith (Ed.), Principles of Environmental Sampling, 2nd ed., American Chemical Society, Washington, DC, 1996, p. xxvii. W.J. Youden, J. Assoc. Off. Anal. Chem., 50 (1967) 1007. F.C. Garner, M.A. Stapanian and L.R. Williams, in: L.H. Keith (Ed.), Principles of Environmental Sampling, 2nd ed., American Chemical Society, Washington, DC, 1996, p. 679. L.H. Keith, Environmental Sampling and Analysis, A Practical Guide. Lewis Publishers, Chelsea, MD, 1991, p. 14. L.P. Provost, Environmental Sampling for Hazardous Wastes, ACS Symposium Series 267, American Chemical Society, Washington, DC, 1984, p. 67. C.O. Ingamells and P. Switzer, Talanta, 20 (1973) 547; C.O. Ingamells and P. Switzer, Talanta, 23 (1974) 263. M.C.R. Johnson, Pharm. Acta Helv., 47 (1972) 546. L. Zheng and B. Kratochvil, Analyst, 121 (1996) 163. A. Benedetti-Pichler, in: W.M. Berl (Ed.), Physical Methods of Chemical Analysis, Vol. 3. Academic Press, New York, 1956, p. 183. Z. Gao and B. Kratochvil, Analyst, 126 (2001) 943 (see also p. 947). J. Visman, Mat. Res. Stds. 1969, November, pp. 8, 51, 62. J. Visman, A.J. Duncan and M. Lerner, Mat. Res. Stds., 11 (1971) 32; J. Visman, J. Mat., 7 (1972) 345. G.W. Snedecor and W.G. Cochran, Statistical Methods, 6th ed., Iowa State University Press, Ames, IA, 1967, pp. 294– 295. W.G. Cochran, Sampling Techniques. Wiley, New York, 1977. L.E. Borgman, J.W. Kern, R. Anderson-Sprecher and G.T. Flatman, in: L.H. Keith (Ed.), Principles of Environmental Sampling, 2nd ed., American Chemical Society, Washington, DC, 1996, p. 204. G.T. Flatman and A.A. Yfantis, in: L.H. Keith (Ed.), Principles of Environmental Sampling, 2nd ed., American Chemical Society, Washington, DC, 1996, p. 779. P.M. Gy, Sampling of Particulate Materials, Theory and Practice. Elsevier, New York, 1982. C.A. Bennett and N.L. Franklin, Statistical Analysis in Chemistry and the Chemical Industry. Wiley, New York, 1954, p. 490. S. Marcuse, Biometrics, 5 (1949) 189. “Required Containers, Preservation Techniques, and Holding Times”, Code of Federal Regulations, Title 40, Part 136, 1984, 49 FR 43260; Part 264, 40 CFR 264. C.E. Koerner, in: L.H. Keith (Ed.), Principles of Environmental Sampling, 2nd ed., American Chemical Society, Washington, DC, 1996, p. 155. L.V. Parker, A.D. Hewitt and T.E. Jenkins, Ground Water Monit. Rev., 10 (1990) 146. J.R. Moody and R. Lindstrom, Anal. Chem., 49 (1977) 2264. J.K. Taylor, Quality Assurance of Chemical Measurements. Lewis Publishers, Chelsea, MI, 1987. S.V. Kulkarni and M.J. Bertoni, in: L.H. Keith (Ed.), Principles of Environmental Sampling, 2nd ed., American Chemical Society, Washington, DC, 1996.


SELECTED BIBLIOGRAPHY G.E. Schweitzer and J.A. Santolucito (Eds.), Environmental Sampling for Hazardous Wastes, ACS Symposium Series 267, American Chemical Society, Washington, DC, 1984. A set of articles ranging from general aspects of sampling statistics and quality assurance to specific sampling problems in industry and in the environment. L.H. Keith (Ed.), Principles of Environmental Sampling, 2nd ed., American Chemical Society, Washington, DC, 1996. An excellent collection of chapters covering planning and sample design, statistical sampling, and quality assurance and quality control. Also included are chapters on specific elements to consider in sampling of air, water, biota, solids, and hazardous wastes. L.H. Keith, Environmental Sampling and Analysis, A Practical Guide. Lewis Publishers, Chelsea, MD, 1991. A guidance manual that focuses on those aspects of the sampling and analysis process required to produce data of a known quality. OSHA Technical Manual (TED 1-0.15A), U.S. Occupational Safety and Health Administration, U.S. Department of Labor, January 1999. Provides information relating to various aspects of sampling survey design and sampling practice. Section II of the manual provides guidance on sampling procedures. OSHA Chemical Sampling Information, U.S. Occupational Safety and Health Administration, U.S. Department of Labor. Contains a summary of sample collection parameters along with information on chemical properties and exposure limits. Available on-line and on CD-ROM. The on-line version is regularly updated. http://www.osha-sic.gov/SLTC/samplinganalysis/sampling.html. F.F. Pitard, Pierre Gy’s Sampling Theory and Sampling Practice, Heterogeneity and Sampling, Vol. I. CRC Press, Boca Raton, FL, 1989. J.K. Taylor, Quality Assurance of Chemical Measurements. Lewis Publishers, Chelsea, MI, 1987. A highly readable treatment of the basic concepts of quality assurance and how to plan and implement a quality assurance program for chemical analyses, including the sampling component.

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Chapter 2

Sources of analyte contamination and loss during the analytical process G. Knapp and P. Schramel

2.1

INTRODUCTION

Contamination of the sample with the analyte and/or losses of the analyte from the sample are the most important systematic errors that can occur during preparation steps such as sampling, storage and preparation of the sample, decomposition, separation and analyte preconcentration and the final determination of the elements. These problems are highlighted in many papers [1 –8]. Direct analytical procedures—such as neutron activation analysis (NAA) or X-ray fluorescence spectrometry (XRF)—minimise these systematic errors, but often cannot be applied for many reasons, but most frequently because these methods require suitable standard reference materials for calibration. Unfortunately, there is a lack of such materials at low concentration levels and for a whole variety of matrices. Therefore, in most cases, combined multistep procedures have to be applied, which can lead to a variety of possible systematic errors. However, the most important advantage is the ease of calibration associated with wet chemical procedures based on aqueous standard solutions, by which the problem of the lack of reliable standard reference materials is overcome. Sampling has been a largely neglected area in trace element research for a long time. The research was mainly focused on the development of trace analytical methods for increasing sensitivity and selectivity. During the last 20 years, the analysts recognised more and more that the majority of systematic errors might be introduced during the analytical steps at the beginning of a combined analytical procedure and not with the final measurement. A good analytical strategy also includes a sampling procedure free of contamination and losses, and proper stabilisation and storage of the samples. As analytical chemistry is a discipline that helps other disciplines to solve their problems, a close co-operation is necessary. In praxis, the analytical chemist is often not involved in the sampling procedures, the analyst is mostly Comprehensive Analytical Chemistry XLI Mester and Sturgeon (Eds.) q 2003 Elsevier B.V. All rights reserved

23

G. Knapp and P. Schramel

not even informed of the origin of the sample. Therefore, severe systematic errors in these first steps of an analytical procedure are the consequence. Such considerations have been discussed by Kratochvil in Chapter 1 of this volume. The influence of contamination and losses on the analytical results becomes increasingly important with decreasing concentrations of the analyte, today down to the mg/kg(l) or even to the ng/kg(l) range. These effects depend not only on the concentration range but also on the nature of the analyte. In fact, elements that occur ubiquitously in higher concentrations create more difficulties in low concentration analysis than the other ones (a typical example is Al in blood, serum or tissues). On the other hand, losses during the analytical procedure depend on the chemical nature of the analyte (volatile compounds, etc.). Accuracy can never be directly measured, meaning that systematic errors cannot be detected easily. One indication of the presence of systematic errors is an unstable blank value. This may result from contaminations or losses during different analytical steps. Only contamination by the reagents can be taken into account, because it is reproducible, at least for one lot of a reagent. This chapter deals with sources of systematic errors caused by contamination and losses of elements during the single steps of a combined analytical procedure and highlights techniques for reducing of the influence of these errors on the analytical results. One should keep in mind that contamination and losses can never be completely eliminated, but they must be reduced to an acceptably low extent. It must be pointed out that this contribution cannot be comprehensive, but will illustrate the ways to minimise these systematic errors for special analytical procedures. Most of the examples given for the single analytical steps are for biological and clinical samples, but the ideas behind how to reduce contamination and losses in trace element analysis can easily be transferred to other sample matrices.

2.2

CONTAMINATION

Sources of contamination include materials from which tools and vessels are made reagents and laboratory air. These are individually discussed below. 2.2.1

Materials

Materials of vessels and tools are of great importance in trace element analysis. Contaminants can be leached out of the materials or desorbed from impurities at the surface of vessels and tools. Table 2.1 shows preferred materials and their use in trace element analysis. Vitreous silica, especially artificial quartz, is available in high purity and is one of the best materials for vessels in trace element analysis. The main

24

Preferred materials and their use for trace element analysis

Storage containers Beakers and flasks Separatory vessels Vessels for wet digestion Vessels for bomb digestion Vessels for microwave digestion Crucibles Boats

PE and PP

FEP (#2008C)

þ þ þ

þ þ þ þ

PFA (#2508C)

TFM (#2508C)

PTFE (#2508C)

þ

þ þ

þ

þ þ þ

Vitreous silica (.5008C) þ þ þ þ þ þ þ

Glassy carbon (.5008C)

þ þ þ þ

Sources of analyte contamination and loss during the analytical process

TABLE 2.1

25


advantages include low risk of contamination, low wall adsorption due to a very smooth surface, temperature resistance up to 12008C and high resistance to most inorganic acids with exception of HF and concentrated phosphoric acid. Most microwave digestion devices have quartz vessels available. PTFE (Teflonw), PFA (Teflonw) and TFM (Hostaflonw) are fluorinated polymers that are preferably used for digestion vessels. The non-polar surface of these materials minimises adsorption of polar ions and, therefore, contamination and losses by adsorption and desorption are low. The maximum digestion temperature for such vessels is about 2508C. Classical Teflon bombs made for use with conductive heating normally allow lower temperatures than Teflon vessels used for microwave heating. The reason for this is the cooling of microwave-heated vessels from the outside. The advantage of these materials is their resistance against nearly all acids and they can therefore be used for sample digestion with HF. Contamination from the vessel material is not a problem as long as vessels from reputable companies are used. Otherwise it can happen that recycled PTFE is used for vessel production and enhanced contamination risk is the consequence. In general, PTFE is not as good as the other two materials because of its porous structure, which arises from the sintering process used for the vessel production. TFM is a chemically modified PTFE and does not suffer from the porous structure of PTFE. FEP is an excellent material for storage containers because of its dense and non-polar surface. Losses of polar ions via adsorption effects can mostly be neglected. It is also used for liners of digestion bombs, but must not be heated above 2008C. PE and PP are successfully used for storage containers, beakers and flasks, but they are not as good as FEP, although they are less expensive. Glassy carbon is used for high temperature digestion vessels. It is also resistant against most acids. Unfortunately, it is not resistant to oxidation. Therefore, it must be heated within an inert atmosphere. Additionally, oxidising reagents such as nitric acid significantly attack the surface at elevated temperatures. Another disadvantage is a comparatively high risk of contamination; glassy carbon is not as clean as quartz glass or fluorinated polymers. As already noted, one source of contamination by vessels and tools is the material itself. The other contamination source is impurities at the surface. To minimise or eliminate such impurities, proper cleaning procedures are necessary. Tables 2.2 and 2.3 summarise cleaning procedures used for polymers (PE and PP) and for Teflon [9]. The most efficient cleaning procedure for quartz vessels is steaming with acid vapour [2]. This method can also be applied to vessels and tools made of borosilicate glass, PTFE, TFM, PFA and glassy carbon. For purification, the vessels are continuously exposed to the hot vapour of the purifying liquid, frequently nitric acid. The principle of this apparatus is shown in Fig. 2.1.

26

Sources of analyte contamination and loss during the analytical process TABLE 2.2 Polymer cleaning procedure

Clean any residue from the polymer Rinse with DDI water Place in or fill with 1:4 HCl for at least 1 week Rinse with DDI water Place in or fill with 1:4 HNO3 for at least 1 week Rinse with DDI water Dry in clean air environment

A partly automated and very convenient device for steaming out vessels is produced by Milestone SLR, Sorisole, Italy. The suggested method for steaming vessels is given in Table 2.4. 2.2.2

Reagents

Significant sources of contamination are the reagents. Gaseous reagents can easily be cleaned, but there are not so many methods where gaseous reagents can be used. Solid reagents on the other hand are difficult to clean and result in comparatively high blanks. Liquid reagents are most important for trace analysis and they are commercially available in high purity grade (Suprapure grade reagents, Merck, Darmstadt, Germany; Optima grade reagents, Fisher Scientific; Double distilled grade reagents, Aldrich, Milwaukee, WI, USA; Ultrex II grade reagents, J.T. Baker Inc., Phillipsburgh, NJ, USA). The highest level of purification can be obtained by sub-boiling distillation by means of stills constructed of quartz or PTFE [10]. Figure 2.2 shows the schematic of a subboiling still. Sub-boiling distillation can be used to purify the following reagents: H2O, HCl, HNO3, HF, H2SO4, NH4OH and organic solvents such as alcohols, chloroform, ketones, etc. A simple and inexpensive version of a subboiling still apparatus is shown in Fig. 2.3 (Savillex Corp., Minnetonka, MN, USA). Table 2.5 summarises the residual impurities in water and different acids. TABLE 2.3 Teflon cleaning procedure

Clean any residue from the Teflon Rinse with DDI water Place in 1:1 HCl acid bath (80–908C) for at least 4 h Rinse with DDI water Place in 1:1 HNO3 acid bath (80–908C) for at least 4 h Rinse with DDI water Dry in clean air environment

27


Fig. 2.1. Scheme of steaming apparatus for vessel purification in acid vapour.

It should be noted that sub-boiling distillation ensures the separation of impurities of low vapour pressure such as metal ions, but it does not eliminate impurities having high vapour pressure such as organic compounds or some anions [10]. 2.2.3

Airborne particles

Contamination by laboratory air is a severe source of rising blank levels. There are three stages for reducing the influence of airborne particles on the TABLE 2.4 Procedure for steaming vessels

28

Steaming with HNO3 for at least 6 h (over night) Rinse with DDI water Dry in clean air environment


Fig. 2.2. Scheme of a sub-boiling distillation apparatus. 1: distillation chamber, 2: heating filament, 3: cooling device, 4: inlet funnel, 5: bottle with purified reagent, 6: reagent to be distilled.

analytical blank. The first and least expensive step is to run the particular analytical step in a closed system. Figure 2.4 shows the scheme of a simple but effective device for evaporation of solutions. The next level of clean working area is a laminar flow bench (Fig. 2.5). The most efficient but also most expensive equipment to reduce contamination with airborne particles is a clean room (Fig. 2.6). Table 2.6 summarises the particulate concentrations in laboratory air in an ordinary laboratory, a clean room and a clean hood [11,12]. 2.3

LOSSES

Losses of elements are caused by volatilisation, chemical reactions or by reactions with the material of vessels and tools and, finally, by adsorption. In general, volatilisation can be prevented by application of closed systems (evaporation, closed vessel digestion, etc.). When closed systems are not suitable, volatilisation can be reduced or prevented by reducing the temperature (storage, freeze-drying, low temperature ashing, etc.). Reaction with the material of vessels and tools can be minimised by proper selection of the material and again by reducing the temperature (e.g. comparison of dry ashing and low temperature ashing). Chemical reactions can lead to precipitation of the analyte but frequently the addition of stabilising reagents (e.g. oxidising or chelating reagents) can prevent such unwanted effects. As described earlier, adsorption and desorption effects lead to losses or contamination. This physico-chemical phenomenon cannot be prevented, as

29


Fig. 2.3. IR-heated Teflon sub-boiling still in class-100 clean air hood constructed from modular PFA segments. TABLE 2.5 Blank levels in water and different acids in p.a. and suprapure grade from Merck, and after sub-boiling distillation [2] Element concentration (mg/l)

H2O HCl HCl HCl HNO3 HNO3 HNO3 HF HF HF

30

10 M 10 M 12 M 15 M 15 M 15 M 54% 40% 54%

Sub-boiling Sub-boiling Suprapure p.a. Sub-boiling Suprapure p.a. Sub-boiling Suprapure p.a.

Cd

Cu

Fe

Al

Pb

Mg

Zn

0.01 0.01 0.03 0.1 0.001 0.06 0.1 0.01 0.01 0.06

0.04 0.07 0.2 1.0 0.25 3.0 2.0 0.5 0.1 2.0

0.32 0.6 11 100 0.2 14 25 1.2 3.0 100

,0.05 0.07 0.8 10 ,0.002 0.7 0.5 0.5 3.0 4.0

0.02 ,0.05 0.13 0.5 ,0.002 0.7 0.5 0.5 3.0 4.0

,0.02 0.2 0.5 14 0.15 1.5 22 1.5 2.0 3.0

,0.04 0.2 0.3 8.0 0.04 5.0 3.0 1.0 1.3 5.0


Fig. 2.4. Evaporation chamber.

there is always a dynamic equilibrium. However, it is possible to significantly reduce this effect to the extent that correct analytical results can be obtained. The lower the concentration range of the analysis, the more difficult it is to reduce adsorption/desorption to an acceptable value. The extent of adsorption/desorption processes can be reduced by application of the following steps: † † † † †

choice of proper vessel materials; treatment of the vessel material; area of the vessel surface; single vessel principle; equilibration of the vessel surface.

The characteristics of vessel materials have been discussed earlier. For storage and analysis of trace metal ions, FEP, PFA and TFM are ideal materials because of their non-polar surface. The situation changes rapidly when organic complexes are formed, e.g. for separation and preconcentration methods. These complexes may possibly be adsorbed to the non-polar surfaces to a significant extent. Artificial quartz is another excellent material for storage and analysis of metal ions, although the material is not as non-polar as the fluorinated

31


Fig. 2.5. Laminar flow bench.

polymers. Therefore, special treatment of the quartz surface is strongly recommended to deactivate some active sites at the surface and to reduce adsorption of metal ions. This happens during the steaming process with nitric acid [2]. Therefore, steaming of quartz vessels is essential because decontamination and deactivation of the quartz surface occur simultaneously. Another important rule in trace element analysis is to keep the surface of vessels as small as possible. In this manner, adsorption/desorption processes can also be reduced. Simply by taking a look at an analytical instrument, one can predict whether it is useful in trace analysis or not. Large vessels equipped with a large condenser, e.g., will lead to erroneous analytical results with high probability. In this same vein, there is another important rule, called the “single vessel principle”, which states: whenever possible keep the sample solution in one and the same vessel during all analytical steps. Each transfer to another vessel raises the probability of additional adsorption/desorption effects. As adsorption/desorption can never be completely eliminated, one can use dynamic equilibrium to keep the concentration of dilute solutions constant, at least for a certain time. This is important for calibration solutions having very low concentration levels. It can easily be observed, by repeated measurements

32


Fig. 2.6. Cross section of a clean room with a clean bench.

33

G. Knapp and P. Schramel TABLE 2.6 Element concentrations in laboratory air dust in an ordinary laboratory, in a clean room and in a clean hood Element concentration (mg/m3)

Ordinary laboratory Clean room Clean hood

Fe

Cu

Pb

Cd

0.2 0.001 0.0009

0.02 0.002 0.007

0.4 0.0002 0.0003

0.002 n.d. 0.0002

at intervals of several minutes, how quickly element concentrations can decrease when at the low mg/l level. Therefore, it is advisable to always use the same vessels for such highly diluted calibration solutions to maintain the dynamic equilibrium at that exact desired concentration level.

2.4

SAMPLING

Sources of contamination include all instruments and containers that come into contact with the samples, as they can cause contaminations to be introduced into the samples, either by mechanical abrasion or by leaching from the surfaces. Particularly in the case of tissues or body fluids, it is especially difficult to eliminate this kind of contamination when samples are taken from a living organism. Generally, it is not possible to intervene in routine clinical procedures where standardised equipment is used. Such possible contaminations of tissues and blood due to sampling have been investigated [13]. Elements such as Co, Cr, Cu, Fe, Mn and Ni, which are present in the material of surgical blades, biopsy needles and metal cannulas, can contribute to contamination. For Co, Mn and Ni, the errors are of the same order of magnitude as the element content in blood or serum. In the case of Cr, the analysis may be completely erroneous because contamination may be one order of magnitude higher than its content in blood or serum. Plastic containers, for example those used for sampling urine or water, may contain some plasticizers that can highly contaminate samples, especially when they have been acidified for stabilisation. A typical example is Cd. Each material used in the sampling procedure should be checked for a contamination risk under worst conditions, e.g. with dilute acid. In addition to contamination by vessels and tools, samples can also be contaminated by reagents, which have to be added as stabilisers, anticoagulants and preservatives [14 –19], especially because they are often added in excessive amounts.

34


A further type of contamination may occur by using the frequently applied “Vacutainer” in combination with a thin needle for taking blood samples. The vacuum inside the tube produces a high velocity of flow through the needle. This will lead to a partial destruction of the erythrocytes, which produces haemolysis and with that contaminations of the serum or plasma with some of the elements that are present in higher concentrations in the whole blood, especially Fe, Zn and many others [20–22]. Contamination of blood can be prevented using the so-called “Braunu¨len” for taking blood samples. They consist of a steel needle in a plastic tube for penetrating the skin, which can be removed. Blood is then only in contact with the plastic tube. After rejecting the first 5–10 ml of blood, one can obtain a blood sample nearly free from contamination. Because of the relatively large diameter (approximately 2 mm) of this sampling tool, these needles are not very popular among the patients. In general, for trace analysis, sampling tools made from materials other than stainless steel, such as titanium and ceramics for blades, or highly pure materials like nickel for needles should be used. In this case, there is only one element that cannot be analysed due to high blanks. Many other examples of such systematic errors can be found in Ref. [23]. Finally, errors may be the result of analytes being introduced into the sample via air dust or via the operating personnel, with particles of skin, perspiration, cosmetics, tobacco smoke, particles of clothing, washing powders, etc. being entrained in the material. These examples clearly demonstrate that adequate sampling is one of the most critical steps for an accurate analysis. Contamination sources arising from sampling procedures are most difficult to identify and often remain unnoticed for a long time. One means of discovering the source of contamination is the application of different, independent sampling procedures. As already stressed, one principle to reduce or avoid contamination during sampling is to use tools or storage vessels made of materials which do not contain the analyte elements or contain them in very low concentration ranges. However, one cannot completely exclude contamination and therefore the principle of “controlled contamination” has to be applied appropriately for the actual analytical problem. Systematic errors caused by element losses due to volatilisation, adsorption or precipitation generally do not occur during the sampling stage. Mercury can be lost by precipitation on metal tools or by volatilisation at high temperatures, e.g. during sampling by drilling of solid materials. 2.5

STORAGE

Liquid samples, such as water, body fluids, fruit juices, etc., are in a dynamic equilibrium state at the time of their collection. During storage, chemical, physical and/or microbiological processes affect the samples and can lead to

35


significant changes in elemental concentrations. The factors responsible for changing the element concentration include adsorption and desorption of elements at or from container walls, chemical interactions with the container material, precipitation and losses by volatilisation. Important points that have to be considered in this context are the type of the container material, container pretreatment, temperature and duration of storage and the addition of stabilising reagents [24,25]. Leaching surfaces with acid presents the worst case for possible contaminants, but cannot be used as “blank” because it does not really reflect the conditions of the sample. This permits only a raw estimation of the possible dangers of contamination. As noted earlier, a wide variety of sample container materials for bottles, flasks, tubes and vials can be used [24,26], the most common being polyfluorocarbons (PTFE and PFA) and other commercial products, polyethylene (PE), polypropylene (PP), polyvinyl chloride (PVC), polystyrene (PS), polycarbonate (PC), high purity quartz and borosilicate glasses. Synthetic polymeric materials can be recommended for achieving minimal contamination or losses. The purest materials were found to be conventional polyethylene, the various Teflonsw and polystyrene [27]. As a rule, cleaning procedures must be adopted to meet the standards required for trace element analytical work [27–32]. The procedures used should minimise the blanks but should not be too drastic so as to damage the surface of the container material and thus expose fresh and increased areas for chemical interactions, absorption and leaching [33]. Many cleaning procedures have been recommended [31], including use of different acids and their concentrations (mainly HNO3, HCl or HClO4), combinations of them but also non-ionic detergents and only distilled water. Again it depends on the problem at hand which method would be the most effective. Various papers [4,34 –42] describe observations on trace element exchanges between dilute aqueous solutions and their container materials. These results cannot be simply transposed to the biological field. Serum, for example, has a high content of proteins, which bind numerous elements. Other problems may arise when serum samples are to be stored. Prolonged storage of small samples in plastic containers may lead to losses of water by diffusion through the wall [43], or by sublimation in the case of an inadequate seal [44]. The best way to store biological material is to maintain it at temperatures around 2 208C until analysis. But again, all such recommendations are very dependent on the element and its species. Every problem requires its own investigation. Urine and water samples have to be acidified (about 2–5 v/v % in HNO3) before storage to avoid losses due to wall adsorptions. However, it is very important, especially for frozen body fluids, to thoroughly mix them (homogenise) after warming to room temperature in order to eliminate concentration gradients formed during the freezing process (Table 2.7) [45,46].

36

Sources of analyte contamination and loss during the analytical process TABLE 2.7 Concentration gradient for calcium, copper and sodium levels in serum, sweat and urine samples frozen and subsequently thawed without shaking [49] Position in tube

T M B C

Serum (meq/l)

Sweat (meq/l)

Urine (meq/l)

Ca

Cu

Na

Ca

Cu

Na

Ca

Na

0.25 1.10 3.60 2.20

17 90 127 89

26 126 270 142

0.09 0.17 0.41 0.31

63 143 364 250

11 23 71 42

4.6 9.8 15.9 10.7

69 152 204 152

Notes: T, top; M, middle; B, bottom; C, control (after shaking).

Drying has also often been used for stabilisation. There is no standardised method and all of these procedures are subject to losses of certain elements and must be again carefully studied for the particular element under consideration. Freeze-drying or lyophilization is the method considered least susceptible to element loss [47–50]. The risk of contaminations persists during the entire storage period. Thus, if samples cannot be analysed immediately, they should be kept in an adequately controlled environment or hermetically sealed in proper (plastic) material. To prevent element losses and/or transformation of the species, samples should be stored at low temperatures, sometimes at 2170 to 2 1908C (specimen banking). Losses of elements by volatilisation mainly occur at high temperatures. However, for very volatile elements such as Hg, As, Se, Sb and some others, this effect may be remarkably high even at room temperature [51,52]. As an example Hg may be lost within a few hours from an acidic solution [53]. Additionally, the use of plastic containers such as polyethylene or polypropylene cannot be recommended because Hg quickly penetrates such sample containers. Some general rules to prevent element losses or contamination during storage can be given. Samples should be stored at low temperatures in cleaned containers made of proper materials. Acidification to pH , 2 with ultra-pure acids is a useful method for preservation of aqueous samples. The optimal conditions for sample storage ultimately depend strongly on the sample material and the analytical problem and must be investigated for every new analytical task. 2.6

DRYING AND HOMOGENISATION

Three methods are commonly used for drying different sample materials—oven drying, microwave drying and freeze-drying. Oven drying of biological material

37


is performed at temperatures around 1008C. It is very important to control the temperature because the biological matrix may decompose, depending upon the nature of the sample [54]. This decomposition results in a loss of residual dry matter and intrinsically volatile elements such as Hg, Sb, Se and others, may be lost. In an experiment described in Ref. [55], it was shown that the loss of dry residue was most pronounced for urine (31%), compared to blood or other soft tissues, where it was found in the range between 4 and 7%. Drying at lower temperatures will reduce volatilisation losses but also exposes the sample to the ambient environment for longer periods of time, which serves to increase the risk of contamination. Microwave drying, a very fast drying procedure, exhibits the same problems as oven drying. An exact control of the microwave energy is necessary to prevent overheating of the sample and losses of some elements [56]. Freeze-drying is most commonly used for trace element analysis of biological materials. It is also known as lyophilization or vacuum drying. To avoid losses of volatile elements, especially Hg or Se, it is recommended that the sample be cooled during lyophilization to ,108C. Without cooling, the temperature of the sample will increase to room temperature after sublimation of the moisture and this may again lead to losses. Modern freeze-drying instruments have an additional device to cool the sampler holder. More details can be found in the literature (e.g. [57]). To avoid systematic errors during the drying process, it is recommended that the original moist sample material be analysed whenever possible and to correct the analytical result with the factor obtained by the separate determination of the dry mass. In this case, losses of volatile elements or contamination by dust during the drying process is inconsequential. The next analytical step, i.e. homogenisation of solid materials, easily leads to contamination and/or losses of elements. Homogenisation of samples is, in many cases, necessary to provide a representative sample. The methods and tools used for this step are very dependent on the sample material. Containers and tools can lead to contaminations and losses as in the other analytical steps. For soft tissues, grinding and milling in vibrating ball mills applying PTFE or PFA containers and PTFE coated balls (made from stainless steel or tungsten, etc.), eventually under cooling with water or liquid nitrogen, are the preferred means. For hard materials, such as bone, teeth, etc., other container materials like Zr, Ti or W and cooling under liquid nitrogen are necessary. The use of mixers with stainless steel blades should be avoided in trace element analysis. On the one hand, there is a potential danger of contaminations and, on the other hand, the warming up of the sample may result in losses of volatile elements or compounds. For these instruments other materials for the blades, such as Ti, W or special ceramic materials, and water-cooling of the containers are commercially available. As mentioned earlier, the strategy of “controlled contamination” can be used. In every case, dependent on the given analytical problem, it is necessary and important to investigate systematically

38


possible errors in this step. It may be difficult to verify, because there is nothing like a “blank”. The only way may be to analyse a piece of the sample before and after the homogenisation step. However, in this case, one has to keep in mind possible inhomogeneities in the original larger sample. All procedures that are commonly used in the production of CRMs cannot be used for trace element analysis in unknown samples. The final CRM always shows traces of contaminations caused by the different production steps. It is only necessary that contaminations for such a material are homogeneously distributed. Most importantly for a CRM, it is the matrix that does not change and corresponds to the original material, but not the original concentration of some trace elements. At this stage it should be pointed out that, for all the analytical steps mentioned before, the use of a CRM for quality control is not permitted. The reason is that a CRM has passed all these steps during the production cycle, meaning that volatile elements were lost during the sample preparation steps such as drying and homogenisation, and are no longer available in the CRM for quality control of these analytical steps. For all subsequent analytical steps the use of an adequate CRM is strongly recommended for quality assurance and for method development. 2.7

DILUTION, DISSOLUTION AND DIGESTION

Dilution is important for direct measurement of aqueous samples such as serum, plasma, urine, milk, etc., especially for serum and plasma as they have a high salt (approximately 0.95%) and protein content (approximately 7%) and a high viscosity. This may create difficulties when a nebuliser is used as a sample introduction device because of changing nebulisation and transport efficiency [58]. Much attention must be given to the trace element impurities of the dilution reagent. Water, acids [59–62], Triton X-100 [63–69] or TMAH [70,71] are mainly used. Contamination control by running blank determination of the diluents is strongly recommended. Dissolution and digestion of samples are other important steps of the combined analytical techniques, which are sources of contamination and losses. Table 2.8 presents a survey of currently used dissolution and digestion techniques used in trace element analysis [72–77]. Fusion techniques should be avoided for trace element analysis whenever possible. Solid reagents can never be purified to such a high level as liquid reagents via sub-boiling distillation. Contamination by dust is also possible but can be normally neglected in comparison with contamination by fusion reagents. Due to the high fusion temperatures, losses of volatile compounds and reaction with the vessel material can lead to low analytical results. Fusion is not a viable sample decomposition technique for ultra-trace analysis.

39

G. Knapp and P. Schramel TABLE 2.8 Decomposition methods for trace element analysis

Fusion Combustion + In open systems + In closed systems Wet decomposition + In open systems + In closed systems + In flow systems

The next family of sample decomposition techniques is combustion of organic materials with oxygen in open and closed systems. In principle, these techniques have the great advantage that gaseous reagents can easily be purified and no contamination from the reagent takes place. There is one exception—the dry ashing technique and unfortunately this technique is often still used in trace analytical laboratories despite its numerous sources of systematic errors. Continuous contamination by dust passing across the sample, together with the air necessary for oxidation, can occur. Another source of contamination is the muffle oven itself. Volatile element compounds are evaporated from the hot surfaces. On the other hand, there are many possibilities for losses via evaporation of volatile compounds, like the halogens. The extent of volatilisation depends not only on the combustion temperature but also to a high degree of the sample matrix. The conclusion of a consideration of all these sources of systematic errors is that it is possible to generate correct analytical results using dry ashing, but the probability of erroneous analytical data is high and in these days there are much better sample digestion methods available. Combustion in closed vessels, the so-called oxygen flasks, or in pressurised oxygen bombs is useful for the determination of volatile elements such as the halogens [78,79]. Contamination from the glass surface of the oxygen flask or the stainless steel surface of the combustion bomb can be a problem. A general adsorption and desorption of elements must be taken into consideration because the relation of surface area to sample weight is very unfavourable with the oxygen flask combustion. Only about 50 mg sample material can be combusted in a 500 ml flask. There are many other combustion techniques described in the literature [80], but they are not significant for trace element analysis, with one exception. The decomposition of organic samples in an oxygen plasma can be useful for ultra-trace analysis because of its extremely low contamination risk and practically no adsorption and desorption effects [81]. Only losses of volatile elements, such as Hg, As and Se, can be detected. Up to 2 g organic material can

40


be oxidised and the remaining ash dissolved in only 2 ml of diluted acid, preferably nitric acid. The most important family of sample digestion techniques is wet digestion. It is used for digestion in open vessels, in closed pressurised vessels and with automated flow systems. All of these techniques can be equipped with conventional conductive heating or with microwave heating. With regard to sources of systematic errors, one erroneous comment must be eliminated, namely the comment that microwave techniques help to reduce systematic errors and therefore improve analytical results. Microwave irradiation does not reduce losses of elements and microwaves do not prevent or reduce contamination. All these systematic errors depend only on the reagents and vessel materials used and on the digestion method (open, closed, temperature, etc.). The only parameter that is significantly influenced by microwaves is the reaction rate. Therefore, the digestion time is reduced dramatically, which is of great importance for routine analysis. Concerning systematic errors there is a big difference between the frequently used techniques of open and closed vessel wet digestion. Losses of elements, well known in open vessel wet digestion, cannot occur intrinsically with closed vessel digestion techniques when the vessel is tightly sealed. Only Hg can get lost when PTFE vessels are used. Mercury, in elemental form, diffuses into the porous structure of the PTFE surface. Ions are rejected from the non-polar surface. Elements can be volatilised in open vessel wet digestion as the element (Hg), as halogen compounds (As, B, Cr, Ge, Sn, Te, Ti, Zn, Zr), under oxidising conditions (Os, Pb, Rh, Ru) and under reducing conditions (Se, W). Contamination by the vessel material and by impurities of the surface can be minimised as described in Section 2.2.1. Vessel materials used in state-of the-art digestion methods include TFM and PFA as well as quartz glass. It must be kept in mind that the surface structure deteriorates with the number of digestion cycles and therefore adsorption and desorption effects increase [82]. By comparing open and closed vessel wet digestion in the context of contamination by reagents, it becomes obvious that closed vessel wet digestion is the method of choice for ultra-trace analysis. For the same amount of sample, much less digestion reagent is needed with closed vessel techniques. In addition, open vessel digestion usually requires sulphuric acid, which is disadvantageous for many measurement techniques.

2.8

SEPARATION AND PRECONCENTRATION

Analyte matrix separation and preconcentration should be avoided whenever possible, because these techniques are severe sources of losses and contamination. In principle, chemical separation techniques will be adopted for a number of different reasons: to concentrate analyte elements due to insufficient

41


detection limits of the final analytical method [83–85], to remove interfering species [78,86–88] or to eliminate matrix problems [89–99]. For the analysis of low-level trace elements, especially in human body fluids, these procedures must be rigidly controlled because of their extreme susceptibility to contamination problems. Reagents other than those discussed earlier, such as, e.g., ion exchange resins, organic solvents, etc., have to be applied, and are thus also potential contaminants. It must be remembered that current manufacturing processes for many of these materials and reagents are not yet capable of yielding products containing only mg/kg levels of trace element impurities [84,95–103]. The other problem that has to be under control is the recovery of analyte during the procedure. It is not necessary that it is 100%, but it has to be constant. Problems with varying recoveries can be overcome by application of ID-MS calibration techniques. 2.9

ELEMENT MEASUREMENT

Instrumental analysis of the sample solution may also introduce severe problems with contaminations and losses. In most cases, an auto sampler is used and the sample solution has to be transferred into special auto sampler cups made of different materials. This is an additional source of contamination and losses. Contamination by the vessel material due to the manufacturing processes and adsorption and desorption effects due to the properties of the material may occur. All the cleaning and conditioning procedures mentioned before for vessels must also be applied to auto sampler vessels. A further source of contamination in this context is dust from the laboratory air. The samples, carefully prepared under clean conditions, stay open sometimes for many hours in the auto sampler. The atmosphere of the laboratory is loaded with particulate matter from different sources, which contaminates the sample. If the whole analytical procedure, including the final measurement, cannot be done in a clean room, it is necessary to put at least the sample changer in a special clean box or in a laminar flow box or zone. In the case of graphite furnace AAS, it may be necessary to keep the whole instrument in such a device, at least for the analysis of elements such as Al, Mn and others having a high environmental background level. On the other hand, losses may occur due to an insufficient stabilisation of the sample solution. Elements such as Al, Cr or Sb need to be complexed with HF to avoid losses due to wall adsorption effects.

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Y. Pegon, Anal. Chim. Acta, 172 (1985) 147. P.A. Pleban and K.H. Pearson, Clin. Chem., 25 (1979) 1915. D.A. Thompson, Ann. Clin. Biochem., 17 (1980) 144. P. Schramel and S. Hasse, Mikrochim. Acta, 116 (1994) 205. G. Knapp, B. Maichin, P. Fecher, S. Hasse and P. Schramel, Fresenius J. Anal. Chem., 362 (1998) 508. R. Bock, Handbuch der Analytisch-Chemischen Aufschlussmethoden. Wiley-VCH Verlag GmbH, Weinheim, 2001. T.T. Gorsuch, The Destruction of Organic Matter. Pergamon Press, Oxford, 1970. J. Dolezal, P. Povondra and Z. Sulcek, Decomposition Techniques in Inorganic Analysis. Elsevier, New York, 1968. H.M. Kingston and S.J. Haswell, Am. Chem. Soc., Washington, DC, 1997. G. Knapp, Mikrochim. Acta, (1991) 445. G. Knapp, Anal. Proc., 27 (1990) 112. M. Reverchon, Chim. Anal. Paris, 44 (1962) 340. A.L. Conrad, Mikrochemie, 38 (1951) 514. R. Bock, A Handbook of Decomposition Methods in Analytical Chemistry. International Textbook Comp. Ltd., London, 1979. R.T. White and C.W. Lawrence, J. AOAC Int., 78 (1995) 99. H.M. Ortner, S. Sterkel, G. Knapp, B. Maichin, P. Kettisch, L. Kocsis, J. Mihaly and J. Mink, Mikrochim. Acta, 137 (2001) 229. E.M. Bem, Environ. Health Perspect., 37 (1981) 183. M. Alt and H. Massman, Z. Anal. Chem., 279 (1976) 100. H.T. Delves, G. Shepherd and P. Vinter, Analyst, 96 (1971) 260. J. Kumpulainen, A.M. Raittila, J. Letho and P. Koivistoinen, J. Assoc. Off. Anal. Chem., 66 (1983) 1129. K. Heydorn and H.R. Lukens, Danish Atomic Energy Commission, Research Establishment Risø, Roskilde, (1966). H. Bem, J. Holzbecher and D.E. Ryan, Anal. Chim. Acta, 152 (1983) 247. R.O. Allen and E. Steinnes, Anal. Chem., 50 (1978) 1553. I. Andersen and A.C. Høgetveit, Fresenius Z. Anal. Chem., 318 (1984) 41. I. Andersen, W. Torjussen and H. Zachariasen, Clin. Chem., 24 (1978) 1198. R.A. Barfoot and J.G. Pritchard, Analyst, 105 (1980) 551. D.J. Halls, G.S. Fell and P.M. Dunbar, Clin. Chim. Acta, 114 (1981) 21. R. Heinrich and J. Angerer, Fresenius Z. Anal. Chem., 315 (1983) 528. E. Kamata, R. Nakashima, K. Goto, M. Furakawa and S. Shibata, Anal. Chim. Acta, 144 (1982) 197. N. Lekehal and M. Hanocq, Anal. Chim. Acta, 83 (1976) 93. Ne`ve, M. Hanocq and L. Molle, J. Pharm. Belg., 35 (1980) 345. M. Suzuki, K. Hoyashi and W.E.C. Wacker, Anal. Chim. Acta, 104 (1979) 389. M. Suzuki and W.E.C. Wacker, Anal. Biochem., 57 (1974) 605. R. Cornelis, J. Versieck, L. Mees, J. Hoste and F. Barbier, J. Radioanal. Chem., 55 (1980) 35. H.T. Delves, Clin. Chim. Acta, 71 (1976) 495. P.E. Gardiner, J.M. Ottaway, G.S. Fell and R.R. Burns, Anal. Chim. Acta, 124 (1981) 281. R. Pietra, E. Sabbioni, A. Springer and L. Ubertalli, J. Radioanal. Chem., 69 (1982) 365.

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Chapter 3

Calibration approaches for trace element determination Douglas C. Baxter and Ilia Rodushkin

3.1

INTRODUCTION

Perhaps 95% of all errors in analytical measurements occur during sample preparation. Avoiding or accounting for these errors is a major theme of this chapter. If it is assumed that the samples have been correctly prepared, then it becomes necessary to ensure that appropriate steps are taken to avoid the remaining sources of errors that are most likely to arise during calibration. Although the primary goal of calibration is to facilitate accurate calculation of analyte concentrations or absolute amounts in the samples, it is no less important to be able to provide a meaningful assessment of the associated uncertainties. The latter allows statistical evaluation of the data, for example, to test whether the measured quantity meets product specifications or conforms to legislative limits [1]. Note that the general use of the term analyte concentration in the following does not exclude the possibility that the absolute amount of analyte may actually be the quantity of interest. In this respect, the two terms can be considered interchangeable in most contexts below. The quantity to be derived, from calibration of the instrument and subsequent analysis of a test portion of the sample material, is more generally denoted as the measurand. This chapter will first introduce the basic assumptions and terminology (Section 3.2) used in the context of calibration. Next, some criteria for selecting the calibration approach are presented in Section 3.3, one of these being recovery, the statistical evaluation of which is summarized in Section 3.4. This is followed by a description of linear regression techniques (Section 3.5), which provide the analytical chemist with some of the most important, yet poorly understood, tools of the trade. Their application to external calibration (Section 3.6), the method of standard additions (Section 3.7) and internal standardization (Section 3.8) is then detailed. For each of these calibration approaches, as well as isotope dilution (Section 3.9), the mathematical models for calculating analyte concentrations and estimating uncertainties are derived, and strategies for optimizing data quality are provided. In the cases of external Comprehensive Analytical Chemistry XLI Mester and Sturgeon (Eds.) q 2003 Elsevier B.V. All rights reserved

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Douglas C. Baxter and Ilia Rodushkin

calibration and standard additions, regression-free alternatives are also included, which may be used when the response of the instrument varies temporally, or with some manipulation, when the number of standards used is limited, e.g. for single point calibration.

3.2

BASIC ASSUMPTIONS AND SOME TERMINOLOGY

It must be emphasized that all calibration approaches are based on certain assumptions, and here it is surmised that there exists a first-order, linear relationship between the measured response and the analyte concentration, as illustrated in Fig. 3.1. This may require suitable transformation of one of the variables, e.g., taking the logarithms of the analyte concentrations or activities when employing ion-selective electrodes [2] or the square root of the response when detecting sulfur as excited dimers with a conventional flame photometric detector [3]. Transformations will not be considered further, but an example of linearization is included in Section 3.9, i.e. correction for detector dead time effects. Perhaps, the most important assumption concerns the selectivity of the measurement. Selectivity may be defined as the extent to which the method can be used to determine a particular species in a mixture or a matrix, without spectral (additive) interferences from other components [4]. The occurrence of spectral interferences implies that the method is not completely selective, and thus the detector will respond to one or more concomitant species, as well as the

Fig. 3.1. Idealized calibration curves in the absence (circles, solid line) and presence of spectral (triangles, dotted line) or non-spectral (squares, dashed line) interferents. Note that spectral interferences always cause a positive bias although over-correction for this effect will result in a negative offset. In either case, additive interferences are manifested by a change in the intercept of the calibration curve. A depressive non-spectral interference is illustrated here, but enhancement effects can also be observed. Such multiplicative interferences change the slope of the calibration curve.

48

Calibration approaches for trace element determination

analyte. For a constant spectral interferent concentration, the result will be a positive offset in the calibration curve, as depicted by the dotted line in Fig. 3.1. Non-spectral (multiplicative) interferences, on the other hand, cause changes in the sensitivity, i.e. variations in the response per unit analyte concentration in the presence of matrix components. The dashed line in Fig. 3.1 exemplifies a depressive, non-spectral interference. The terms non-spectral and spectral interferences used here are in accordance with the classification ramified by the IUPAC Commission on Spectrochemical and Other Optical Procedures for Analysis in 1978 [5]. In the following, it is implicitly assumed that spectral interferences are absent or have been appropriately dealt with. It must be emphasized that the calibration techniques considered here cannot directly correct for spectral interferences. However, appropriate selection of the standardization approach can eliminate errors originating from non-spectral interferences. The term “errors” will only be utilized to describe situations where the measured quantity systematically deviates from the true value. Random errors will be exclusively denoted as uncertainties, to avoid ambiguities and to conform to current recommendations on terminology [5 –8]. Two further important terms are accuracy and precision that, despite having been defined in IUPAC nomenclature recommendations in 1976 [9], have been frequently misused and confused in the literature. Accuracy refers to the closeness between a measured and expected, consensus or true value. Precision, on the other hand, describes how well repeated measurements of the same quantity in the same sample agree with each other. The major goals of calibration in analytical chemistry are thus to generate results that are both as accurate and as precise as possible.

3.3

SELECTION OF THE CALIBRATION APPROACH

Figure 3.2 summarizes the factors that must be considered in order to make the correct choice of calibration technique. The first question to answer concerns whether or not the instrumental response is stable as a function of time, the two possible outcomes leading to different branches of the family of calibration techniques. If the short-term stability of the instrumental response is poor, then it will be necessary to alternate measurements of unknowns and calibrants at regular intervals to facilitate correction for drifting sensitivity. During analytical method development or validation, work will be required to determine whether interference effects are present. This may be achieved by recovery studies, using a range of matrices and analyte concentrations for which the method will be applied [10]. The recovery is the proportion of the amount of analyte, either initially present in or added to the analytical portion of the test material, which is actually measured. Losses of analyte or contamination during the process of sample preparation will obviously cause the recovery to deviate from 100%. (Note that contamination can be considered

49


Fig. 3.2. Flowchart for the selection of a suitable calibration approach. The question of whether an internal standard can be used depends on whether the instrument is capable of determining several different species or elements in a single run. If it is possible to perform isotope-specific measurements, then isotope dilution is a viable calibration option, favored for its potential to provide very accurate and precise results.

50


as a form of spectral interference [11], requiring additional measurements for its correction, as discussed in Chapter 2.) Further complication arises because concomitant species may depress or enhance the response of the measurement system. Thus, recovery experiments should be designed to facilitate identification of the stage at which errors are introduced. Further discussion of recovery is deferred to Section 3.4, although it must be emphasized that this is one of the most important factors in calibration approach selection. If the sensitivity is constant and the recoveries are quantitative, external calibration is the obvious choice. External calibration, also referred to as the calibration curve technique, is the most widely employed approach. The popularity of external calibration is due to the fact that, once calibrated, the measurement system can be used to analyze as many samples as required, at least in principle. In practice, re-calibration may be necessary at appropriate intervals, depending on the temporal stability of the instrumental response. Thus, a crucial prerequisite is that the sensitivity is constant for the duration of the instrument calibration and the analysis of the unknowns. A second prerequisite for employing external calibration is the presumption of a complete lack of interferences; in other words, the sensitivity must be independent of the matrix composition. To ameliorate the stringency of this requirement, some form of matrix matching may be adopted. In certain situations, this may be as straightforward as preparing the unknown test portions and calibrants in the same solvent to ensure a constant sample introduction rate, e.g. for analytical techniques utilizing solution nebulization, such as ICP-OES, ICP-MS or flame AAS [11 – 13]. For potentiometric measurements, the so-called total ionic strength-adjusting buffer is added to sample test portions and standard solutions to fix both the ionic strength and the pH [2]. Whatever the approach chosen, the constancy of the sensitivity can, and should, be confirmed by preliminary method validation work, such as using recovery studies [10]. If recoveries are inadequate, however, then employing internal standardization should be contemplated. Internal standardization provides a means to correct for drift in the sensitivity of the instrument and for non-spectral interferences [14–18]. Instead of using the analyte itself as the calibrant, a chemically distinct species is employed. The internal standard may be a different chemical form (species) of the analyte or another element altogether. In the former case, detection must be preceded by on line separation of the analyte into its native and added forms (e.g. by chromatography, electrophoresis, etc.); in the latter case, the instrument should be capable of detecting both the analyte and the internal standard element simultaneously (or at least in rapid sequence, see Chapter 5). In either case, the internal standard is added at a predetermined concentration, although in favorable circumstances, a species present in all unknowns at a known and preferably constant level may prove suitable. Isotope dilution is considered to represent the ideal variant of internal standardization and involves spiking the unknown with isotopically enriched

51


analyte [19–24]. As for internal standardization, it is necessary to be able to measure two components in each unknown, i.e. the enriched isotope of the spike and an additional, naturally occurring isotope of the analyte. This requirement obviously dictates the use of analytical instrumentation facilitating isotope specific measurements. Mass spectrometry is generally the technique of choice, allowing stable isotopes to be employed, and will be treated exclusively here. Like internal standardization, isotope dilution will correct for both drifting sensitivity and non-spectral interferences. The advantage is that the same element is used, avoiding the difficulties encountered with internal standardization of finding chemically distinct species that behave identically with respect to non-spectral interferences [14,16]. Nevertheless, it should be remembered that isotopes of the same element differ with respect to bond energy [25], vapor pressures [26], etc. This is particularly important for lighter elements, which display notable mass effects in their physical and chemical characteristics. However, such effects are of negligible consequence for most applications of isotope dilution. When the sensitivity is constant but neither internal standardization nor isotope dilution is a viable option, the method of standard additions may be adopted. As the name implies, known amounts of standard are added to portions of the sample to derive a calibration function specific to that sample. The fact that calibration is performed individually on each unknown is both a blessing and a curse. Variable degrees of non-spectral interferences are admirably dealt with, but at the cost of expending much more valuable instrument time per sample. For this reason, applying the method of standard additions is often seen as a last resort. Note that, although correction for recovery is mentioned in two of the calibration approaches included in Fig. 3.2, this operation is not always specified in descriptions of analytical methods. On the one hand, the accuracy of the reported result is of prime concern in analytical chemistry. On the other hand, some widely employed analytical methods do not provide complete recoveries. If results reported by some, but not all, laboratories were corrected for recovery, the comparability of the data would be compromised and could lead to disputes. Thus, it is important to provide complete documentation of how the results were actually computed [10].

3.4

STATISTICAL EVALUATION OF RECOVERY DATA

Fortunately, the question as to whether the sensitivity is dependent on the matrix can be easily resolved, without controversy, by simple spiking experiments. The recovery, R; is calculated from the difference in the instrumental response to the analytical portion, before ðInative Þ and after ðIobs Þ spiking, and the signal obtained for a calibrant containing the same analyte

52


concentration as that added to the spiked material ðIspike Þ: R ¼ ðIobs 2 Inative Þ=Ispike

ð3:1Þ

Ideally, the analytical method will provide recoveries of 100% (i.e. R ¼ 1) for all tested matrices and analyte concentrations. In reality, this is an unlikely situation, due to either the presence of interferences or random variations in the measured responses. This raises the question as to which of these two possibilities actually explain the deviations. To answer this question, some basic statistical concepts will be introduced at this juncture. Here, the discussion will be confined to estimating the uncertainty in the example of Eq. (3.1). The approach can readily be extended to consider all situations of interest. It should be mentioned that a detailed account of evaluating uncertainties associated with recovery experiments has been provided by Barwick and Ellison [27]. If the measurements included in Eq. (3.1) are repeated, such that the uncertainties in the signals can be estimated, it is possible to determine whether the recovery is statistically significantly different from unity, i.e. 100%. This requires calculation of the combined standard uncertainty, uc ðRÞ; of the recovery value. First, the uncertainties must be derived for each of the variables, these being termed standard uncertainties if expressed as standard deviations for q replicates: "

q X ðIx;i 2 Ix Þ2 uðIx Þ ¼ q i¼1

#0:5 ,

where the mean is Ix ¼

q X Ix;i q i¼1

ð3:2Þ

Equation (3.2) assumes that the measurements are normally distributed about the mean value, Ix ; which is true for most experimental data providing that q $ 4 [28]. For estimating standard uncertainties for variables following other distributions, reference can be made to a variety of sources [6 –8]. Armed with standard uncertainties for the three variables in Eq. (3.1), the rules for the propagation of random uncertainties can be applied [6 –8,29 –31], enabling an expression for uc ðRÞ to be formulated. This universally applicable approach involves evaluating the partial derivatives of the computed quantity, R in this case, with respect to each of the variables: !2 2 ›R 2 2 ›R ›R 2 uc ðRÞ ¼ u ðIobs Þ þu ðInative Þ þu ðIspike Þ ›Iobs ›Inative ›Ispike 2

0:5

ð3:3Þ

Note that the partial derivative terms may also be denoted sensitivity coefficients, being measures of the extent to which individual uncertainties are amplified by the relationships of the variables to the calculated

53


quantity [8,23]. Evaluating the partial derivatives yields: 1

2

uc ðRÞ ¼ u ðIobs Þ

Ispike

!2 2

þu ðInative Þ 2

1 Ispike

!2

I 2I þu ðIspike Þ 2 obs 2 native Ispike

!2

0:5

2

ð3:4Þ The penultimate stage in the process is to calculate the expanded uncertainty, U; by applying an appropriate multiplier to uc ðRÞ: This multiplier is termed the coverage factor, k; with k ¼ 2 being generally recommended as this provides a confidence level of approximately 95%. However, should uc ðRÞ be associated with less than six effective degrees of freedom, n, then k should be equated with the corresponding two-tailed value of Student’s t-statistic at the desired confidence level (generally 95%). One degree of freedom is lost for each parameter, p; calculated from the data set. For example, when the mean was calculated in Eq. (3.2), one degree of freedom was lost, n ¼ ðq 2 pÞ ¼ ðq 2 1Þ: The partial derivative terms are also instrumental in approximating the effective degrees of freedom for the combined standard uncertainty, using Satterthwaite’s formula [28,32]. Again using Eq. (3.1) as an example, nðRÞ is calculated as: 8 2 2 32 2 3 2 > ›R 2 ›R > 2 2 > u ðI Þ u ðI Þ obs native < 1 6 1 6 ›Iobs 7 ›Inative 7 7 7 6 6 nðRÞ ¼ 7 þ 7 6 6 2 2 5 5 4 4 > nIobs nInative u ðRÞ u ðRÞ > c c > : 2

›R 6 u ðIspike Þ ›Ispike 1 6 6 þ 6 nIspike 6 u2c ðRÞ 4 2

21 !2 32 9 > > > > 7 > 7 = 7 7 7 > 5 > > > > ;

ð3:5Þ

the result being rounded down to the nearest integer. Summarizing the aforementioned considerations, the recovery and its expanded uncertainty can be expressed as: R ^ U ¼ R ^ kuc ðR

3:6Þ

or for the more general case: x ^ U ¼ x ^ kuc ðx

3:7Þ

where k ¼ 2 for an approximately 95% confidence level and n . 6; or k ¼ again assuming a 95% confidence level. If the inequality ðR 2 UÞ , R , ðR þ UÞ encompasses the value of one, then it may finally be concluded that the recovery is not statistically different from 100%. Thus, any deviations from 100% recovery in the data set can be

tna¼0:05,

54


attributed to random variations rather than to non-spectral interferences. If, on the other hand, this inequality does not contain the value of 1, then the possibility that the instrumental response is subject to non-spectral interferences cannot be excluded and therefore it can be concluded that the matrix alters the sensitivity. Although this might appear discouraging, it will certainly provide a rational basis on which to select the means to calibrate. However, before embarking on any further exposition of calibration, the most relevant variants of linear regression analysis will be introduced, as these constitute the foundation for several of the approaches to be discussed. It is important to note that, in the following, the term standard deviation of the mean will be required, a quantity that is often referred to as the standard error or the standard error of the mean [8] and is symbolized here by sðxÞ: If a quantity is determined repeatedly such that there are m estimates of the mean and its standard uncertainty, x ^ uðxÞ; then: " #0:5 m m X X ðxi 2 x Þ2 x i sðxÞ ¼ where x ¼ ð3:8Þ m m i¼1 i¼1 and x is the mean of means. There is an exact relationship between the standard deviation of the mean and the standard uncertainty, i.e., uðxÞ sðxÞ ¼ pffiffiffi m

ð3:9Þ

showing that, as m increases, the spread of the means decreases. Note that calculation of the expanded uncertainty in x is given by: x ^ U ¼ x ^ ksðx

3:10Þ

subject to the same conditions as those noted directly following Eq. (3.7), pffiffiffi despite the inclusion of the additional divisor m [8]. 3.5

LINEAR REGRESSION

Before embarking on our discussion of calibration approaches, we will attempt to review the linear regression techniques that may be applied to situations of the type illustrated in Fig. 3.1. For fitting straight lines to experimental data, ordinary (or unweighted) linear regression (OLR), as described in Section 3.5.1, is undoubtedly the most popular statistical technique. This is hardly surprising, given that it is the only form of regression analysis included in general analytical textbooks. Application of OLR requires that several assumptions are fulfilled, the two most important being that (a) the variation in measured response is constant, irrespective of concentration and (b) the concentrations of analyte in the calibrants are known exactly, with negligible error. The former prerequisite can be readily assessed by examining a residuals plot (see below and Fig. 3.3), which necessitates replicate measurements at each

55


Fig. 3.3. Illustration of the application of OLR to measured data. The fitted line in (a) has the equation Iî ¼ ð0:010 ^ 0:026Þ þ ð0:920 ^ 0:043ÞCi with r ¼ 0:9993: Uncertainties are 95% confidence limits. A residuals plot is shown in (b), clearly indicating curvature that cannot be modeled by a first-order linear regression equation.

concentration [33]. If condition (a) is not met, then weighted linear regression should be used (Section 3.5.2). Linear regression for situations where there are uncertainties along both variable axes is considered in Section 3.5.3. 3.5.1

Ordinary linear regression

The estimation procedure for computing the OLR equation of the line relating measured responses, I, to concentrations, C; Ii ¼ ao þ bo Ci þ 1i

ð3:11Þ

is that of least squares [34]. Here the subscript “o” is used to indicate that OLR is being used to estimate the parameters describing the relationship between I and C: Obviously, in a calibration experiment, the magnitude of the response is dependent on the concentration. Therefore, the terms dependent and independent variables can be applied to I and C; respectively. In Eq. (3.11), ao and bo are the regression coefficients obtained by minimizing the sum of squares (SS) of the deviations ð1i Þ from the line, i.e., SSo ¼

n X i¼1

12i ¼

n X

ðIi 2 ao 2 bo Ci Þ2

ð3:12Þ

i¼1

These deviations, reflecting the distances of the measured data points from the fitted equation at Ci ; are usually referred to as residuals. The regression coefficients, ao and bo ; represent the intercept of the line with the y-axis (with the same units as the response) and the slope (units of response per unit analyte concentration, which equates with the sensitivity), respectively. Differentiating Eq. (3.12) with respect to ao and then to bo ; and setting

56


the results equal to zero, yields: X X Ii 2 nao 2 bo Ci ¼ 0

ð3:13aÞ

X X X Ci Ii 2 ao Ci 2 bo C2i ¼ 0

ð3:13bÞ

where all summations are from i ¼ 1 to n: These are called the normal equations, with the solution: ao ¼ I 2 bo C P P P P Ci Ii 2 Ci Ii =n ðCi 2 C ÞðIi 2 IÞ ¼ bo ¼ P P 2 P 2 2 ðCi 2 CÞ Ci 2 Ci =n

ð3:14aÞ ð3:14bÞ

Additional statistics are needed for the purposes of assessing the quality of the fitted regression line and estimating the uncertainty in the determined analyte concentration. The most important of these is actually that given by Eq. (3.12), divided by n ¼ n 2 2: P P ðIi 2 ao 2 bo Ci Þ2 ðIi 2 Iî Þ2 S2o ¼ ¼ ð3:15Þ n22 n22 where two degrees of freedom have been lost through calculation of two regression coefficients, ao and bo ; from the data. S2o is called the mean square about regression, elsewhere symbolized in various other ways, such as s2yx [33] or s2y=x [35], and S the residual standard deviation. Iî denotes the predicted or fitted response calculated from ao ; bo and Ci : Knowing S2o ; the standard deviations of the mean values of the estimated slope and intercept, as well as the covariance between ao and bo ; can be calculated, i.e., " #0:5 P 2 Ci sðao Þ ¼ So ð3:16aÞ P 2 n ðCi 2 CÞ " #0:5 S2o sðbo Þ ¼ P ð3:16bÞ ðCi 2 C Þ2 S2 C ð3:16cÞ covðao ; bo Þ ¼ 2 P o 2 ðCi 2 CÞ It is worth noting that the standard deviations of the mean values of the intercept and slope are equivalent to their standard uncertainties [33]. Frequently, the correlation coefficient, r, is used as a measure of the quality of the regression: "P P #0:5 ðCi 2 C ÞðIi 2 IÞ ðCi 2 C Þ2 r ¼ P ¼ bo P ð3:17Þ P 2 2 0:5 ðIi 2 IÞ ðC 2 C Þ2 ðI 2 IÞ i

i

57


As 21 , r , 1; the correlation coefficient is, in fact, a scaled version of the estimated slope [34], as is clearly demonstrated by Eq. (3.17), implying only that there is a dependence between C and I: Were this not true, then the instrument could not be used for calibration purposes at all. Most important is the fact that a value for the correlation coefficient close to 1 does not actually prove that the calibration is linear over the concentration region considered [33,35,36]. An example of this problem is shown in Fig. 3.3. Despite a correlation coefficient of 0.9993, the first-order linear model (Fig. 3.3a) is clearly demonstrated to be inadequate by the appearance of the associated residuals plot of Fig. 3.3b. The latter plot nevertheless indicates that the use of OLR is appropriate, since the range of residuals is independent of concentration, i.e. 1i ðmaximumÞ 2 1i ðminimumÞ < constant: A further possibility to check for curvature is provided by examining the confidence interval for the intercept. Confidence limits for both model parameters, ao and bo ; are given by: ao ^ tsðao

3:18aÞ

bo ^ tsðbo

3:18bÞ

where t is the Student’s t-value for a two-tailed distribution at the selected confidence level and with n ¼ n 2 2: If n . 6; t can be replaced by k ¼ 2 for an approximately 95% confidence interval, as mentioned previously (Section 3.4). If the confidence interval for ao includes the value of the measured response at zero concentration, then there is no evidence for curvature. Unfortunately, this test is not particularly robust, as OLR tends to yield rather broad confidence intervals for the intercept (as shown in the example described in Fig. 3.3, where the response at Ci ¼ 0 was zero and thus within the confidence interval for ao ). Therefore, residuals plots should always be constructed to test for limitations of the fitted equation. An additional benefit is that outlying points may reveal themselves, as suggested by the results for a calibration graph containing a deviating data point (Fig. 3.4a). The corresponding residuals plot (Fig. 3.4b) identifies the responses at the central calibration point as outliers, because they are not randomly distributed about the 1i ¼ 0 line. The ability to discern outliers is highly dependent on the number of concentration levels and the measurement precision, and the use of robust regression techniques may be preferable to ascertain whether outlying data are present [37,38]. If no evidence for curvature is detected, and if the confidence interval for ao includes zero (assuming, of course, that the response at C ¼ 0 really is zero), the data can be better fitted using an alternative, simpler version of Eq. (3.11): Ii ¼ b0o Ci þ 1i

ð3:19Þ

This results from the fact that the intercept term is not significantly different from zero and can thus be eliminated. Equations (3.12)– (3.16) can be rewritten

58


Fig. 3.4. Effect of an outlying data point (at C ¼ 0:5) on OLR. The fitted line in (a) is described by the equation Iî ¼ ð0:010 ^ 0:063Þ þ ð1:00 ^ 0:10ÞCi with r ¼ 0:997; with no sign of any outlier. The residuals plot (b) is, however, efficient at revealing the deviating behavior of this point.

as: SS0o ¼

n X i¼1

12i ¼

n X

ðIi 2 b0o Ci Þ2

ð3:20Þ

i¼1

X X Ci Ii 2 b0o C2i ¼ 0 P CI b0o ¼ P i 2 i Ci P P ðIi 2 b0o Ci Þ2 ðIi 2 Iî Þ2 ¼ S02 o ¼ n21 n21 hX i 2 0:5 0 0 sðbo Þ ¼ So = ðCi 2 C Þ

ð3:21Þ ð3:22Þ ð3:23Þ ð3:24Þ

with the prime indicating that the relevant term applies to the zero intercept, one parameter OLR model. Apart from computational simplicity, this model offers the advantages of using up only one degree of freedom and avoiding the need to account for covariance between regression parameters, as will become apparent below (Sections 3.6 –3.8). This can lead to considerable reductions in the magnitudes of the uncertainties associated with predicted concentrations. Figure 3.5 is an example of a spreadsheet that could be used for performing the calculations required by OLR. Harris [18] has also presented a similar spreadsheet solution for the case of OLR with a non-zero intercept term. 3.5.2

Weighted linear regression

The choice between ordinary and weighted linear regression (WLR) methods falls on the uncertainty structure. In the residuals plot of Fig. 3.6a, all values fall within a region bounded by two parallel lines, symmetrically displaced from

59

60 Douglas C. Baxter and Ilia Rodushkin Fig. 3.5. Example of a spreadsheet for OLR analysis of calibration data. Note that the figures in bold in row 10 are all the sums of data contained in the cells above. Also included are data for five “unknowns”, and the analytical results generated by external calibration (see Section 3.6) using OLR models with and without the intercept, Eqs. (3.11) and (3.19), respectively. Uncertainties in the response values for the unknowns were arbitrarily kept constant at all signal levels.


Fig. 3.6. The residuals plots depicted in (a) and (b) are for cases where first-order models are adequate, i.e., neither outliers nor curvature is present. However, in (b) the range of the residuals is obviously a function of concentration, indicating that application of WLR is necessary.

the line defined by 1i ¼ 0: This is characteristic of a constant level of uncertainty in the measurements; in other words, uðIÞ is independent of response and hence concentration, and the uncertainty is said to exhibit homoscedasticity. For such cases, OLR is the method of choice. In the case of Fig. 3.6b, the occurrence of heteroscedasticity is evident, i.e., the uncertainty of measurement is clearly a function of concentration and modeling the data will benefit from the use of WLR. The need for WLR follows quite logically from a simple analysis of the major noise contributions common to all instrumental methods of analysis [39]. In the simplest cases, the uncertainty may be dominated by white noise, uðIÞ < ðIÞ0:5 ; flicker noise, uðIÞ / I or a combination of both. Irrespective of which, response variations increase with signal level. This leads to the conclusion that the use of OLR will only be valid in a few, exceptional circumstances. Despite the fact that the benefits of WLR have been advocated quite frequently over the last quarter of a century or so [33,35,40–44], its use in current analytical practice would appear to have remained rather limited. This is perhaps due to the perceived complexity of the calculations involved. Actually, implementation of WLR is relatively straightforward using modern spreadsheet software (Fig. 3.7). Previously highlighted problems concerning the computational intensity and introduction of rounding errors [42] are no longer limitations. Recently, a software package facilitating OLR and WLR analyses of calibration data was made available on the World Wide Web [44], and hopefully this initiative will stimulate further use of the technique. The underlying idea behind WLR is that the fitting procedure should be performed in such a way as to ensure that the calculated line passes, as close as possible, the points with the smallest uncertainties. In OLR, on the other hand, the derived line best fits the points with largest responses. In order to force

61

62 Douglas C. Baxter and Ilia Rodushkin Fig. 3.7. Example of a spreadsheet for WLR analysis of calibration data. Note that the figures in bold in row 10 are all the sums of data contained in the cells above. Also included are data for five “unknowns”, and the analytical results generated by external calibration (see Section 3.6) using WLR models with and without the intercept, Eqs. (3.27) and (3.28), respectively. Uncertainties in the response values for the unknowns were arbitrarily kept constant at all signal levels. The uncertainties in the response data for the standards were modeled as uðIi Þ ¼ 2 þ 0:02 £ Ii : In some versions of Excel, the arguments to the IF statement should be separated by semi-colons instead of commas.


the line through the most precisely measured points, generally those at lower concentrations, all data are weighted, typically using: wi ¼ u22 ðIi

3:25Þ

It should be mentioned that using experimentally measured response uncertainties to calculate weights has its limitations. For example, in the admittedly unlikely event that uðIi Þ ¼ 0 at some response level, the weight at that point would be infinite. Uncharacteristically large, or small, uncertainties at any given response will result in an abnormal distribution of weights along the regression line. To avoid potential problems in the weighting process, the uncertainties can be modeled as a function of response level [42,43], using a polynomial of the form: u2 ðIi Þ ¼ f þ gIi þ hIi2 þ 1i

ð3:26Þ

The regression parameters may be considered to represent the variance at zero response ðf Þ; and the coefficients of white ðgÞ or flicker ðhÞ noise contribution to signal variability. A comparison of WLR using experimental and modeled uncertainties for ICP-OES calibration indicated that there was little to choose between them [43]. Modeling the uncertainty has a smoothing effect that will filter out noise to some extent, potentially resulting in artificially improved precision in the regression parameter estimates. After careful deliberation, we have decided not to pursue the use of modeled uncertainties any further. Depending on whether or not the intercept is significantly different from zero, as can be tested as described above, the linear models are: Ii ¼ aw þ bw Ci þ 1i Ii ¼

b0w Ci

ð3:27Þ

þ 1i

ð3:28Þ

where the subscript “w” indicates weighted regression coefficients and the prime denotes the use of the zero intercept assumption. The weights are first introduced in the expressions describing the sums of squares to be minimized, with respect to the weighted residuals: SSw ¼

n X

wi 12i ¼

i¼1

SS0w ¼

n X i¼1

n X

wi ðIi 2 aw 2 bi Ci Þ2

ð3:29Þ

wi ðIi 2 b0 Ci Þ2

ð3:30Þ

i¼1

wi 12i ¼

n X i¼1

Differentiating with respect to aw ; if appropriate, and to the slope, the normal equations are obtained: X X X wi Ii 2 aw wi 2 bw wi Ci ¼ 0 ð3:31aÞ X X X 2 wi Ci Ii 2 aw wi Ci 2 bw wi Ci ¼ 0 ð3:31bÞ

63


X X wi Ci Ii 2 b0w wi C2i ¼ 0 with the solutions: X X X X w ¼ wi Ii = wi 2 bw wi Ci = wi aw ¼ Iw 2 bw C P P P P P wi ðCi 2 C w ÞðIi 2 Iw Þ wi Ci Ii 2 wi Ci wi Ii = wi bw ¼ ¼ P P P P wi ðCi 2 C w Þ2 wi C2i 2 wi Ci 2 = wi

ð3:32Þ

ð3:33aÞ ð3:33bÞ

and

P wCI b0w ¼ P i i 2 i wi Ci

ð3:34Þ

The necessary statistics for describing the uncertainties in the WLR models of Eqs. (3.27) and (3.28) are given by: P P wi ðIi 2 aw 2 bw Ci Þ2 wi ðIi 2 Iî Þ2 ¼ ð3:35Þ S2w ¼ n22 n22 P P 0 wi ðIi 2 b0w Ci Þ2 wi ðIi 2 Iî Þ2 ¼ ð3:36Þ Sw2 ¼ n21 n21 " #0:5 " #0:5 P P P wi C2i wi C2i = wi ¼ S ð3:37aÞ sðaw Þ ¼ Sw P P P P P w wi wi ðCi 2 C w Þ2 wi C2i 2 wi Ci 2 = wi Sw Sw sðbw Þ ¼ P P P i0:5 2 0:5 ¼ hP 2 wi ðCi 2 C w Þ wi Ci 2 wi Ci 2 = wi w S2w C w S2w C covðaw ; bw Þ ¼ 2 P P P 2 ¼2P wi ðCi 2 C w Þ wi C2i 2 wi Ci 2 = wi hX i0:5 wi ðCi 2 C w Þ2 sðb0w Þ ¼ S0w =

ð3:37bÞ

ð3:37cÞ ð3:38Þ

Figure 3.7 displays an example of a spreadsheet that could be used to perform WLR of calibration data, based on the models with and without an intercept described above. 3.5.3

Linear regression for data with uncertainties in both variables

The third important variant of linear regression is that used for data sets where both sets of variables are subject to uncertainties. This is probably most applicable when concentrations, covering a wide range, are determined using two independent analytical methods and the results are to be compared. As such, each data point will consist of two estimated concentrations and their associated uncertainties. It is common practice to assign the results determined using a standard or reference method to the x-axis, implying that

64


the concentrations so obtained have negligible uncertainties. This leads to the unintentional inference that the new method must exhibit much greater uncertainties, hardly a desirable conclusion. Another possibility is that the calibrants employed are reference materials, containing experimentally derived concentrations of one or more analytes. Such materials obviously have uncertainties associated with the specified measurands, which in many situations cannot be justifiably neglected, and therefore one of the basic assumptions of OLR and WLR is violated. In either case, it is necessary to account for uncertainties in both the x- and y-directions, and weight the data accordingly. MacTaggart and Farwell [45] have critically evaluated the pertinent methods available in the literature and, on the basis of their findings, only the most robust version will be considered further, comprising a first-order linear model with an intercept: Ii ¼ aW þ bW þ 1i

ð3:39Þ

where the subscript “W” indicates that weighting is now applied to both the y- and x-variables. (The use of responses, I; and concentrations, C; as variables is only for reasons of consistency. For method comparison studies, the responses should be replaced by concentration estimates from the second method.) The weighted residuals sum of squares are given by: SSW ¼

n X i¼1

Wi 12i ¼

n X

Wi ðIi 2 aW 2 bW Ci Þ2

ð3:40Þ

i¼1

where Wi ¼ ½u2 ðIi Þ þ b2W u2 ðCi Þ21

ð3:41Þ

Unfortunately, as shown by Eq. (3.41), the weighting factors are dependent on the slope, and therefore, Wi and bW must either be determined iteratively or additional restraints placed on the weights, such as a constant uncertainty ratio, u2 ðIi Þ=u2 ðCi Þ: For the iterative solution, attributable to Williamson [46], the slope and intercept are estimated as: X X X X W ¼ Wi Ii = Wi 2 bW Wi Ci = Wi ð3:42aÞ aW ¼ IW 2 bW C P WzV ð3:42bÞ bW ¼ P i i i Wi zi Ui where

X X W ¼ Ci 2 Wi Ci = Wi Ui ¼ Ci 2 C X X Vi ¼ Ii 2 IW ¼ Ii 2 Wi Ii = Wi zi ¼ Wi ½u2 ðIi ÞUi þ bW u2 ðCi ÞVi

ð3:43aÞ ð3:43bÞ 3:43cÞ

Standard uncertainties for the slope, as well as the intercept and its

65

66 Douglas C. Baxter and Ilia Rodushkin Fig. 3.8. Example of a spreadsheet for linear regression analysis of calibration data with uncertainties in both variables. Note that this spreadsheet is an extension of that shown in Fig. 3.5, requiring an initial estimate of the slope as provided by OLR. Uncertainties in the response values for the unknowns were arbitrarily kept constant at all signal levels. The uncertainties in the response data for the standards were modeled as uðIi Þ ¼ 2 þ 0:02 £ Ii ; whereas those for the concentrations were random numbers generated by the spreadsheet software. To iterate, cells (O2:Y19) should be copied and pasted into cells (AA2:AK19), (AM2:AW19), etc. The results of the fourth and fifth iterations are identical, those for the latter being included. In some versions of Excel, the arguments to the IF statement should be separated by semi-colons instead of commas.


covariance, can also be calculated: X sðaW Þ ¼ 1= Wi þ 2ðC W þ 2zÞzQ þ ðC W þ 2zÞ2 s2 ðbW Þ

0:5

nX o0:5 Wi2 ½u2 ðIi ÞUi2 þ u2 ðCi ÞVi2 sðbW Þ ¼ Q W s2 ðbW covðaW ; bW Þ ¼ 2C

ð3:44aÞ ð3:44bÞ 3:44cÞ

where nX o21 Q¼ Wi ½Ui Vi =bW þ 4z0i ðzi 2 Ui Þ X X z ¼ Wi zi = Wi

ð3:45aÞ

z0i

ð3:45cÞ

¼ zi 2 z

ð3:45bÞ

Solution of Eq. (3.42) requires an initial estimate of the slope, which can most simply be provided by OLR analysis of the data. Although these equations appear rather intimidating, they can be readily solved using commercial spreadsheet programs, as exemplified in Fig. 3.8. MacTaggart and Farwell [45] have confirmed that the slope estimate converges rapidly, generally no more than 20 iterations sufficing to reach a stable value. It should be noted that this model provides an estimate of the slope that is equal to the reciprocal of that obtained upon switching axes. This is particularly important when using linear regression to compare data obtained using two independent methods applied to the same set of samples. In that case, the confidence intervals for the slope and the intercept should contain the expected values for perfect agreement of 1 and 0, respectively. OLR will yield two different sets of values that may provide contradictory conclusions regarding the equivalency of the two methods, clearly an inconsistent and unsatisfactory result. Another attractive feature of Williamson’s [46] approach is that sðbW Þ=bW remains constant on switching axes, which is further proof of the models’ quality.

3.6

EXTERNAL CALIBRATION

To use linear regression, a sufficient number, n; of calibration points must be measured; this requires that n $ 3 or 2 for the models with and without the intercept, respectively. The concentration of analyte in the unknown sample, Cu ; is then given by inserting the measured response, Iu ; in the appropriate variant of the regression equation, Eq. (3.11), (3.19), (3.27), (3.28) or (3.39), and solving for Cu (or C0u ): Cu ¼ ðIu 2 aÞ=b C0u ¼ Iu =b0

where a [ ðao ; aw ; aW Þ and b [ ðbo ; bw ; bW Þ

where b0 [ ðb0o ; b0w ; b0W

ð3:46Þ 3:47Þ

67


As far as we are aware, linear regression with uncertainties in both variables has never been utilized for calibration purposes, but then again, it has not found much application at all to date. 3.6.1

Estimating uncertainty

The next step is to calculate the combined uncertainty in the estimated concentration sc ðCu Þ or sc ðC0u Þ with reference to Eqs. (3.46) and (3.47), respectively. (Perhaps, the term “combined standard deviation of the mean estimated concentration” would be more consistent with the quantity actually being calculated, although such terminology is not used in the current literature [8].) This includes uncertainties in the measured response and the slope of the calibration line, and, where applicable, the intercepts’ uncertainty and covariance with the slope. The addition of a covariance term accounts for the fact that the estimates of a and b are not independent (unless the data are first mean-centered [28]), both being computed from the same calibration data set: sc ðCu Þ ¼ s2 ðIu Þ " sc ðC0u Þ ¼ s2 ðIu Þ

›Cu ›Iu ›Cu ›Iu

2

2

þs2a þs2b

›Cu ›a ›Cu ›b

2

þs2b

›Cu ›b

2 þ2 covða; bÞ

›Cu ›Cu ›a ›b

0:5

ð3:48Þ

2 #0:5 ð3:49Þ

Evaluating the partial derivatives and simplifying yields: sc ðCu Þ ¼

1 2 ½s ðIu Þ þ s2a þ s2b C2u þ 2 covða; bÞCu 0:5 b

ð3:50Þ

sc ðC0u Þ ¼

0 1 2 ½s ðIu Þ þ s2b0 Cu2 0:5 b0

ð3:51Þ

showing how the combined uncertainty in the estimated concentration is related to the parameters described in previous sections. Note that, when using Eqs. (3.50) and (3.51), s2 ðIu Þ ¼ u2 ðIu Þ=m; where m is the number of independently prepared and analyzed unknowns, not the number of replicate measurements of the same test portion of a single sample. The standard uncertainty of replicate measurements of a single unknown, i.e. uðIu Þ; should only be used in the situation that no other estimate of the variation is available, in which case m ¼ 1: This point is further developed at the end of Section 3.6.2. As Eqs. (3.50) and (3.51) are applicable to all types of linear regression with uncertainties in both variables, they are certainly the most flexible forms of the uncertainty expressions for estimated concentrations. However, valuable insight into the uncertainty structure, for the cases of OLR and WLR, can be

68


gained by some manipulation of these equations, i.e., " #)0:5 ( 2 1 u2 ðIu Þ ðIu 2 IÞ 2 1 sc ðCu Þo ¼ þ So þ 2P bo m n bo ðCi 2 C Þ2 sc ðC 0u Þo

1 ¼ 0 bo

(

" #)0:5 u2 ðIu Þ Iu2 02 þ So 0 P m bo2 ðCi 2 C Þ2 " # u2 ðIu Þ 1 ðIu 2 Iw Þ2 þ Sw P þ 2P m wi bw wi ðCi 2 C w Þ2

1 sc ðCu Þw ¼ bw sc ðC 0u Þw

1 ¼ 0 bw

ð3:52Þ

(

" #)0:5 u2 ðIu Þ Iu2 0 þ S w 02 P m bw wi ðCi 2 C w Þ2

ð3:53Þ 0:5

ð3:54Þ

ð3:55Þ

Unfortunately, due to the complexity of the equations, little insight is gained by similar manipulation of Eqs. (3.50) and (3.51) for linear regression with uncertainties in both variables. Thus, we will confine ourselves to the OLR and WLR cases when considering how to optimize precision for the analyte concentration estimates in Section 3.6.2. The same strategies are, of course, also applicable to data sets with uncertainties along both axes. Calculation of the expanded uncertainty for the estimated analyte concentration in a single unknown is performed on the basis of Eq. (3.10). Here, the approximate nature of the resultant “confidence interval” should be recognized. In reality, the upper and lower confidence limits for a concentration estimated using any of the linear regression techniques will be asymmetrically distributed about the mean value [33,34,41]. Use of Eq. (3.10), on the other hand, is simple and in line with current recommendations [6,8]. Clearly, application of Eq. (3.10) requires knowledge of the number of degrees of freedom, simply given by [8,33 –35]: n¼n22

ð3:56Þ

or n¼n21

ð3:57Þ

for calibration lines calculated with and without the intercept, respectively. However, once the calibration equation has been calculated, it will normally be used to estimate the analyte concentrations in a larger number of unknowns. As the same regression parameters will be used for all unknowns, Eq. (3.46) or (3.47), the resulting concentration estimates will all be correlated. This particular complication is rarely addressed in the analytical literature, notable exceptions including the work of Kragten [28], MacTaggart and Farwell [33] and Watters et al. [43]. Adequately accounting for this additional uncertainty remains a source of dissatisfaction for statisticians. A simplified approach is to

69


make the following substitution in Eq. (3.10): u k ¼ ta=2n n

ð3:58Þ

where a is the desired probability level (e.g. a ¼ 0:05 for a 95% confidence level), nu is the number of unknowns that will be calibrated and n is the appropriate value from Eq. (3.56) or (3.57). The two-tailed t-statistic demanded by Eq. (3.58) can be readily calculated using modern spreadsheet software. More details are provided by MacTaggart and Farwell [33] and the literature cited therein. It must be emphasized at this juncture that multiple uses of calibration lines is a topic not considered in current official publications discussing the treatment of uncertainties in analytical measurements. Therefore, it is probably advisable to ignore this complication at the present time. Returning to the formulae for uncertainty calculations, it is worth noting that the first term within the brackets in Eq. (3.52) is often represented as S2o =m: The implication is then that repeated analyses of the unknown will yield equivalent precision to that obtained for any given standard. (To be more specific, sðIu Þ must be insignificantly different from So in Eq. (3.52) or S0o in Eq. (3.53). The validity of this assumption can be tested by calculation of the F-statistic: Fn1 ;n2 ¼ s2 ðIu Þ=S2o F 0n1 ;n2

¼s

2

0

ðIu Þ=So2

where n1 ¼ ðm 2 1Þ; n2 ¼ ðn 2 2Þ

ð3:59aÞ

where n1 ¼ ðm 2 1Þ; n2 ¼ ðn 2 1Þ

ð3:59bÞ

If the calculated F-statistic is greater than the tabulated critical value at the selected confidence level, then the use of this alternative form is unjustified and 0 Eq. (3.52) or (3.53) should be used as written. For OLR, S2o or So2 is a pooled estimate of the spreads of responses over the range of concentrations [33]. 3.6.2

Optimizing precision

Considering OLR with a non-zero intercept, Eq. (3.52) suggests that there are several possibilities to minimize the uncertainty in the estimated concentration. In the vast majority of cases, the first term on the right-hand side of each of the Eqs. (3.52) –(3.55) is the major contributor to the uncertainty in the estimated concentration, as m will probably be close to one. Thus, increasing m; or indeed in Eq. (3.52); n; will have the desired effect, as will decreasing the term ðIu 2 IÞ obviously, as the measured response for the unknown approaches the mean, the third term in the brackets in Eq. (3.52) tends toward zero. Consequently, the importance of this term increases toward both extremes of the calibration line, as exemplified by the plot of the expanded (and scaled) uncertainty of the concentration estimate versus unknown response in Fig. 3.9a. For this reason, unknown samples should be prepared in such a way as to ensure that the IÞ; of the calibration line as measured responses are as close to the centroid, ðC; possible. Increasing the sum in the denominator can also reduce the contribution of the third term. One approach is to extend the concentration

70


Fig. 3.9. Effect of the type of external calibration model, (a) OLR or (b) WLR, on concentration estimates, Cu (solid lines), and expanded scaled uncertainties as a function of response for unknown. The dashed and dotted lines bound the expanded uncertainty bands for calibration lines with and without an intercept term, respectively. The data for OLR and WLR are those included in Figs. 3.5 and 3.7, respectively. As n ¼ 5 ðn , 6Þ; we ¼ 3:18 or 2.78 for the models with and without an intercept, respectively, used k ¼ ta¼0:05 n rather than k ¼ 2 (Section 3.4). To emphasize the differences between models, the expanded uncertainty terms were also scaled by a factor of 5.

range covered by the calibration line. The other is to prepare standards with concentrations focused at the extremes of, rather than evenly distributed over, the calibration range [47]. However, care should be exercised to avoid violating the assumption of a first-order linear relationship between response and concentration when attempting to apply the latter approaches. Similar considerations are also applicable to the use of WLR with a non-zero intercept, Eq. (3.54). For the latter, Fig. 3.9b shows that the expanded (and scaled) uncertainty of the concentration estimate is a generally increasing function of the unknown response. The most striking feature in comparing panels (a) and (b) in Fig. 3.9 is that the use of weighting results in smaller absolute uncertainties at lower concentrations. From Fig. 3.9b it is apparent that the uncertainties of the concentration estimates are proportional to the responses; this is consistent with the fact that most analytical instruments provide readings with larger absolute uncertainties at higher concentrations. Generally, WLR with a non-zero intercept will not provide a pronounced w ; Iw Þ: This results from the minimum in uncertainty close to the centroid, ðC fact that the first term on the right-hand side of Eq. (3.54) increases monotonically with response, whereas the corresponding term in Eq. (3.52) should be constant, i.e., consistent with the homoscedasticity assumption. Examples of expanded (and scaled) uncertainty plots derived on the basis of zero intercept OLR and WLR are included in Fig. 3.9. The most obvious effect of eliminating the intercept is that of providing smaller uncertainties in estimated concentrations at lower responses, close to the origin, for OLR. This is as expected from Eq. (3.53), because the first and second terms on

71


the right-hand side are constant and quadratic in the response, respectively. The latter term tends to zero on approaching the origin, whereas the corresponding term in Eq. (3.52) is minimized at the centroid. For the zero intercept WLR model, Fig. 3.9b clearly indicates the advantage gained, compared to the variant including the intercept, in terms of the smaller uncertainties in estimated concentrations over almost the entire calibration range. Obviously, one of the key considerations is the number of standards to use in constructing the calibration function. Preparing a large number of calibrants is tedious, particularly for the multi-elemental measurements that are becoming the status quo in analytical chemistry, and demands valuable instrument time for their analysis. It might, therefore, be tempting to include all replicate measurements in the OLR analysis of the calibration data. There is a precedent for this provided in one of the examples included in the current EURACHEM/CITAC guide to the evaluation of measurement uncertainty [8]. Furthermore, this practice is both pragmatic and logically defensible because standards are usually prepared from solutions; thus, replicate measurements of a single standard should yield an uncertainty that is statistically indistinguishable from that for single measurements of several, independently prepared calibrants of the same nominal concentration. However, the same argument is not applicable to most sample types. Substantial sample preparation may precede analysis, the material under study may be inhomogeneous with respect to analyte distributions, and corrections for spectral interferences may be required. Thus, replicated measurements of a single test portion will often yield an overly optimistic estimate of the uncertainties involved. On the other hand, the costs of preparing series of independent replicate test portions from every laboratory sample are likely to be prohibitive. During method validation, the analytical method should be thoroughly evaluated using the range of matrices and concentrations to which it will be applied [10], as noted previously in Section 3.3. At this stage, sufficient data should be collected so that a reasonable estimate can be made of the expected reproducibility for any future sample. Such data can then be used to provide estimates of response uncertainties for Eqs. (3.50) –(3.55). Unless several test portions of the same laboratory sample are prepared for analysis and subjected to the measurement process, the divisor in the first term of Eqs. (3.52)– (3.55) must be set to one, irrespective of the regression technique being used. 3.6.3

Accounting for non-constant sensitivity

If the sensitivity were not constant during the period over which unknowns are analyzed, external calibration would have to be repeated at appropriate intervals. In practice, this is likely to be achieved by measurement of a single standard to correct for changes in the slope of the calibration function. This is,

72


statistically speaking, an example of a single point calibration for which linear regression is unsuitable because of insufficient degrees of freedom, as shown by Eqs. (3.56) and (3.57). An alternative form of data treatment is therefore required. Considering the sequence of measurements shown in Fig. 3.10, consecutive pairs of blank and standard responses can be used to estimate the slope and intercept parameters required for calibration: aj ¼ I0 2 bj ¼

j ðI 2 I5 nu þ 2 0

1 j21 ðI1 2 I6 Þ 2 aj I1 2 Cs nu þ 2

3:60aÞ ð3:60bÞ

where Cs is the analyte concentration in the standard, j ¼ 0; 1; …; 6 is the index of the measurement beginning with the blank at j ¼ 0; nu is the number of unknowns and ðnu þ 2Þ is the total number of measurements made before the blank and standard are next analyzed (assuming one blank and one standard per recalibration interval). Here it is assumed that the sensitivity drifts linearly between recalibrations. The concentration of analyte in the unknown can then be estimated as: Cu;j ¼

Iu; j 2 aj bj

ð3:61Þ

Assuming that the uncertainty in the concentration of analyte in the standard is negligible, as will generally be the case, propagating the uncertainties in

Fig. 3.10. Experimental design for external calibration in the presence of non-constant sensitivity. The number above each response bar is the index, j; of the measurement. Note that, if more unknowns had to be analyzed after the second measurement of the standard, j ¼ 6; then indices 5 and 6 would be reset to 0 and 1, respectively, and the cycle continued.

73


Eqs. (3.60) and (3.61) leads to: (

2

2 )0:5 j j 2 2 þs ðI5 Þ sðaj Þ ¼ s ðI0 Þ 1 2 nu þ 2 nu þ 2 sðbj Þ ¼

1 j21 2 2 j21 2 2 s2 ðI1 Þ 1 2 þs ðI6 Þ þs ðaj Þ b j Cs nu þ 2 nu þ 2

" #2 )0:5 ( Iu; j 2 aj 1 2 2 2 s ðIu; j Þ þ s ðaj Þ þ s ðbj Þ sc ðCu; j Þ ¼ bj bj

ð3:62aÞ 0:5

ð3:62bÞ

ð3:63Þ

a series of expressions rather more complicated than might be anticipated for single point calibration. Of course, this complexity arises out of the need to correct for the drifting blank and standard signal levels, requiring five separate measurements to calculate one concentration. (As an aside, it can be mentioned that all concentrations computed from consecutive recalibration cycles are correlated, as at least one pair of blank and standard measurements will be common to all the calculations; see discussion in Section 3.6.1.)

3.7

METHOD OF STANDARD ADDITIONS

When the matrix exerts non-spectral interferences, calibration must be performed with the sample itself [11,17,18,35]. The most common solution to this calibration problem is application of the method of standard additions. This typically involves adding either (a) variable volumetric increments ðVs;i Þ of a standard solution ðCs Þ or (b) a fixed volume ðVs Þ of varying concentration ðCs;i Þ standards to constant-volume sample aliquots ðVu Þ; and diluting to a predetermined volume ðVT Þ before measurement. Both cases, illustrated in Fig. 3.11, correspond to spiking the sample at known concentration levels. Depending on the units of concentration employed, volumes may be replaced by masses, as the situation requires. A crucial aspect of this mode of calibration is that the matrix exerts the same effect on the added analyte as it does on the analyte originally present in the unknown. Failure to conform to this requirement will result in the generation of analytical data subject to systematic errors. For trace element determinations, it is therefore good analytical practice to add analyte in the same chemical form as that present in the unknown [11]. It should also be emphasized that this mode of calibration can correct for matrix-induced, but not temporal, variations in sensitivity when carefully applied. For cases (a) and (b), the total analyte concentration in any given spiked sample is expressed by Eq. (3.64a) or (3.64b), respectively, CT;i ¼ C0u Vu =VT þ Cs Vs;i =VT ¼ Cdu þ Cds;i

74

ð3:64aÞ


Fig. 3.11. Illustration of the experimental design for the method of standard additions using (a) constant concentration and variable volume additions, or (b) constant volume and variable concentration additions.

CT;i ¼ C0u Vu =VT þ Cs;i Vs =VT ¼ Cdu þ Cds;i

ð3:64bÞ

the superscript “d” indicating the dilution operation. Implicitly assuming a first-order linear relationship between response and concentration, the recorded analytical signals for the series of solutions can be described by an expression of familiar form: Ii ¼ bSA CT;i ¼ bSA ðCdu þ Cds;i Þ ¼ aSA þ bSA Cds;i

ð3:65Þ

where aSA ¼ bSA Cdu and hence: Cdu ¼ aSA =bSA

ð3:66Þ

Thus, the analyte concentration in the diluted unknown is given by the ratio of the intercept to the slope for the method of standard additions (distinguished by the subscript “SA”). Taking account of Eq. (3.64), the concentration in the initial unknown is: C0u ¼ Cdu VT =Vu

ð3:67Þ

The least-squares techniques, OLR or WLR, as described in Sections 3.5.1 and 3.5.2, respectively, may be used to determine the intercept and slope. 3.7.1


Calculation of the combined uncertainty for the method of standard additions is normally based on Eq. (3.66), thus neglecting uncertainties in volume

75


measurement [17,18,35]: " #0:5 SSA;o 1 I2 d þ 2 P d sc ðCu Þo ¼ ds Þ2 bSA;o n bSA;o ðCs;i 2 C

ð3:68Þ

where the subscripts on the mean square about regression ðSÞ and slope terms are reminders that the method of standard additions and OLR are being employed. Calculation of these parameters is as described in Section 3.5.1, Eqs. (3.14) and (3.15). Similarly, for the use of WLR, the combined uncertainty can be defined as: " #0:5 SSA;w I2w 1 P sc ðCdu Þw ¼ þ 2 P ð3:69Þ ds;w Þ2 bSA;w wi bSA;w wi ðCds;i 2 C Again, computation of the slope and mean square about regression terms have been detailed previously, in Section 3.5.2, Eqs. (3.33) and (3.35), respectively. Having established estimates of the concentration and its uncertainty in the diluted unknown, using Eq. (3.67) gives: sc ðC0u Þ sc ðCdu Þ < C0u Cdu

ð3:70Þ

under the condition that the uncertainties in VT and Vu are negligible, as will generally be true. (The subscripts “o” and “w” denoting OLR and WLR have been dropped, as the equation is equally applicable in either case.) Otherwise, it is a simple matter to account for all three sources of uncertainty: " #0:5 2 d s2 ðVT Þ s2 ðVu Þ 0 0 sc ðCu Þ sc ðCu Þ ¼ Cu þ þ ð3:71Þ VT2 Vu2 ðCdu Þ2 Expanded uncertainties for concentrations estimated using either OLR or WLR, as appropriate, can then be calculated according to Eq. (3.10), noting that there are n 2 2 degrees of freedom. However, examination of Fig. 3.12 shows that the uncertainty bounds for the estimated concentration are not actually symmetric, as was also the case for external calibration. Thus, it should be recognized that the application of Eq. (3.10) would again imply some degree of approximation by inherently providing a symmetric estimate of the expanded uncertainty for the method of standard additions. 3.7.2


It would appear that OLR has been used almost exclusively in combination with the method of standard additions, although Gardner and Gunn [48] have demonstrated the precision advantage gained using WLR. Recalling Fig. 3.9, it was apparent that the uncertainties associated with concentrations estimated using non-zero intercept OLR increase considerably at the extremes of the

76


Fig. 3.12. Comparison of expanded uncertainty bands when applying OLR (dashed lines) and WLR (dotted lines) to analytical data acquired using the method of standard additions. The uncertainty bars are 95% confidence limits for the measured responses, and are indicative of heteroscedasticity.

calibration region. As shown graphically in Fig. 3.12, the method of standard additions requires extrapolation of the fitted line beyond the lower extreme of the experimental response range. As such, the width of the uncertainty band around the estimated concentration increases rapidly with the extent of the extrapolation required [35,49]. This effect is particularly acute with OLR, because the centroid of the fitted line is further from the concentration-axis intercept than with WLR. From the 95% confidence intervals for the responses included in Fig. 3.12, it is clear that uncertainty increases with response and hence the application of WLR is justified. For the example shown, including weighting factors in the calculations approximately halves the expanded uncertainty. One important point to bear in mind is that the apparent advantage of WLR, indicated in Fig. 3.12, is dependent on the uncertainty structure. As noted above (Section 3.5.2), analytical instruments have a tendency to generate increasing measurement uncertainties with response. If measurement uncertainty is truly independent of signal level, then the weighting factors will be constant and the WLR solution to the normal equations reduces to that of OLR. If the correct regression technique is used, the uncertainty in the concentration estimated using the method of standard additions would always decrease monotonically with increasing level of added analyte [48,50]. This is analogous

77


to one of the considerations made for optimizing precision using external calibration, specifically that increasing the calibration range reduces the uncertainties. An expanded calibration range maximizes the sum in the denominator of the second term on the right-hand side of Eq. (3.68) or (3.69) and decreases the extent of extrapolation. AsPfor external calibration, uncertainties are also minimized by increasing n (or wi in the case of WLR). 3.7.3

Accounting for non-constant sensitivity

If the sensitivity is not constant, the method of standard additions will tend to yield erroneous results. The magnitude of this bias will depend on the rate of change of the sensitivity over the time interval required to complete the analyses of the unspiked and spiked unknowns. Such errors can be minimized by the experimental design shown in Fig. 3.13, where each unknown (or blank) is analyzed before and after a single spiked test portion. The analyte concentration is then estimated using: bu ¼

I1 2 ðI0 þ I2 Þ=2 Cds

ð3:72Þ

Cu ¼

I0 þ I2 2bu

ð3:73Þ

where the slope is calculated individually for each unknown (or blank), u, on the basis of the three measurements. Correction for dilution is made, as before, using Eq. (3.67) and finally, blank subtraction is implemented, if required.

Fig. 3.13. Experimental design for standard additions in the presence of non-constant sensitivity. The number above each response bar is the index, j; of the measurement.

78


The uncertainties associated with the computation of the slope parameter, Eq. (3.72), and the estimated concentration, Eq. (3.73), are evaluated as: 1 ½s2 ðI0 Þ þ 4s2 ðI1 Þ þ s2 ðI2 Þ0:5 2Cs (

)0:5 1 I0 þ I2 2 2 2 2 sc ðCu Þ ¼ s ðI0 Þ þ s ðI2 Þ þ s ðbu Þ bu 2bu sðbu Þ ¼

ð3:74Þ ð3:75Þ

assuming negligible uncertainty in the standard used to spike the unknowns (or blank). 3.8

INTERNAL STANDARDIZATION

As will become apparent, the concentration of internal standard added to the unknown is the basis of this calibration technique. Consequently, the internal standard species should be absent from the unknowns. Alternatively, the species selected should occur at such low concentrations that the result is not significantly affected. A useful rule of thumb is that the internal standard is added in a 100-fold excess over the highest concentration originally present in the unknowns. The resulting uncertainty in the measured signal level will thus drown any variations caused by the presence of varying concentrations of the internal standard species. Of course, this approach requires a certain amount of prior knowledge about the unknowns and their expected composition. To function properly as a surrogate analyte, the internal standard should exhibit similar chemical and physical properties. The greater the similarity, the more likely both are affected to the same extent by drift and non-spectral interference processes. It is also necessary to ensure that both the analyte and the internal standard can be detected without spectral interferences from other components present in the unknowns. Consequently, selection of an internal standard necessitates a considerable investment in terms of preliminary investigations [14–18]. It is certainly worth the effort, as internal standardization is one of the most versatile calibration approaches, by virtue of its ability to correct for sensitivity variations caused by instrument instability and non-spectral interferences. To apply internal standardization, it is necessary to establish the relationship between the sensitivities for the analyte and the internal standard. This is typically achieved by analyzing a series of calibrants containing varying analyte and constant internal standard concentrations. The ratio of analyte (subscript “X”) to internal standard responses, IX=IS;i ; is a function of the concentration ratio, CX=IS;i ; and can be modeled using the relationships: IX=IS;i ¼ aIS þ bIS CX=IS;i þ 1i IX=IS;i ¼

b0IS CX=IS;i

þ 1i

ð3:76Þ ð3:77Þ

79


depending on whether or not an intercept is included in the linear regression model. The model parameters are appropriate for internal standardization, as identified by the subscript “IS”, and may be calculated by OLR or WLR as discussed in Sections 3.5.1 and 3.5.2, respectively. Analyte concentrations in unknown samples, spiked with internal standard, can readily be determined on the basis of their response ratios and internal standard levels: IX=IS;u 2 aIS Cu ¼ CIS;u ð3:78Þ bIS IX=IS;u C 0u ¼ CIS;u ð3:79Þ bIS Note that addition of internal standard implies dilution of the unknowns. Obviously this dilution effect must be corrected before reporting the results, using Eq. (3.67), but it will not be considered implicitly in the following. Again, as for the method of standard additions (see Section 3.7.1), it is assumed that dilution does not provide a significant contribution to the uncertainty in the concentration estimate.

3.8.1


If it is further assumed that CIS;u is known exactly, the combined uncertainty in the estimated analyte concentration can be readily determined, because Eqs. (3.78) and (3.79) are of the same form as Eqs. (3.46) and (3.47), respectively. " # 0:5 ðIX=IS;u 2 IX=IS Þ2 CIS;u u2 ðIX=IS;u Þ 1 sc ðCu Þo ¼ þ S2IS;o þ 2 P ð3:80Þ X=IS Þ2 m bIS;o n bIS;o ðCX=IS;i 2 C sc ðC0u Þo

CIS;u ¼ 0 bIS;o

sc ðCu Þw ¼

0:5

ð3:81Þ

CIS;u bIS;w

sc ðC0u Þw ¼

" # 2 IX=IS;u u2 ðIX=IS;u Þ 0 2 þ SIS;o 0 2 P X=IS Þ2 m bIS;o ðCX=IS;i 2 C

u2 ðIX=IS;u Þ ðIX=IS;u 2 IX=IS;w Þ2 1 þ 2 P þ S2IS;w P X=IS;w Þ2 m wi bIS;w wi ðCX=IS;i 2 C

CIS;u b0IS;w

"

2 IX=IS;u

u2 ðIX=IS;u Þ 0 2 þ SIS;w P 02 X=IS;w Þ2 m bIS;w wi ðCX=IS;i 2 C

#

0:5

ð3:82Þ 0:5

ð3:83Þ

Equations (3.80) and (3.81) apply to OLR with and without an intercept, respectively, whereas Eqs. (3.82) and (3.83) are the corresponding expressions

80


for WLR. The terms have been defined in Section 3.6.1 following Eqs. (3.52)– (3.55), the same considerations made there applying even here. 3.8.2


Strategies for minimizing uncertainties in estimated analyte concentrations are the same as those pertaining to external calibration. Somewhat surprisingly, however, the question as to the optimum internal standard concentration to use does not appear to have received much attention. For this reason, some model calculations were performed, a selection of the results for regression models including the intercept being illustrated in Fig. 3.14. Several situations were considered: (i) a constant level of measurement uncertainty, corresponding to conditions for which OLR would be applicable; (ii) a constant level of relative measurement variance, i.e. u2 ðIÞ / I 2 ; (iii) response variability determined by Poisson (counting) statistics as ideally obtained using a mass spectrometer, i.e. u2 ðIÞ ¼ I; and (iv) a linear combination of (ii) and (iii). Note that WLR is required to treat data for the situations described by scenarios (ii) –(iv). From Fig. 3.14a it transpires that, for situations (i) and (ii), the choice of internal standard concentration is irrelevant. Furthermore, when the requirements for applying OLR are met, the precision attainable is only favorable at very high analyte signal levels. Fortunately, most analytical instruments generate absolute signal variations that are an increasing function of response or concentration, as mentioned earlier. As such, WLR should then be used and this will result in considerably improved precision in analyte concentration estimates. For cases (iii) and (iv), the results of Fig. 3.14b,c demonstrate that using a high concentration of internal standard is optimal at high, but not low, analyte concentrations. It is therefore of importance to be aware of the noise characteristics of the instrument in order to determine the optimum spiking protocol. Of course, if the unknowns are likely to contain small quantities of the internal standard species, high levels must be added, which may be detrimental for the precision achievable at lower analyte concentrations, depending on the uncertainty structure. 3.9

ISOTOPE DILUTION

Isotope dilution mass spectrometry has the potential for application to some 60 elements that have at least two stable isotopes. The availability of long-lived radionuclides of additional elements further extends the suite of analytes that are amenable to this calibration approach, as illustrated in Fig. 3.15 [20]. Although calibration curve-based methods for the calculation of analyte concentrations by isotope dilution have been developed [51–53], these require the use of rational polynomials and are outside the scope of this treatment. We

81

82 Douglas C. Baxter and Ilia Rodushkin

Fig. 3.14. Effect of the internal standard concentration ðCIS Þ used on the relative standard uncertainty ðRSU ¼ sðCu Þ=Cu £ 100%Þ of the estimated analyte concentration. For all examples, the same set of normally distributed random numbers was used, generated at concentration levels ranging from 0 to 106, assuming that IX ¼ CX ; IIS ¼ CIS ; aIS ¼ 110 and bIS ¼ 1; with a constant standard uncertainty, sðIÞ; of 50. Mean values at concentration levels of 0, 104, 105 and 106 ðn ¼ 4Þ were employed to generate calibration curves using internal standardization and OLR or WLR, as appropriate, with a specific type of noise included. In (a), curve 1 (left axis) was obtained using OLR [m ¼ 1 in Eq. (3.80)] with sðIX Þ ¼ sðIIS Þ ¼ 50; independent of CIS ; curve 2 (right axis) was obtained using WLR (mu ¼ 1 in Eq. (3.82)) with sðIX Þ=IX ¼ sðIIS Þ=IIS ¼ 0:01; i.e. 1% proportional noise at all signals levels, and was independent of CIS : In (b), response precision was assumed to be governed by Poisson statistics, i.e. s2 ðIÞ ¼ I; the curves showing the results for CIS at (1-thick, solid line) 103, (2-dashed line) 104, (3-dotted line) 105 and (4-solid line) 106. In (c), the response precision was described by the relationship s2 ðIÞ ¼ I þ ð0:01 £ IÞ2 ; i.e. a linear combination of counting statistics and proportional noise; CIS as for (b).


Fig. 3.15. Periodic table showing the elements for which isotope dilution mass spectrometry can be applied. The number of stable (or long-lived) naturally occurring isotopes is given above the element symbol. For the mono-isotopic elements Al, I and Th, nominal masses for long-lived radionuclides, which are readily available for use as tracers, are given in parentheses. Adapted from Ref. [20].

will therefore confine the ensuing discussion to the most commonly adopted variant of isotope dilution analysis, i.e., that without a calibration curve. Under the provision that the added material is equilibrated with the native analyte, exact compensation can be made for incomplete recoveries or losses during the sample preparation, as well as for non-spectral interferences [54]. Like all the calibration approaches discussed earlier, however, correction for spectral interferences is not directly possible and so selection of the isotopes used must be made carefully to avoid potential contributions from polyatomic ions or isobars having the same nominal mass-to-charge ratios as the native and enriched isotope analyte ions. Alternatively, suitable empirical correction factors must be applied, or the isotope system of interest must be separated from interfering species during sample preparation. Being based on experimental measurements, the former approach leads to the introduction of additional sources of uncertainty that must be accounted for in the uncertainty budget. The latter approach is that commonly adopted prior to TIMS measurements, and is becoming ever more widely applied for ICP-MS-based procedures as well [24]. Isotope dilution requires that the isotopic composition of both the native analyte in the sample and that of the spike [55] be known. When known

83


amounts of sample and spike are mixed, the measured isotopic composition of the resulting mixture can be used to calculate the concentration of analyte in the original unknown. In many cases, the isotopic composition of the sample can be safely assumed to equal that given in the current listing from IUPAC [56]. In other cases, where considerable natural variations occur, e.g. as a result of radiogenic processes, it may be necessary to first establish the isotopic composition of the unknown. One such example is lead, where three of the four naturally occurring isotopes, 206Pb, 207Pb and 208Pb, are the ultimate products of radioactive decay of 238U, 235U and 232Th, respectively. Only 204Pb is nonradiogenic, and the isotopic composition of lead in any sample will reflect the origin of the lead incorporated in the material, as well as the original concentrations of the parent isotopes [26]. The isotope ratio, IR, in a spiked unknown is given by: n1;u þ n1;t n1 IRm ¼ ¼ ð3:84Þ n2 m n2;u þ n2;t where n1 and n2 (mol) are the amounts of isotope (or isotopomer in the case of molecular species) present in the mixture (subscript “m”), in the unspiked sample (u) and in the enriched isotopic spike or tracer (t). Note that all isotope ratios mentioned here are consistently defined in terms of moles of isotope 1 divided by moles of isotope 2; other conventions may be encountered in the literature [23]. Rearranging to express the terms as isotope ratios and solving for n1;s yields [22]: n1 n 2 1 IRt 2 IRm IR ðIRt 2 IRm Þ n n t 2 ¼ n2;t u ð3:85Þ n1;u ¼ n2;t 2 m ¼ n2;t n1 n2 IRm 2 IRu IRm =IRu 2 1 21 n2 m n1 u Isotope 1 is preferably the most abundant isotope in the sample, although the choice is also made on the basis of freedom from spectral interferences. Isotope 2 is generally a minor isotope in the sample, but available in highly enriched form. Thus, by definition, n1;u . n2;u and IRu . IRm . IRt : Equation (3.85) provides the number of moles of isotope 1 present in the unknown relative to the amount of isotope 2 in the spike. To convert the quantity n1;u into more traditional concentration units, due consideration must be given to the relative atomic (or molecular) weights of the native ðMu Þ and spiked ðMt Þ analyte, as well as to the atom fractions or relative abundances of isotopes 1 and 2 in the original unknown ðA1;u Þ and in the tracer ðA2;t Þ; respectively. The volumes (or masses, depending on the units of concentration employed) of unknown ðVu Þ and tracer ðVt Þ must also be taken into account: n1;u ¼

84

Cu Vu A1;u Mu

ð3:86aÞ


n2;t ¼

Ct Vt A2;t Mt

ð3:86bÞ

Substituting Eqs. (3.86) into Eq. (3.85), the isotope dilution equation is obtained, enabling the desired analyte concentration in the unknown to be calculated:

Mu Vt A2;t IRu ðIRt 2 IRm Þ ð3:87aÞ Cu ¼ Ct Mt Vu A1;u IRm 2 IRu

IRu ðIRt 2 IRm Þ Cu ¼ Ct F ð3:87bÞ IRm 2 IRu where F is a coefficient summarizing the ratio preceding the bracketed term. Various alternative forms of the isotope dilution equation may be found in the literature [20,23], all giving the same result. The advantage of the present form is that the contributions of the unknown, tracer and mixture isotope ratios are shown explicitly. As isotope ratios are computed from the measured count rates for isotopes 1 and 2 as I1 =I2 ; it is important to use blank and background corrected responses for the calculations. Subtraction of the blank concentration (calculated in the same way as for an unknown) from the result for an unknown could lead to systematic errors unless the blank and all unknowns exhibit exactly the same measured intensity ratio. Many mass spectrometers, particularly older quadrupole models, yield fairly high background count rates across the entire mass-to-charge range that have no relation to true isotope ratios [57]. 3.9.1

Mass discrimination and detector dead time

As yet, no mention has been made of the fact that the raw isotope ratios generated by a mass spectrometer are not accurate reflections of the true values. In Eqs. (3.85) and (3.86), it is actually implied that ðn1 =n2 Þx ¼ IRx ¼ ðI1 =I2 Þx ; i.e., that the measured count rate ratio equals the molar ratio of isotopes 1 and 2 in the analyzed test portion. That this is not the case that depends on two clearly defined factors, the most important resulting from variations in the transmission efficiencies of ions of differing mass-to-charge ratios, causing an instrumental mass discrimination effect [21,24,58 –60]. Thus, a measured isotope ratio ðIRmeas Þ will differ from the true value ðIRtrue Þ by a factor K : IRtrue ¼ IRmeas K

ð3:88Þ

If no correction for instrumental mass discrimination were made, the concentration calculated using Eq. (3.87) would be biased. For example, if all three isotope ratios were measured, and the uncorrected data were inserted in

85


Eq. (3.87), the calculated analyte concentration would be in error by a factor of 1=K (assuming that the same mass discrimination factor applied to each ratio). Habfast [59] and Marećhal et al. [60] have given useful recent discussions of mass discrimination factors, and the inclusion of uncertainty contributions has been described by Garcıá Alonso [23]. For systems consisting of three or more isotopes (refer to Fig. 3.15), it is possible to correct for mass discrimination effects using an internal normalization technique [61]. Alternatively, again for polynuclidic elements, double or triple spiking can be used to provide mass discrimination correction [8,62 –65]. For descriptions of these methodologies, reference should be made to the relevant literature. At low concentrations, or when measuring isotopes of low abundance, electron multipliers are employed to enable registration of the smallest ion count rates. As a result of the finite time required by the detection system to process each ion signal pulse, the electron multiplier exhibits a dead time during which no further ions can be detected. At higher concentrations, the probability that an ion reaches the detector while a previous signal pulse is still being processed increases, leading to a concomitant loss in count rate [21,66, 67]. Thus, the detector dead time constitutes a second source of error in measured isotope ratios. For most ion counting systems, it is necessary to determine the dead time experimentally. This has been a subject of considerable interest in recent years, leading to the development of a variety of experimental methods for dead time determination [21,68–70]. Having determined the dead time, t (s), correction of the measured count rates, It ¼

I 1 2 It

ð3:89Þ

and hence of the isotope ratio, can be implemented: IRK;t ¼ K

I1 I2

1 2 tI2 1 2 tI1

¼

IRmeas K 2 tI1 1 2 tI1

ð3:90Þ

Note that correction for mass discrimination is also required, and included in the above expression. The rightmost form of Eq. (3.90) has been introduced to reduce the number of variables, as this will assist in the uncertainty analysis later. 3.9.2


The coefficient F in Eq. (3.87b) can sometimes be assumed to contribute little uncertainty to the concentration estimate [22]. Nevertheless, some caution in making this assumption is warranted, particularly when the exact isotopic composition of the analyte or the spike is uncertain. The combined uncertainty

86


in F can be readily estimated: sc ðFÞ ¼ F

s2 ðA1;u Þ s2 ðMu2 Þ s2 ðVt2 Þ s2 ðA2t Þ s2 ðMt2 Þ s2 ðVu2 Þ þ þ þ þ þ 2 2 2 2 2 A1;u Mu Vt At Mt Vu

0:5

ð3:91Þ

and factored into the calculation of the combined uncertainty for the analyte concentration, if deemed necessary. Garcıá Alonso [23] provides detailed accounts of the calculation of uncertainties in atomic weights and relative abundances. Note that the measurement of volumes (or masses) of unknowns and tracers can also introduce substantial uncertainties to the factor F: Propagating the uncertainties in Eq. (3.87b) and evaluating the partial derivatives provides the necessary relationship for the combined uncertainty in the analyte concentration estimated by the isotope dilution analysis of the unknown [22,23,71,72]: (

2 s2 ðCt Þ s2 ðFÞ s2 ðIRu Þ IRm sc ðCu Þ ¼ Cu þ c 2 þ 2 2 IRm 2 IRu Ct F IRu ð3:92Þ )

2 2 0:5 s2 ðIRt Þ IRt s2 ðIRm Þ IRm ðIRu 2 IRt Þ þ þ IRt 2 IRm ðIRt 2 IRm ÞðIRm 2 IRu Þ IR2t IR2m Note that, when the purity or stoichiometry of the enriched tracer is in doubt, the terms Ct and IRt must be determined experimentally, and might thus constitute a major source of uncertainty in the analytical result. It also follows that the terms Mt and A2;t will have to be evaluated in Eq. (3.87a), introducing further uncertainties. We should also emphasize that it has been assumed that all the uncertainties are independent, thus covariance terms have been omitted. Adding a term allows the variance introduced by correcting for mass discrimination effects to be included in the combined uncertainty for the isotope ratio [23]: " #0:5 sc ðIRK Þ s2 ðIRmeas Þ s2 ðKÞ ¼ þ ð3:93Þ IRK IR2meas K2 where the subscript “K” denotes the nature of the correction applied to the isotope ratio. The combined relative uncertainty, in a mass discrimination [58] and dead time corrected isotope ratio [22,66], is given by [23,73]: ( 2 sc ðIRK;t Þ s2 ðIRmeas Þ 1 s2 ðKÞ ¼ þ 2 IRK;t 1 2 tI1 =IRmeas IRmeas K2 " þ

s2 ðI1 Þ s2 ðtÞ þ I12 t2

#

tI1 ðIRmeas 2 1Þ ðIRmeas 2 tI1 Þð1 2 tI1 Þ

2 )0:5

ð3:94Þ

87


Equations (3.93) and (3.94) can replace any, or all, of the affected isotope ratio terms in Eq. (3.92), as appropriate for the system under consideration. 3.9.3


Equation (3.92) provides a useful theoretical basis for optimizing the precision of isotope dilution measurements [20,22]. Analyte isotope ratios in the unknown and in the tracer are obviously fixed, whereas that of the mixture can be controlled. The differences between isotope ratios appearing in the denominators of the last three terms in Eq. (3.92) should be maximized in order to minimize uncertainty magnification, i.e. those factors enclosed in square brackets. As the isotope ratio of the mixture approaches that of the unknown, a condition denoted underspiking, the term ðIRm 2 IRu Þ tends to zero and two of the uncertainty magnification factors become very large. In the case of overspiking, ðIRt 2 IRm Þ tends to zero, again resulting in considerable uncertainty magnification. Purely from the standpoint of uncertainty propagation, the optimum precision will be realized when [20]: IRm ¼ ðIRu IRt Þ0:5

ð3:95Þ

Note that this equation only applies assuming that the relative uncertainties are constant, i.e. uðIÞ / I: In practice, additional considerations must also be observed. On the one hand, mass spectrometric measurement yields optimum precision at isotope ratios near unity. On the other hand, if the analyte concentration in the unknown is very low, then it may be advantageous to add a three- to 10-fold excess of tracer to avoid measuring both isotopes close to the detection limit [20]. Potentially, such overspiking could result in the introduction of analytical errors in determined concentrations, unless dead time effects are accurately corrected for [54], so care must be exercised. Mass spectrometric measurements tend to obey Poisson statistics, i.e. uðIÞ < I 0:5 at least at low signal levels, and as such, the isotope ratio yielding the best precision for the spiked unknown tends to be overestimated by Eq. (3.95). This is best illustrated by an example, taken from the excellent treatment of uncertainties in isotope dilution mass spectrometry by Adriaens et al. [22]. Three uncertainty models are considered: (a) a constant level of relative measurement variance, i.e. u2 ðIi ti Þ=ðIi ti Þ2 ¼ constant ¼ ð0:002Þ2 in this example; (b) response variability determined by Poisson (counting) statistics, i.e. u2 ðIi ti Þ ¼ Ii ti ; and (c) a linear combination of (a) and (b). The corresponding uncertainties in the isotope ratio for the mixture are then: sðIRm Þ ¼ IRm

88

"

s2 ðI1 t1 Þ s2 ðI2 t2 Þ þ ðI1 t1 Þ2 ðI2 t2 Þ2

#0:5 ¼ ½2 £ ð0:002Þ2 0:5

ð3:96aÞ


0:5 1 1 0:5 1 t þ ¼ 1 þ IRm 1 t2 I1 t1 I2 t2 I 1 t1

0:5 sðIRm Þ 1 t ¼ 1 þ IRm 1 þ 2 £ ð0:002Þ2 t2 IRm I1 t1 sðIRm Þ ¼ IRm

ð3:96bÞ ð3:96cÞ

where I1 and I2 are the count rates (counts s21) measured for total times (s) of t1 and t2 for isotopes 1 and 2, respectively. Insertion of the appropriate form of Eqs. (3.96) in Eq. (3.92) allows the uncertainty in determined analyte concentrations to be assessed as a function of the isotope ratio in the mixture. (Here it is assumed that dead time correction is unnecessary, e.g. by using a Faraday cup detector [24].) Results of the calculations are plotted in Fig. 3.16. In the case of constant proportional noise, curve (a), the optimum isotope ratio in the mixture is that predicted by Eq. (3.95). The relative uncertainty curve is, in this example, symmetrical about the optimum on a logarithmic scale [22]. This is only true, however, when the relative uncertainties in IRu and IRt are equal. If the relative uncertainty in the ratio for the spiked unknown is a function of the isotopic composition, the position of the optimum is clearly shifted toward

Fig. 3.16. Effect of the isotope ratio in the spiked unknown on the uncertainty in the estimated analyte concentration determined by isotope dilution analysis. Parameters used in the simulation were: I1 ¼ 10,000 counts s21; t1 ¼ 100 s; t1 =t2 ¼ 1; sðCt Þ=Ct ¼ 0:001; sðFÞ=F ¼ sðKÞ=K ¼ 0; sðIRu Þ=IRu ¼ sðIRt Þ=IRt ¼ 0:01; sðtÞ=t ¼ 0:25; IRu ¼ 415:7 and IRt ¼ 1:818: Curve (a) was obtained assuming the presence of proportional noise, Eq. (3.96a); curve (b) for Poisson statistics, Eq. (3.96b); and curve (c) for a linear combination of both types of noise, Eq. (3.96c).

89


the tracer composition, as shown by curves (b) and (c) in Fig. 3.16, corresponding to Eqs. (3.96b) and (3.96c), respectively. This means that a greater quantity of enriched isotopic tracer must be added to the unknown to minimize the uncertainty in the determined analyte concentration. Prior knowledge of the uncertainty structure is therefore of importance in determining optimal spiking regimes for isotope dilution analysis. It is also worth mentioning that, for the conditions used for the simulations in Fig. 3.16, the uncertainty contribution made by dead time correction was negligible. Indeed, uncertainty budgets suggest that correction for mass discrimination is often the precision-limiting factor in isotope ratio measurement [73].

Acknowledgements This work was financially supported by EUs structural fund for Objective 1 Norra Norrland and Analytica AB, Lulea˚, Sweden, http://www.analytica.se/. REFERENCES 1 2 3

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Chapter 4

Stated references for ensuring traceability of trace element analysis Ph. Quevauviller

4.1

INTRODUCTION

Measurements constitute one of the foundations of modern society. Among the billions of analyses performed every year around the world, trace element determinations are a key issue in many sectors of industrial and societal importance (health, food, environment, industrial production). Sound decisionmaking, hence, calls for measurement systems capable of producing analytical data of demonstrated quality. Analytical problems to be tackled with respect to trace element analyses are numerous, and concern a wide range of different matrices, which are analysed for a wide variety of elements and their species at various levels of concentrations. This issue is becoming increasingly complex regarding the analytical problems encountered and the pressure that laboratories are facing with respect to providing fit-for-purpose data quality. The last decade has seen an increasing awareness of the need for quality assurance (QA) in all the sectors directly relying on analytical data, which has been reflected by the development of a number of guidelines and documented standards, e.g. for managerial aspects and technical operations (e.g. sampling, method validation, etc.) and tools [e.g. reference materials (RMs), proficiency testing schemes] [1 –4]. In this context, traceability of chemical data is at the heart of the ongoing discussions. This concept is a heritage of metrology as conceived for physical measurements (e.g. mass, length, time, temperature, etc.) more than a century ago. Metrology in chemistry is now actively discussed among experts in metrology and analytical chemistry in order to propose a system that would be applicable to complex chemical measurements [5]. Recently, the application of metrology concepts to environmental analysis has also been examined [6]. The discussions generally point out that the direct application of theoretical metrology concepts to chemical measurements is not possible, because of major differences between chemical and physical measurement processes, e.g., chemical analysis results are often strongly dependent upon the nature of Comprehensive Analytical Chemistry XLI Mester and Sturgeon (Eds.) q 2003 Elsevier B.V. All rights reserved

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samples (whereas physical measurements are less or not affected). With respect to trace element analysis, a wide variety of analytical problems are encountered in relation to different elements (and their species) and matrices. Preliminary steps are necessary (e.g. sampling, sample pre-treatment) that may have an effect on the final result. When dealing with (routine) trace element analyses, these theoretical discussions seem to be very distant from real-life situations, and the practice is, in most cases, very far from theory. Even though the situation has drastically improved within the last few years, the warning made at the beginning of the 1990s [7] is still relevant: many chemists still do not pay sufficient attention to the reliability of analytical results and confuse trueness and precision. With respect to traceability, the situation is even worse and this concept is prone to many misunderstandings when applied to chemical measurements. This chapter discusses traceability of trace element analysis and examines, in particular, the various stated references to which the chemical data may be traceable. 4.2

MEANING OF TRACEABILITY FOR CHEMICAL MEASUREMENTS

ISO defines traceability as “the property of the result of a measurement or the value of a standard whereby it can be related to stated references, usually national or international standards, through an unbroken chain of comparisons all having stated uncertainties” [8]. In this definition, three key elements may be distinguished, which have been extensively discussed with respect to their applicability to chemical measurements: (1) the link to stated references, (2) the unbroken chain of comparisons and (3) the stated uncertainties. Detailed discussions have already investigated how these elements apply to chemical measurements [9 –12]. Let us now examine how they may be understood in the context of trace element analysis. In the definition, the stated references may be reference methods, RMs or SI units (kg or mole for chemical measurements) [13]. In theory, all chemical measurements should aim at being traceable to SI units. In practice, measurements correspond to approximations via comparisons of amounts, instrumental response generated by a number of particles, etc. Establishing SI traceability nowadays implies demonstrating to what extent these approximations are clearly related to the stated references [9]. As discussed below, most of the trace element analyses performed nowadays are actually traceable either to an RM [pure substances or matrix certified reference materials (CRMs)] or to a reference method (e.g. standardised method). The unbroken chain of comparison basically means that there is no loss of information during the analytical procedure (e.g. incomplete recovery, contamination). Achieving this requirement is more or less difficult according to the analytical problem considered. It will be more critical for techniques involving successive analytical steps (e.g. extraction, separation, detection in

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Stated references for ensuring traceability of trace element analysis

the case of the determination of chemical species of elements) and less acute for direct measurement procedures (e.g. sensors) which may, however, be faced with other difficulties (e.g. lack of sensitivity or selectivity). An additional difficulty, which is being faced in many instances (e.g. in the case of environmental analyses), is sample collection and storage. These two steps form an integral part of the traceability chain which is too often forgotten. The third key element, the stated uncertainties, is also a critical feature that many analysts still overlook. The theory implies that the uncertainty of a measurement is based on the traceability and uncertainty of all the stated references that contribute to this measurement. In other words, uncertainty components should be estimated at each step of an analytical process, i.e., the smaller the chain of comparison the better the uncertainty of the final result. Here again, theory is confronted by practice when dealing with complex trace element measurements (e.g. analysis of complex environmental or biological matrices). This chapter will not consider uncertainty matters, which are widely discussed in the literature [1,9,14], but will rather focus on the first two elements, which are more likely to be prone to misunderstandings. Before starting these discussions, it is useful to remind the reader that traceability should not be confused with accuracy. The latter covers the terms trueness (closeness of agreement between the “true value” and the measured value) and precision (closeness of agreement between the results obtained by applying the same experimental procedure several times under prescribed conditions). General aspects of QA of analytical measurements (including considerations on accuracy) have been extensively discussed in the literature [1] and will not be repeated here. Let us underline the fact that a method that is traceable to a given stated reference is not necessarily accurate (i.e., the stated reference does not necessarily correspond to the “true value”), whereas an accurate method is always traceable to what is considered to be the best approximation of the true value (defined as “a value, which would be obtained by measurement, if the quantity could be completely defined and if all measurement imperfections could be eliminated” [8]). At another level, precision and uncertainty are also often confused and considered to be similar concepts, which is not correct because uncertainty includes both random and systematic errors (while precision is solely linked to random errors) [10]. Trace element analyses are generally based on a succession of actions, namely (i) sampling, storage and preservation of representative samples, (ii) pre-treatment of a sample portion for quantification, (iii) calibration, (iv) final determination and (v) calculation and presentation of results. Based on this, we may now consider in further detail the types of stated references that are relevant to ensure the traceability of trace element determinations.

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4.3

SI UNITS

Units of the “Syste`me International” (SI) correspond to internationally recognised fundamental units that are used in metrology. They establish units of length (metre), mass (kilogram), time (second), temperature (Kelvin), etc. The unit that underpins chemical measurements is the unit of amount of substance, the mole. In principle, all chemical measurement data should be traceable to the mole [5]. In practice there is no “12C mole” standard, and kg is needed to define the mole [8]. Therefore, chemical measurements, in general, (and trace element analysis in particular) are actually traceable to the mass unit, the kg. Trace element analyses are based on the determination of amount of substance per mass of matrix. One should not confuse this traceability to mass units with the traceability to the “true value” of the substance in the matrix. This is discussed in section 4.6.5. 4.4

DOCUMENTED STANDARDS

Standardisation is an important aspect of routine analytical work. Documented standards (norms) related to measurement procedures are designed to establish minimum quality requirements and to improve the comparability of analytical results. They also often represent the first step of the introduction of techniques/methods into regulations. In this case, the reference is closely related to the documented protocol, representing one of the main links of the traceability chain. This aspect will be particularly acute when dealing with operationally defined parameters (i.e. parameters determined following a strict analytical protocol), e.g. fractionation of elements in soils or sediments [15], leachable trace elements from paint matrices [16], etc., as the traceability chain may be broken if the protocol is not strictly followed. Standardised procedures (documented standards) have been developed for sampling strategies and analytical techniques (documented protocols describing in detail analytical procedures, from the sub-sampling to the actual determination). In the area of environmental monitoring, the implementation of documented standards bound to regulations has been criticised (e.g. EPA methods) as standardised methods may become outdated while still being enforced by regulations that have not been revised. It may indeed happen that the analytical state of the art in a certain field has drastically improved but laboratories are still obliged to use old-fashioned inferior methods for legal reasons. Standardisation bodies have recognised this problem and now allow the progress in analytical technologies to flow into standardisation work. The use of a standardised method does not guarantee that no errors will occur; it only provides an analytical framework that is considered as the reference for a given measurement. Let us consider a specific example: the determination of extractable forms of elements using a single or sequential extraction procedure. This approach is

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widely used for soil and sediment analysis for studies on plant bioavailability or mobility of trace elements [15,17]. The measurements do not relate to specific forms of elements, but rather to amounts extracted by a given procedure and operationally defined according to interpretations such as “mobile”, “carbonatebound” forms, etc. The comparability of data is only possible if the extraction (documented) protocols are strictly followed [18], i.e., the traceability of the final results will be linked to this documented extraction protocol taken as reference. If a change in operational parameters is made by a laboratory, the traceability link will be broken. This comment applies to all the measurements that correspond to a partial extraction of a substance in a given medium. Detailed (documented) guidelines are difficult to set up for sample collection and storage which, however, remain the primary source of error (and hence one of the weakest links of the traceability chain). As an example, recommendations are available in the scientific literature with respect to environmental monitoring [3,19], but there are very few examples of documented standards that can formally be used as stated references in this field. Recent progress has been achieved in the field of soil monitoring through a systematic evaluation of sampling and sample pre-treatment procedures in the framework of an interlaboratory study; a group of laboratories actually used their own methods on a reference site and compared them with a reference method that was optimised and finally proposed as a harmonised method [20]. Sampling standards generally define the method of sampling, the number of samples to be collected, their representation, the frequency of sampling (taking into account natural variations), the sampling techniques and tools, etc. Statistical sampling tools exist [21,22] but they are often neglected and are hardly applicable to practical cases. The natures of the sample and the substance to be monitored actually dictate the choice of the sampling, which is hence adapted case-by-case. A similar situation is encountered for sample storage for which recommendations are given with respect to protection of the samples from light and elevated temperatures. This situation is obviously unsatisfactory with respect to the comparability of data as no clear stated references may be presently used. 4.5

REFERENCE METHODS

Analytical methods differ in the link between the signal produced by a given determined substance and the signal obtained from the calibration material. For a vast majority of methods, the link is usually related to an amount of substance of established purity and stoichiometry. In some fields of trace element analysis, e.g. the determination of chemical forms of elements (also referred to as “speciation” [23]), the techniques are based on a succession of analytical steps such as extraction, derivatisation, separation and detection. This multiplies the risk that the traceability chain is broken owing to lack of proper tools (e.g. RMs containing actual analysed species, secondary

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standards, etc.) to accurately determine the result, e.g. extraction recoveries, derivatisation yields, etc. [12]. For other methods, e.g. XRF, the link is through CRMs (see Section 4.6.1). So-called primary methods are methods with the highest metrological qualities for which the uncertainty can be established in terms of SI units and the result is accepted without reference to an external calibrating material. These methods have few random errors and are supposed to be exempt from systematic errors; they are also referred to as “definitive”, “absolute” or “stoichiometric” methods (e.g. gravimetry, titrimetry, coulometry of simple solutions, etc.) [9]. Using primary methods guarantees, in principle, that measurements will be traceable to SI units, i.e., traceability links will be established to the “true value” of the amount of substance. One would think that “reference methods” should hence systematically be “definitive methods”. However, these methods mainly exist for trace element determinations. For chemical forms of elements (or organic compounds), there are no real definitive (or primary) methods for the reasons expressed above (analytical steps with impossibility to firmly demonstrate full recovery). As stressed above, primary methods theoretically enable the traceability of chemical measurements to the SI unit (i.e., to the mole) to be achieved. This has been demonstrated for relatively “simple” measurements, such as trace elements in sea water, using isotope dilution mass spectrometry (ID-MS) [24]. However, what can be obtained for inorganic parameters in water samples is far from achievable for the analysis of complex organic substances and matrices requiring a series of analytical steps (e.g. extraction, clean-up, etc.). In this case, the traceability chain will be broken at several stages and the stated references will only rely on approximations (recovery estimates). The better these approximations, the closer the traceability of the measurement to the true value. In many cases (e.g. for trace organometallic determinations), true “definitive” methods do not exist for environmental measurements as there are no means at present to give proof that extraction or chemical reactions (e.g. derivatisation) have yielded a 100% recovery. As an example, ID-ICP-MS has been used for determining tributyltin (TBT) in sediment and mussel matrices after HPLC separation [25]: one could think that the measurements were then of demonstrated trueness because, in principle, no loss could occur after separation. The situation, however, was that the final results were traceable to the “true” value of TBT present in the extract but not necessarily to the true value in the sample; the link to extraction recovery simply hampers this traceability being achieved. It has been argued that the development of “reference measurement” procedures that would be adequately applicable to real sample matrices (rather than matrix-dependent methods) would be a much better trend than trying to develop thousands of matrix-matched CRMs (see Section 4.6.1) [24]. However, these “reference methods” also need to be validated, which cannot negate the necessity to develop suitable CRMs.

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Methods based on internal or external calibration rely on the availability of calibrants of high purity and verified stoichiometry but this is only the last link of the traceability chain (i.e., calibration of the detector signal). In principle, all steps of an analytical technique should be recorded in such a way that the result of the final determination is linked through an unbroken chain of comparisons to appropriate standards. In other words, firmly establishing traceability in analytical measurements means that several “primary” chemical RMs in the form of (ultra-)pure substances are interlinked by well known, quantitative, high-precision high-accuracy chemical reactions [24]. In practice, this is not achievable for a vast category of chemical measurements. In the case of the analysis of chemical forms of elements and starting from extraction, there is no way at present to firmly ensure that a substance has been fully recovered from a complex matrix; methods that are generally used (e.g. successive extractions or spiking procedures) enable the estimate of method reproducibility but do not necessarily demonstrate full recovery. For methods including a derivatisation step, there are few or no appropriate calibrants available to date to check the yield of derivatisation reactions, which represents an additional gap in the traceability chain. As a conclusion, these methods are dependent on a number of more or less well-controlled parameters that may vary from one sample to another. As stressed below, the analytical steps that rely on a recovery estimate can only be validated in comparison to independent methods, giving a good indication on data comparability but not necessarily on accuracy. Hence, few of these methods may be considered as reference methods unless they are documented down with a great level of detail, describing all the analytical operations and the limits of applicability of the methods. This is the case of “official methods” that are required regulations. These are faced with the problem discussed above regarding documented standards (possible risks of becoming outdated).

4.6

REFERENCE MATERIALS

4.6.1

The various categories of materials and related requirements

The role of RMs is, in principle, well known. Certified Reference Materials may be calibration materials (pure substances or solutions or materials of known composition for techniques requiring matrix-matched calibrants, e.g. XRF) or matrix materials representing as far as possible “real matrices” for the verification of measurement processes. They must meet certain requirements before being accepted as references by the analytical community: †

Homogeneity has to be verified to ensure that all sub-samples of the RM taken for measurement will produce the same analytical result within the stated measurement uncertainty. This requires a verification of the within-

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†

†

†

unit and between-unit for each batch of RM produced. The product must also specify the minimum sample intake for which homogeneity has been measured and may be guaranteed. Finally, the ease of re-homogenising the material after packaging must be taken into consideration. Stability is another important requirement, i.e., producers must test the stability of the RM and its sensitivity to light, humidity, microbial activity, temperature, time, etc., and state the usable lifetime of the RM. Long-term testing is required to validate the stability of a material under a variety of storage and transport conditions. The similarity of the RM to the real sample should, in principle, be as close as possible in terms of matrix composition. This is not always achievable in practice, and compromises are sometimes necessary (e.g. production of synthetic samples mimicking the composition of natural samples). Accuracy, uncertainty and traceability of certified values are other crucial parameters which have to be duly considered at the stage of certification. They are discussed below.

Laboratory reference materials (LRMs) [also known as quality control materials (QCMs)] have the same basic requirements of representativeness, homogeneity and stability as CRMs. The distinction of these “LRMs” or “QCMs” with CRMs is only related to the fact that these materials are not certified and are generally produced on a much smaller scale, e.g., for interlaboratory studies or internal quality control (control charts), i.e., to monitor the performance of analytical methods with time (reproducibility) through the establishment of control charts [26]. In this view, control charts and related RMs may be considered as long-term stated references for analytical measurements. It has been stressed that the “reference” represented by an RM may not always be reliable because, in many cases, the RM does not have the “same” matrix as the unknown sample [24]. This is discussed in section 4.6.5. 4.6.2

Production

Several recent books have been written describing the requirements and procedures for the production of RMs in general [4] and for environmental science in particular [2,27], of which only the main elements are presented in this chapter. The general steps for the production of an RM are summarised in Table 4.1, which illustrates that the preparation of an RM requires substantial planning and appropriate production facilities. There are obvious differences between producing a small batch of RM for internal quality control of a laboratory and a large amount of material for certification purposes (requiring half-industrial capacities), and the producer needs to be equipped accordingly. Various equipment for sampling, treatment (e.g. grinder, milling devices, etc.), packaging, stabilising procedures, etc. are described in the literature [2,4].

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Stated references for ensuring traceability of trace element analysis TABLE 4.1 General steps in the production of an RM (adapted from Stoeppler et al. [4]) 1. Definition of the RM, including the matrix, the properties to be certified and the desired levels 2. Design of the sampling procedure 3. Design of sample preparation procedure 4. Selection of an appropriate method for homogeneity and stability testing 5. Design of the characterisation of the RM 6. Acquisition of the samples 7. Preparation of the samples 8. Homogeneity testing 9. Stability testing 10. Characterisation of the RM 11. Combination of the results of homogeneity and stability tests and of the characterisation for calculation of the expanded uncertainty 12. Establishment of certificate and, if appropriate, preparation of a certification report

Once a sample is acquired, the producer may start to prepare the material in the state it will be used. It is particularly important at this stage to stabilise the material (if required) to prevent possible changes in composition of critical components and to homogenise the material so that all future sub-samples will be as identical as possible. If additional treatment is needed (e.g. reduction of grain size), it should occur at this stage. At this point, a preliminary assessment of the homogenisation process should be performed. As one of the main goals of RM production is to provide a stable material, stability tests should begin at an early stage of the production process. Ideally, these tests should be conducted over the expected lifetime of the RM prior to its distribution. The packaging of the homogeneous and stable material will be conducted according to the analyst’s expectations, i.e., some materials will be packed in large units (e.g. bottles containing 50 g of material allowing several analytical repetitions) whereas in some cases materials may be packaged as single-use samples (e.g. in the case of potentially unstable properties). Additional uniformity assessment will be performed after the packaging (e.g. grain size, colour), which will be followed by the analysis of the material for the analyte(s) of interest for certification purposes. The certificate of analysis will be issued at the end of the overall process. Stability testing should, in principle, be continued over the useable lifetime of the RM, depending on its inherent stability and also on its rate of use.

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4.6.3

Methods used for material characterisation or certification

There are several accepted methods for characterising and producing reference values of RMs and/or certified values of CRMs. The more widely accepted methods include: † †

†

Certification using one definitive method, used when a primary method is available and m for the purpose of the certification analyses. An example of such a method for trace element analysis is ID-MS. Certification through interlaboratory testing, in which the reference/certified values are obtained by pooling results from several laboratories (having demonstrated their quality) and using laboratory means for the calculation of the values. Certification using at least two independent methods.

Combination of the above approaches are often used to certify RMs, e.g. using two or more independent methods in the frame of an interlaboratory testing programme, after which the data are combined to obtain the reference/certified value. Note that the calculation of the uncertainty of reference/certified now takes into account ISO recommendations for assessing expanded uncertainties that include contributions from homogeneity and stability tests, characterisation results and results of certification work [4]. Let us note that official organisations attempt, wherever possible, to produce RMs estimating the true values as closely as possible, following the above-mentioned approaches. In the case of matrix CRMs, this is mainly achieved by employing a variety of methods with different measurement principles in the material certification study; if these methods are in good agreement, one may assume (but not firmly demonstrate) that no systematic error has been left undetected, and the reference (certified) values are the closest estimate of the true value. This approach possibly includes definitive methods (see above), which seldom exist for analyses involving an extraction or derivatisation step. In many instances, consensus values are accepted as true values reflecting the state of the art (hence ensuring data comparability). Discussions are ongoing on the fact that many matrix CRMs do not guarantee a full verification of accuracy owing to possible remaining bias (e.g., all extraction methodologies, although being in good agreement, could be biased to a certain degree with no means to demonstrate it at the present stage). This is a point of discussion in section 4.6.5. 4.6.4

Use of reference materials

The use of RMs is also widely described in the literature [4,28], e.g. for validation purposes, interlaboratory testing, control charting, etc., and the reader is encouraged to consult these works directly if additional information is needed. As mentioned above, some (certified) RMs are intended for calibration

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purposes; in these cases, the uncertainty of the certified value is of prime importance as it will affect the final uncertainty of the measured value in the unknown sample. In the case of certified pure substances or calibrating solutions, the uncertainties of certified values are usually negligible in comparison with the method uncertainty. This is not the case with matrix CRMs that are, in principle, reserved for the validation of methods; these materials are used for calibration purposes for non-destructive methods (e.g. XRF) and the larger uncertainty of the certified values may lead to a too large uncertainty of the final results (thus leading to semi-quantitative measurements). 4.6.5

Traceability of reference materials

In the field of trace element analysis, the wide variety of matrices and substances encountered calls for a large availability of matrix RMs representative from various sample types (e.g. sediments, soils, plants, waters, biological tissues, industrial products, etc.). Each of these sample types has a wide range of samples, e.g., for a soil—clay, sandy, humic-rich and calcareous soils). Reference materials represent “physical” stated references to which measurements can be linked. As mentioned above, this traceability is often criticised because the requirement of matrix similarity between unknown samples and matrix CRMs is never achievable in practice and often compromises have to be found. It should be stressed that a correct result obtained with a matrix CRM does not give full assurance that “correct results” will be achieved when analysing unknown samples, owing to differences in matrix composition [12]. The question of traceability of matrix CRMs (representing complex chemical systems) to SI units, and hence their values as “reference”, is an ongoing debate. Traceability implies “an accurate realisation of the unit in which the property values are expressed”. Similar to the achievement of traceability to SI units, the “accurate realisation” is often hardly demonstrated in practice. Indeed, as underlined in Section 4.5, it is difficult to demonstrate, e.g., that a 100% extraction recovery has been obtained for a given substance in a complex environmental matrix. The assumption of a 100% recovery will actually be more valid if the certified values have been obtained in the frame of interlaboratory studies using a variety of (different) techniques. Even though, in the absence of “primary” (or “definitive”) methods, the collaboratively obtained value may only be considered as a “consensus value”, reflecting the state of the art of a given method. This consensus value represents an excellent reference to achieve traceability in a given area, but does not necessarily correspond to the “true value” (which is actually not quantifiable in most complex environmental measurements). In addition, there are numerous fields of environmental monitoring for which RMs are lacking (or are available but with a matrix too far removed from

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the analysed samples) or cannot be prepared owing to their instability. This hampers traceability being achieved. In this case, other approaches have to be followed (e.g. interlaboratory studies, see below). In the case of good correspondence between the matrix of samples and the matrix of CRMs, this reference is certainly the most appropriate one to check the accuracy of analytical methods and compare the performance of a method with other methods (or other laboratories). Similar comments with respect to representativeness may be made concerning matrix LRMs (not CRMs, see Section 4.6.1) used for internal quality control purposes (establishment of control charts). In order to clarify the traceability links represented by RMs, a classification has been established, categorising the various types of materials [29], as shown in Table 4.2. In this classification, primary RMs are traceable to SI units through primary methods and physical standards (i.e., mass standards); CRMs and LRMs certified or reference values are obtained by reference or validated methods, but they are not mentioned to be necessarily traceable to SI units. The TABLE 4.2 Classification of chemical RMs (adapted from Pan [29]) Level

Appellation

Criteria

I

Primary RM

II

Certified RM

III

Working RM (or LRM or QCM)

Materials with the highest metrological qualities, whose values are determined (certified) by a primary method Developed by a national metrological institute Recognised by national decision Traceable to SI units and verified by international intercomparisons Fulfil the ISO Guide 30 definition Generally developed by a national reference laboratory or a specialised organization Certified by reference methods, by comparisons of different methods or a combination of the two approaches Recognised by national or specialised organisations Accompanied by a certificate indicating the uncertainty of the certified values and describing the traceabilty Fulfil the ISO Guide 30 definition Produced by an accredited organisation Establishment of reference values by one or more validated methods Accompanied by a description of the achieved traceability and giving an estimate of the uncertainty

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table suggests a traceability link between LRMs, CRMs and primary RMs, which may be achieved in some cases but not as a general rule, in particular for matrix RMs as discussed above. In other words, even if primary RMs are used for calibrating an analytical method used for obtaining certified (or reference) values, the uncertainties that may remain, e.g. on recoveries, do not allow one to firmly establish traceability to the primary RMs (and hence to SI units). This is discussed in one of the case studies below. 4.7

SPECIMEN BANKING

Specimen banking is another type of stated references that may be used in relation to trace element analysis. A speaking example of this application is environmental monitoring. The approach consists of collecting environmental samples, processing them and storing them on a long-term basis under conditions which prevent any significant changes in their chemical composition [30]. The aim is to create a systematic repository of samples, providing information about current levels of pollution and tools to evaluate contamination trends. This approach certainly represents the best referential system for long-term environmental monitoring, enabling use of specimens as stated references for possibly repetitive analysis focusing on specific contamination studies (e.g. when more sophisticated (accurate) analytical methods will become available). Besides specimens that are “true” stated references of the environment status at the time, RMs can also be produced from surplus specimen material (i.e., stabilising them, e.g. by freeze-drying) in order to monitor the reproducibility of analytical techniques, hence ensuring internal quality control using the most representative samples [28]. “Fresh” RMs may also be prepared from samples similar to collected specimens and processed the same way (i.e., in an uninterrupted cryo-chain to preserve their integrity), homogenised and stored as fresh powder materials for the purpose of developing new analytical procedures, optimising existing methods, internal quality control and stability experiments of environmental specimens [31]. 4.8

PROFICIENCY TESTING

Participating in interlaboratory studies (or proficiency testing, which is the equivalent term used in regulations) is a way for laboratories to establish stated references for evaluating the performance of their methods. These exercises imply that one or more materials are distributed to several laboratories for the determination of given substances. The comparison of different methods enables the detection of possible sources of errors linked to a specific procedure or the way a method is applied by a given laboratory. Exercises focusing on a single method enable the establishment of performance criteria (e.g. precision).

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The stated references, here again, may be RMs that should meet homogeneity and stability requirements (see Section 4.6). However, in contrast to RMs used for internal quality control, proficiency testing may involve samples with a limited shelf life that are distributed to laboratories for analysis of particular parameters that could not be evaluated using stabilised RMs. Examples include “fresh” materials (e.g. biological samples) treated with a short-term preservation period. Similarly to what has been discussed for RMs, the measurement values obtained in relation to interlaboratory studies (using different techniques) are taken as the “best representation of the state of the art”, i.e., offering an excellent means for laboratories to achieve comparability (i.e., traceability) of their results to a recognised reference, which is in this case a consensus value (generally the mean of laboratory means). This reference does not enable traceability to the true value of the substance in the medium to be achieved, but it represents a very useful method for achieving comparability of environmental measurements. 4.9

4.9.1

REAL-CASE ACHIEVEMENT OF TRACEABILITY OF TRACE ELEMENT ANALYSIS Total trace element determinations

The first example corresponds to the most “simple” system that may enable the traceability of trace element analysis to the SI unit to be achieved. Traceability of trace element determinations in various matrices to the mole is, in principle, achievable using primary methods, such as ID-MS. This does not infer that accurate results cannot be obtained with other methods (e.g. spectrometric methods relying on external calibration), but the traceability chain will be more prone to systematic errors in the case of calibration-dependent methods in comparison to primary methods. The example below, therefore, uses ID-MS as primary method and examines how traceability may be achieved in the framework of a “real-case” analysis. Figure 4.1 distinguishes four steps, separated into an operational part (sampling to detection) and a reference part representing the traceability to stated references. (A) Sampling is recognised to represent the major contribution to analytical errors. This has been clearly demonstrated in some areas, e.g. sampler contamination hampering use of trace element data in oceanic waters produced over the years 1965–1986 [32]. The high variability of results also depend on the way samples are collected, as illustrated by systematic comparisons of sampling methods used for trace element analysis in soils [20]. Considerable improvements in sampling methodologies, and in some instances standardisation, have permitted minimisation of possible contamination and losses of trace elements. The “stated reference” at

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Fig. 4.1. Example of traceability chain for total trace element determinations.

this stage, hence, relies on the experience gained in the past and documented guidelines (standardised or not) that have been adopted as a consensus. It is not possible to demonstrate that all contamination sources have been avoided (explaining the dotted line in the reference links), but this is the best consensus that may be achieved at the present stage for the analysis of most matrices. A way to control the likelihood of contamination is to process a blank sample using the same procedures and materials as used for the unknown sample. (B) Similar to sampling, sample pre-treatment and storage represent a high risk of contamination and procedures are generally tested to minimise these sources of errors, in particular the sample stabilisation and storage. Here again, one has to rely on the common experience to assume that the traceability has not been drastically affected at this stage. Errors can be due to the addition of acids of insufficient purity (e.g. when used to stabilise water samples at the collection stage), errors of manipulation, etc. As observed in step A, the reference link is also weak in this case as it is hardly possible to firmly identify all errors that may have occurred on the field.

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(C) This step corresponds to the laboratory work, in this case determination of the trace elements by ID-MS. The method is considered to be a primary method as it is based on the addition of known amount (determined by weighing) of the analyte in an isotopic composition different from that of the analyte present in the sample. Spiking is achieved prior to chemical treatment of the sample and has to be performed in such a way that an equilibrium is reached between the spike and the isotopes naturally present in the sample. Considering the similarity between the isotopes, the chemical treatment does not affect the isotopic ratio even if the analyte recovery is not complete. Possible contamination or loss has no effects on the traceability of the result, as the analyte and spike will undergo the same pattern and the ratio will not be affected. (D) The amount of analyte is related to the amount of the spiked isotope according to a known formula [33]. The calculation is only based on isotopic ratios that may accurately be measured by mass spectrometry. The measurement is hence considered to be traceable to the mole. In the context of trace element analysis in various matrices, using ID-MS as a determination technique, the weakest parts of the chain are hence the sample collection and pre-treatment steps. An improvement in the traceability chain could be achieved if the isotope spiking was to be conducted immediately after collection, i.e., at the pre-treatment stage (step B). Isotope dilution mass spectrometry measurements, in theory, guarantee the traceability of the pretreated sample to the SI unit. However, an example of application of this technique in interlaboratory studies on trace elements in sea water has shown that even if this method is considered as a primary method, it is not without errors owing to the operating difficulties; an example has been published, showing that 5 laboratories out of 16 using ID-MS reached the required performance for the certification of a sea water CRM [34]. As a conclusion, one may realise that even a relatively “simple” system with respect to the analytical measurements is not exempt from sources of errors, either due to possible contamination occurring, e.g. at the sampling stage (with few means to firmly demonstrate the lack of contamination, in particular, for the sampling itself) or manipulation errors at the determination stage (due to the operating complexity of ID-MS).

4.9.2

Operationally defined trace element determinations

The second example deals with the determination of “extractable” contents of trace elements, i.e., analyses based on a given operational protocol (e.g. single or sequential extraction scheme, or leaching procedure). In this case, all steps prior to the analytical work are basically the same as for total trace element determinations, but the pre-treatment actually corresponds to a strictly

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defined protocol (that may be an official standardised method), representing a key “stated reference”. Traceability of determinations relies, in this case, on the proper application of the operational protocol and, of course, on the analytical measurement itself (final detection of trace elements in the extract or the leachate). Figure 4.2 gives an account of the different steps, with indication of the references linked to traceability. (A,B) (C)

These steps are basically similar to their equivalent steps in Section 4.9.1. As stressed above, the laboratory work will start by an extraction (or leaching) procedure, strictly following a written procedure. The traceability will hence rely heavily on compliance to the written procedure (errors being made if different reagents are used or if the extraction/ leaching scheme is not applied as written). Trace element contents in the extract or leachate may be determined by ID-MS (as

Fig. 4.2. Example of traceability chain for extractable trace element determinations (based on the use of a single or sequential extraction scheme).

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(D)

discussed in step C of Section 4.9.1), even if this is not a current practice, i.e., final trace element determinations are generally conducted by spectrometric methods. If spiking with a known amount of the analyte having an isotopic composition different from that of the analyte is achieved prior to the extraction procedure, possible contamination or losses will have no effect on the result traceability of the trace element contents in the extract. If we consider the possible ID-MS determination of trace elements in the extract, the calculation will be based on isotopic ratios (similar to step D of Section 4.9.1) and the measurement of extractable content will hence be considered to be traceable to the mole. In other words, we will be able to determine the “true value” of the trace elements in the extract, which does not necessarily mean that traceability to a welldefined chemical form is achieved (i.e., traceability to the mole is hardly achievable).

This particular case represents a transition between the total trace element analysis and the next example, corresponding to well-defined chemical forms of elements. This type of measurement is only comparable from one laboratory to another if a strictly followed standardised procedure is applied. Examples exist of procedures that have been adopted as a consensus method, e.g. single and sequential extraction procedures for trace element analysis in soils and sediments [15], or internationally adopted standardised procedures, e.g. the aqua regia extraction (ISO 11466 Standard) for the determination of “pseudo-total” contents of elements, and many others such as the EN 71-3: 1994 Standard for the evaluation of toxic trace element mobility from paints in the context of toy testing [16]. Very few of these schemes are, however, backed up by relevant CRMs (i.e., certified for their extractable trace element contents, following defined protocols). The references [15,16] give examples of such CRMs, namely for soil and sediment analyses, and paint analysis.

4.9.3

Determinations of chemical forms of elements

In the case of determination of chemical “species”, the substance of concern is more complex (e.g. elements in different oxidation states, organometallic compounds) and requires a more sophisticated measurement approach (Fig. 4.3). The element species or compound is prone to possible alteration (change of oxidation state or degradation of organometallic compound) if insufficient care is taken at the sample collection and storage steps. Analytical procedures have been extensively reviewed for speciation analyses [23,35,36] but, as discussed below, the measurement traceability chain presents many weak links in comparison to the first case study.

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Fig. 4.3. Example of traceability chain for the determination of chemical forms of elements.

(A,B)

In contrast to total trace element analysis, there are no formal recommendations regarding sample collection (step A) and storage (step B) for the analysis of chemical species in various matrices. A laboratory will only rely on published procedures, which are often described along general lines, lacking details on QA. Procedures will hence be adapted case-by-case, following “home-made” approaches, which are often hardly comparable sensu stricto from one laboratory to another and do not allow firm establishment of traceability owing to the lack of well-defined stated references. In other words, the data

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(C)

(D)

(E)

(F)

produced on the basis on “in-house-made recipes” may or may not be of good quality. There is simply no means to anchor them to a firm reference system. Extraction methodologies also vary considerably from one laboratory to another. The extraction recoveries are, in principle, calculated, but here also there is no real consensus as to the approach to be followed [37]. One may say that there is no real need of setting up documented extraction protocols that could rapidly become outdated with the constantly improving analytical methodologies and it is only necessary to demonstrate that the methods used are validated. This validation is indeed possible on the basis of existing matrix CRMs [2]. The recovery check is, however, prone to the uncertainties discussed in Section 4.6 with respect to the “similarity” of matrix composition between the CRM and the unknown. Therefore, the traceability link exists but it is still questionable. Derivatisation reactions (e.g. hydride generation, Grignard reactions) are frequently used for the determination of chemical species in environmental matrices [38]. The traceability chain implies, in principle, that the derivatisation yields are verified, which is only possible on the basis of available “secondary” standards, i.e., pure derivatised calibrants (e.g. ethylated, pentylated, etc.). This validation has been followed in the case of certification of RMs for TBT [39] but it is far from being a routine practice. As in step C, the completeness of the derivatisation yield will be evaluated on the basis of analysis of matrix CRMs, with the already expressed limitations. Separation will be necessary to isolate the different organotin compounds from possible interfering compounds from the matrix. Selectivity is the key feature here. The risk of losing traceability is related to a possible degradation of the compounds (heat-induced degradation on the column) or an insufficient selectivity. Internal standards with a composition close to the determined compound are useful stated references to detect possible losses or insufficient species separation. This part of the traceability chain is considered to be reasonably under control. High-purity primary standards for the calibration of chemical forms of elements are generally commercially available (except in some analytical fields, e.g. chemical species of As) and this final link of the traceability chain (detection) is considered to be satisfactory.

This case study shows that a “real case” monitoring exercise is subject to many questions with respect to measurement traceability of chemical forms of elements. The weakest links are certainly the sample collection and storage, for which no strong reference system exists. The situation is better with regard to the analytical measurements, even if the links are not considered to

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be that strong (e.g. with respect to evaluation of analyte recoveries). At present, one must admit that we are far from being able to firmly demonstrate measurement traceability to the “true value” of chemical forms of elements in many instances. 4.10

CONCLUSIONS

Trace element analyses can only be valid if the data are obtained under a reliable QA regime. Comparability of data is mandatory for evaluating spatial and temporal contamination trends, studying bio- or geo-chemical pathways, assessing product quality, evaluating risks, etc., and this is only achievable if harmonised approaches are considered, from sampling to final detection, for the analysis of given substances in given media. Without the demonstration of data traceability to any kind of well-documented stated references, a considerable amount of data published in the scientific literature is actually totally useless and this represents a huge waste of resources. As a final word, one should not confuse the search for traceability to welldefined and accepted stated references to the achievement of accuracy. The first concept is a moving feature, i.e., stated references may evolve with progress of knowledge and technical capabilities, while still maintaining comparability of data, but it does not necessarily mean that the resulting data are accurate (i.e., close to “true value”). This is partly compatible with the metrological principle of traceability which implies that “if the traceability of measurements is claimed to be other than the mole unit itself, but rather through a procedure, material or standard, then they must be credibly described and their relation to the mole clearly established” [24]. Indeed, the traceability of trace element analyses can be demonstrated to pure calibrating substances, CRMs or documented standards, the latter two often corresponding to “consensus” values and not “true” values, except in some specific cases (e.g., certified trace element contents in water matrices). Demonstrating traceability of an amount of substance to its true value in a given matrix is, therefore, hardly achievable in practice. We are in a world of compromises and the best compromise to date for many analytical issues is to achieve the best possible comparability of data both spatially (e.g. for trend studies) and geographically (between-laboratory comparability), which relies on physical tools (RMs and, in some cases, specimens). With respect to trend studies, this comparability quest should respond to progress in analytical sciences so that the stated references may be regularly improved while still maintaining a traceability chain with “old data”. Indeed, if analytical progress actually enables us to refine the determination of certified (or reference) values, approaching their closeness to the true value, links with measurements conducted decades ago (but verified with RMs of lesser confidence) will still be possible and data will not be lost. In other words, small detected biases (detected with more advanced techniques) could be

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corrected in the future if a system of RM banking is implemented. Therefore, one may hope that progress in analytical chemistry will be such within the next decades that the accuracy of measurements will be firmly demonstrated, i.e., traceability to the true amounts of contaminants in the environment will be established.

REFERENCES 1 2

3 4

5

6 7 8 9

10 11 12 13 14 15

16 17

18 19

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H. Gu¨nzler (Ed.), Accreditation and Quality Assurance in Analytical Chemistry. Springer-Verlag, Berlin, 1996. Ph. Quevauviller and E.A. Maier, Certified Reference Materials and Interlaboratory Studies for Environmental Analysis—The BCR Approach. Elsevier, Amsterdam, 1999. D. Barcelo´ (Ed.), Sample Handling and Trace Analysis of Pollutants, 2nd edn. Elsevier, Amsterdam, 2000. M. Stoeppler, W.R. Wolf and P.J. Jenks (Eds.), Reference Materials for Chemical Analyses—Certification, Availability and Proper Usage. Wiley-VCH, Weinheim, Germany, 2001, ISBN: 3-527-30162-3. B. King, M. Walsh, K. Carneiro, R. Kaarls, V. Komppa, C. Nieto de Castro and J. Lexow, Metrology in Chemistry—Current Activities and Future Requirements in Europe, EUR Report, EUR 19074 EN, European Commission, Brussels, 1999, ISBN: 92-828-7465-6. Ph. Quevauviller and O.F.X. Donard, Trends Anal. Chem., 20 (2001) 600. B. Griepink, Fresenius J. Anal. Chem., 338 (1990) 360. ISO, International Vocabulary of Basic and General Terms in Metrology, 2nd edn. International Standardisation Organisation, Geneva, Switzerland, 1993. M. Valca´rcel, A. Rıós, E. Maier, M. Grasserbauer, C. Nieto de Castro, M.C. Walsh, F.X. Rius, R. Niemela¨, A. Voulgaropoulos, J. Vialle, R. Kaarls, F. Adams and H. Albus, Metrology in Chemistry and Biology—A Practical Approach, EUR Report, EUR 18405 EN, European Commission, Brussels, 1998, ISBN: 92-828-4049-2. M. Valca´rcel and A. Rıós, Anal. Chem., 65 (1999) 78A. M.C. Walsh, Trends Anal. Chem., 18 (1999) 616. Ph. Quevauviller, J. Environ. Monit., 2 (2000) 292. B. King, Analyst, 112 (1997) 197. A. Maroto, R. Boque´ and F.X. Rius, Trends Anal. Chem., 18 (1999) 577. Ph. Quevauviller (Ed.), Methodologies for Soil and Sediment Fractionation Studies. The Royal Society of Chemistry, Cambridge, United Kingdom, 2002, ISBN: 0-85404453-1. P. Roper, R. Walker and Ph. Quevauviller, Fresenius J. Anal. Chem., 366 (2000) 289. A.M. Ure and C.M. Davidson (Eds.), Chemical Speciation in the Environment. Blackie Academic and Professional, London, United Kingdom, 1995, ISBN: 0-75140021-1. Ph. Quevauviller, Trends Anal. Chem., 17 (1998) 289. Ph. Quevauviller (Ed.), Quality Assurance in Environmental Monitoring— Sampling and Sample Pretreatment. VCH, Weinheim, Germany, 1995, ISBN: 3-527-28682-9.

Stated references for ensuring traceability of trace element analysis 20

21 22 23

24 25 26 27 28

29 30 31 32 33 34 35 36 37 38 39

G. Wagner, Ph. Quevauviller, A. Desaules, H. Muntau and S. Theocharopoulos (Eds.), Comparative Evaluation of European Methods for Sampling and Sample Preparation of Soils, Special issue of Sci. Total Environ., 2001, p. 264. F.M. Garfield, Quality Assurance Principles for Analytical Laboratories. AOAC International, Arlington, USA, 1991. P.M. Gy, Mikrochim. Acta, II (1991) 457. L. Ebdon, L. Pitts, R. Cornelis, H. Crews, O.F.X. Donard and Ph. Quevauviller (Eds.), Trace Element Speciation for Environment, Food & Health. The Royal Society of Chemistry, Cambridge, United Kingdom, 2001, ISBN: 0-85404-459-0. P. de Bie`vre, in: H. Gu¨nzler (Ed.), Accreditation and Quality Assurance in Analytical Chemistry. Springer, Berlin, Germany, 1996. S.J. Hill, L.J. Pitts and A.S. Fisher, Trends Anal. Chem., 19 (2000) 120. T.H. Hartley, Computerized Quality Control: Programs for the Analytical Laboratory, 2nd edn. Ellis Horwood, Chichester, 1990. Ph. Quevauviller, Mate´riaux de Re´fe´rence pour L’environnement. Tec&Doc Editions, Paris, France, 2002, ISBN: 2-7430-0579-3. V. Barwick, S. Burke, R. Lawn, P. Roper and R. Walker, Applications of Reference Materials in Analytical Chemistry. The Royal Society of Chemistry, Cambridge, United Kingdom, 2001, ISBN: 0-85404-448-5. X.R. Pan, Metrologia, 34 (1997) 35. H. Emons, J.D. Schladot and M.J. Schwuger, Chemosphere, 34 (1997) 1875. T.-M. Sonntag and M. Rossbach, Analyst, 122 (1997) 27. G. Topping, Sci. Total Environ., 49 (1986) 9. W. Richter, Accredit. Qual. Assur., 2 (1997) 354. B. King, Analyst, 112 (1997) 197. Ph. Quevauviller, Method Performance Studies for Speciation Analysis. The Royal Society of Chemistry, Cambridge, United Kingdom, 1998, ISBN: 0-85404-467-1. K.L. Sutton and J.A. Caruso (Eds.), Elemental Speciation—New Approaches for Trace Element Analysis. Elsevier, Amsterdam, 2000. Ph. Quevauviller and R. Morabito, Trends Anal. Chem., 19 (2000) 86. R. Morabito, P. Massanino and Ph. Quevauviller, Trends Anal. Chem., 19 (2000) 113. Ph. Quevauviller, M. Astruc, R. Morabito, F. Ariese and L. Ebdon, Trends Anal. Chem., 19 (2000) 180.

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Chapter 5

Detection methods for the quantitation of trace elements Les Ebdon, Andrew S. Fisher, Maria Betti and Maurice Leroy

5.1

INTRODUCTION

This chapter serves as a general introduction to the methods of trace element determination discussed throughout this book. Brief overviews of these instrumental techniques will be given, along with discussions of their analytical capabilities, requirements of the sample, sample throughput, figures of merit and descriptions of the numerous methods of sample introduction. In addition, a brief overview of some of the sample preparation methods and sample manipulation procedures will also be given. Included in the chapter are selected examples of applications, although many of these will be treated in more detail throughout later chapters in this volume. It is worth noting that many of the sample introduction methods for the more frequently used atomic spectrometric techniques are common to all. Therefore, the description of the theory behind them will only be given once and, thereafter, applications of each will be given for the other methods of detection. 5.2

CLASSICAL METHODS

Classical methods of analysis will be dealt with only very briefly here since it is largely outside the scope of this chapter. It should be noted, however, that titrations are still an important part of an analyst’s armory because an “EDTA” titration readily provides traceability to a primary standard. Titrations can be relatively time consuming and do not usually offer very great sensitivity, so are of limited use for many sample types. For those applications where the analyte is present at an appreciable concentration (contaminants at the 0.01–5% m/m range in metallic samples, or even the major constituent), a titration can offer very accurate and precise results. As an example, inspection of the certificate for the reference material BCS 177/2 lead-base white metal (available from the Bureau of Analysed Samples, Middlesbrough, UK) Comprehensive Analytical Chemistry XLI Mester and Sturgeon (Eds.) q 2003 Elsevier B.V. All rights reserved

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indicates that lead (present at an average of 84.5%), antimony (10.1%) and tin (5.07%) were all determined by different titrimetric methods. In addition, three analysts determined arsenic (0.05%) by titration. A sound knowledge of the chemistry of both the sample and of the analytical method is required to prevent interferences. A useful textbook covering many of the classical “wet-chemical” methods is “Vogel’s Textbook of Quantitative Chemical Analysis” [1] which contains an assortment of titrimetric, gravimetric, potentiometric, electrogravimetric, spectrophotometric and amperometric methods. As well as giving the basic theory behind each of the techniques, it also gives experimental details for some selected applications.

5.3 5.3.1

FLAME SPECTROMETRY Introduction

Flame spectrometry, either atomic absorption spectrometry (AAS) or atomic emission spectrometry (AES), are amongst the most simple and inexpensive of the instrumental methods of trace element analysis. The cost of a basic AAS instrument can be less than US $10,000, although for the more powerful computer controlled instruments containing autosamplers, the cost can easily be double this. A flame photometer (a very basic AES instrument) that can be used to determine analytes such as lithium, sodium and potassium costs even less. 5.3.2

Theory

A detailed description of the basic theory of AAS and AES is not required here; a detailed and theoretical description of the processes within the flame (or plasma for emission) may be found elsewhere [2]. The relaxation of electrons in an analyte atom from different excited energy levels populated by (thermal) flame processes back to the ground state will yield photons of light of different energy, i.e., the wavelength of the light emitted will be different for each transition. A characteristic spectrum for each element will therefore arise. The transition probability governs the sensitivity of a wavelength (an analytical line). If the probability of a transition is low, then the number of analyte atoms (or ions) in which the electrons are excited to that energy level will also be low. This means that the number of atoms/ions emitting light at the corresponding wavelength will be relatively few and hence the overall sensitivity will also be low. Therefore, to obtain a detectable signal, the concentration of that analyte in the sample will have to be higher. Each line of an analyte, therefore, has a different sensitivity. This can be useful analytically, because each line will have its own linear range. If the expected range of

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concentration of an analyte in a sample is known, then the most appropriate analytical line may be chosen, thereby negating the need for large sample dilutions and hence keeping sample manipulation to a minimum and decreasing the likelihood of dilution errors or contamination. In atomic absorption, the amount of light absorbed from an incident beam of light is proportional to the number of analyte atoms in the optical path, and hence to its concentration in the sample. As for all absorption-based techniques, the path length also has an effect on the sensitivity. Typically, a 10 cm path length is used for an air–acetylene flame, but there is no reason why a smaller path length (5 cm, used for nitrous oxide –acetylene), could not be used, which would lead to half the absorbance of that using the larger burner. This also has the effect of extending the linear range by a factor of two. As in atomic emission, numerous wavelengths are available for each analyte and these will each confer a different sensitivity. A comprehensive theory behind atomic absorption may be found elsewhere [3]. It is worth noting that true spectral interferences for atomic absorption are very rare. This is because of the “lock and key” effect of the incident radiation and the analyte atoms. Theoretically, there are no other atoms present in the atom cell that should absorb the radiation and hence false high signals should not be obtained. Unfortunately, the presence of particulate matter or of some molecules may lead to absorption of the light. Under circumstances such as these, erroneously high signals may then be obtained. 5.3.3

Instrumentation

As discussed above, a light source is required to excite the analyte atoms when using the AAS technique. There are two common types of light source, of which the line source hollow cathode lamp (HCL) is the more frequently used. It has a cup-shaped cathode coated internally with (or fabricated from) the analyte of interest. Often, only one element is used per lamp, but multi-element lamps are also commercially available that may contain two, three or perhaps even five or six elements. Although multi-element lamps are more expensive than single element ones, they have the advantage of being less expensive than investing in five or six individual lamps. Their disadvantage is that often compromise operating conditions must be used, which may have an unfavorable effect on the signal-to-noise ratio and linear range for some of the analytes. The other light source commonly used is the electrodeless discharge lamp (EDL). These are more expensive to purchase but offer an increased light intensity and for some analytes, e.g., arsenic, and provide for higher sensitivity and enhanced detection limit. Sample is usually, but not always (see Section 5.3.6), introduced to the instrument as a liquid via a nebulizer/spray chamber assembly. As the gas used for combustion passes the end of a capillary, a pressure drop is obtained. If the other end of the capillary is immersed in a liquid sample, it will be drawn

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through the capillary by the Venturi effect. As it enters the gas stream rushing across the end of the capillary, the stream of liquid is shattered into a nebular (an aerosol or mist of droplets). This nebular then enters a spray chamber where the larger droplets are separated from the smaller ones by a combination of an impact bead (that helps shatter the droplets into smaller droplets) and a series of baffles. The smallest droplets are then carried in the gas flow towards the atom cell whilst the larger ones pass to waste under the influence of gravity. For flame spectrometry, typically 10– 15% of the sample reaches the atom cell whilst 85 –90% is wasted. Once the sample aerosol enters the atom cell, the flame desolvates the aerosol and then dissociates the salts present into their constituent atoms. The atoms of the analyte will then absorb the light emitted from the light source and the amount of light absorbed can be related to the concentration of the analyte in the sample. There are several flame types that can be used, the most common being a mixture of air and acetylene. These can be mixed in several different proportions, including fuel rich (here a yellow flame that has reducing properties is produced), fuel lean (a blue flame that is chemically oxidizing) or stoichiometric (an intermediate flame that is blue but also has yellow “feathers” at its base). The type of flame chemistry used will depend on the analyte, and should be optimized for every element determined. The temperatures of these flames range between 1700 and 2200 K, which is sufficient to dissociate the majority of compounds. A hotter flame, e.g., nitrous oxide –acetylene (2500 – 2700 K) may be necessary for the more refractory compounds. Again, different proportions of nitrous oxide and acetylene may be required for optimal determination of different analytes. Other flames used, albeit less frequently, include a hydrogen diffusion flame and a methane flame. The former has the advantage of being very optically clean at lower wavelengths, which will improve the signal-to-noise characteristics for wavelengths such as 193.7 nm (As), 196 nm (Se), 213.9 nm (Zn) and 217 nm (Pb). The methane flame is useful when the sample matrix may contain a very high concentration of a component that forms an explosive acetylide compound (e.g., Ag or Cu). The analyte wavelength used for the measurement process is usually isolated with the use of a low-resolution monochromator, since in AAS the resolution of the instrument is essentially derived from the narrow wavelength output of the line source. Once the wavelength of light of interest has been isolated, it may be detected using a photomultiplier tube or solid-state electronic device (such as a charge coupled or charge injection device or diode array). These convert photons to an electrical signal, the magnitude of which may be related to the concentration of the analyte within the sample. Since atomic absorption is a ratio technique, i.e., a comparison is made of the initial light intensity with the intensity after absorption by the analyte has occurred, there are no units. Tuned electronic circuits are used to ensure that light produced by emission

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processes arising from the analyte within the flame do not interfere with the measurement of atomic absorption. Atomic emission spectrometry utilizes all of the above procedures with the exception of the HCL light source. A monochromator is usually used to isolate the wavelengths of interest but very low-resolution optical filters may be used in the less expensive flame photometers. Analyte atoms thermally excited by the flame emit multiple wavelengths of light, one of which is isolated and detected. Since the advent of inductively coupled plasma instrumentation for atomic emission, the flame emission technique has been in decline and is now rarely used. A more detailed description of the instrumentation used and of the processes occurring within it may be obtained in several other publications [2,3]. 5.3.4

Interferences and background correction techniques

The majority of interferences that are encountered are either physical or chemical in nature, although a few spectral interferences arising from molecular species also exist. Transport efficiency of the sample through the sample introduction system can lead to interferences if standards are not closely matrix matched with the samples. For example, differences in viscosity between samples and standards result in different nebulization efficiencies. If less of the sample reached the flame, an underestimate of the true concentration of the analytes would be made. If the concentration of the analyte is sufficiently high, it may be possible to dilute the sample such that the dissolved solid content becomes negligible. If the analyte cannot be diluted, then it may be necessary to perform a standard additions analysis. Many analysts are not overly keen to use the standard additions technique, because it means that the same sample must be analyzed up to four times with different added concentrations, thereby lengthening the analytical process fourfold. In addition, the volume of sample used will be increased fourfold, which may be problematic if only a limited supply is available. There are several types of interference that may occur in flame spectroscopy. Chemical interferences may cause either depressions or enhancements in the signal, depending on the particular interferent. If a species is present in the sample that will combine with the analyte to form a less volatile compound that is difficult to dissociate in the flame, then a depression in signal may occur, an example being the presence of phosphate during the determination of calcium. There are various methods for overcoming this, including adjusting the nebulizer so that smaller droplets are produced; making observations higher in the flame so that the less volatile compound has a longer time to become dissociated; using a releasing agent (e.g., lanthanum), that preferentially combines with the phosphate, and using a chelating agent (e.g., EDTA) to complex with the analyte so that it cannot combine with the phosphate. The addition of other chemicals to the samples may lead to

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contamination and error, so possibly the most simple and reliable method of overcoming this type of interference is the use of a hotter flame, i.e., nitrous oxide–acetylene. Signal depressions may also occur if the analyte becomes occluded in a refractory compound, such as is the case of the oxides of rare earth elements, uranium or zirconium. Again, the use of a hotter flame usually overcomes this problem. Signal enhancements are much rarer, but an example is the formation of an EDTA complex for calcium. The complex is more volatile than many other calcium compounds. Matrix matching may overcome this, but the use of a hotter flame will ensure that all of the analyte in both standards and samples is atomized. Similarly, if an analyte is occluded into a volatile compound (e.g., ammonium chloride), the atomization of the analyte may be enhanced. Again, matrix matching usually provides a solution to the problem. Ionization interferences occur mainly for the alkali metals that have an exceptionally low first ionization potential (IP). Since flame spectroscopy usually determines atoms (either by atomic absorption or by atomic emission), the formation of ions may lead to problems because these will not absorb or emit at the same wavelength. If, for example, sodium is to be determined by either atomic absorption or emission, it is usually necessary to add a high concentration of another easily ionized element such as potassium or cesium (assuming that these are not amongst the analytes), to the samples, standards and blanks. These will become ionized in the flame, producing a large excess of electrons that then force the ionization equilibrium to favor the neutral analyte species. Samples are more likely to contain other easily ionizable elements than pure aqueous standards, and therefore the extent of ionization will be less than for the standards. Unless a large excess of ionization buffer is added to all samples, standards and blanks, an overestimate of the analyte concentration could result. Spectral interference, caused by direct line overlap, is negligible for flame spectroscopy. However, since molecules exhibit a much wider wavelength band of absorption/emission, these can occasionally prove to be problematic. Examples include phosphate and sulfate interferences on the arsenic and selenium lines at 193.7 and 196 nm, respectively. Similarly, small particulate matter within the flame may attenuate the light beam, leading to an erroneously high signal unless it is corrected for by a method of background correction. There are several types of background correction systems used for AAS. These include the deuterium lamp (continuum source), Zeeman effect and Smith-Hieftje systems, and each has been available in commercial instrumentation. Each background correction system has its own relative advantages and disadvantages. A detailed description of their operation is not necessary here, but may be found in the literature [3]. However, a brief description of the advantages and disadvantages of each is appropriate. The deuterium lamp has relatively ineffective output above 350 nm and so the most sensitive resonance line for chromium (at 357.9 nm) may occasionally be problematic if particulate

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material is present in the flame. The deuterium lamp is, however, fairly inexpensive to purchase and to operate and provides an adequate correction for those analytes that have a primary wavelength in the UV region. Since the large majority of background absorption phenomena occur below 350 nm, it is adequate for most applications. Another disadvantage of this continuum source system is that the beam from this light source must follow an identical optical path to the beam from the HCL. The Smith-Hieftje correction system operates on the principle of self-reversal, i.e., if the source HCL is pulsed to much higher operating current, the narrow emission line profiles are broadened and suffer some self-reversal. The analyte atoms absorb only a fraction of this broadened line radiation whereas the background absorption is unaffected. By operating the HCL at normal current (2 –25 mA) and at much higher current (. 100 mA) in a rapidly oscillating manner, estimates of the total absorbance (atomic and background) and the background absorbance may be made by subtracting the two absorbance signals. The technique works fairly well for many types of interference, but has several drawbacks. These include the shortened lifetime of the HCL, the “assumption” that the atomic absorption during the high current pulse is negligible, which leads to reduced analytical sensitivity, and increased curvature of the calibration curve. The Zeeman background correction system is used almost exclusively for electrothermal AAS (ET-AAS), but will be included here amongst the other background correction techniques for completeness. It is a technique that uses a powerful magnet (approximately 1 T) to separate the normal single atomic line profile into several different components, as described in more detail elsewhere [3,4]. The significant advantage of this type of correction system is that it is capable of correcting much larger background signals than any of the other methods. Unfortunately, it also suffers from decreased sensitivity and increased curvature (and ultimately complete roll-over) of the calibration function. 5.3.5

Conventional nebulization

The process by which conventional nebulization occurs and some of the potential problems that may arise (i.e., different viscosity of samples and standards leading to different nebulization efficiency and hence, different sensitivity) have been described previously and several other factors need to be discussed. The sample uptake rate for conventional nebulization in a typical flame AAS/AES instrument is between 4 and 8 ml min21. This may usually be changed by careful adjustment of the nebulizer. Fortunately, the time required for the analyte to pass through the nebulizer/spray chamber system, into the burner head and then into the flame, is only about a second. Therefore, measurements may be made only 2–5 s (depending on the integration time used) after sample introduction commences. The amount of sample consumed will depend on the number of replicate readings taken, but usually, a volume of 3–4 ml is adequate to determine an analyte. However, for most instruments,

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flame AAS is a single element technique, i.e., the operating conditions will have to be changed for another analyte, 3–4 ml of sample is required for every analyte. This may prove problematic if 10 –20 analytes need to be determined and only 25 ml of sample is available. It should be noted, however, that some modern instruments have a rapidly scanning monochromator (2000 nm min21) and specialized valves that enable a very rapid change of the flame chemistry. These attributes, combined with a series of fast rotating mirrors to change between different HCLs, enable very rapid sequential determinations to be made. Such an instrument offers significant improvements in analysis time, although sample consumption may be marginally higher. Conventional nebulization into flame AAS is, as discussed above, very rapid. After the measurement of one sample or standard, a washout period is necessary in which water, dilute acid or a chemical matched to the matrix of the samples is used to ensure that there is no signal carry over between samples. Depending on the matrix and the analyte, this requires anywhere from just a few seconds to in excess of a minute. The washout may have to be especially long if the sample contains a very high concentration of dissolved salts. Once these samples enter the burner head, they will desolvate and, unless a long wash period is used, there is a chance that the salts will start to block the burner head. This would result in several effects, including a reduction in the sensitivity (because if gaps start appearing in the flame, the path length is effectively decreased) and excessive signal drift. Occasionally, the burner may be cleaned by gentle scraping with a non-combustible item, e.g., a stainless steel spatula but often, flame extinction followed by dismantling of the burner assembly is necessary to clean it. This would obviously lead to an increase in the analysis time. A single element may be determined in only a few seconds per sample, and therefore a batch of 20 samples could be analyzed for one analyte in less than 10 min. Using the rapid sequential instruments, sample throughput for several analytes can be improved significantly. Many modern instruments come equipped with an autosampler, which facilitates the unattended operation of the instrument, thereby maximizing sample throughput with minimal human intervention. Many modern instruments have software that enables the complete analysis to be pre-programmed, incorporating assorted quality control measures, e.g., check standards, collection of data from multiple replicates and the calculation of mean values, standard deviations and precision, etc. Some instruments have a moveable turret in which three or four HCLs may be inserted. The software then controls the monochromator, changing to the wavelength necessary for each analyte. The figures of merit of flame techniques tend to be the least impressive of the standard instrumental techniques, but are still adequate for many applications. The limit of detection will depend on several factors, including the analyte itself. Some analytes, e.g., magnesium or cadmium, are extremely sensitive, whereas others, e.g., lead, are not. Other analytes that are extremely refractory, such as tantalum and tungsten, offer relatively poor sensitivity.

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The limit of detection will also depend on which analytical line of the analyte is being measured. As described previously, numerous analytical lines may be used and each has a different sensitivity, LOD, calibration range, etc. The LOD obtained using each of the lines will also depend on the instrumental parameters used, as each element has an optimal flame chemistry, HCL current, spectral bandpass, viewing height, etc., and unless the optimal conditions are used, the optimal figures of merit will not be obtained. A list of limits of detection for numerous elements is given in Table 5.1, for which it has been assumed that the most sensitive analytical line is used for each analyte under optimal conditions. It should be noted that the figures given in Table 5.1 relate to liquid samples. If a solid has been dissolved or digested, then a dilution has occurred and the LOD related to the solid would have to be re-calculated. For atomic absorption measurements, the linear range usually spans 1.5 –2 orders of magnitude before there is a departure from linearity. Therefore, assuming that several standards are prepared that cover this range and that steps have been taken to overcome potential interferences, reliable data should be obtained. Any sample that has an absorbance greater than the most concentrated standard should be diluted so that it comes into the working range. If many or all samples contain a concentration of an analyte that is above the most concentrated standard, it would be less time consuming to use an alternative wavelength and prepare a more appropriate calibration range than to dilute perhaps 50 samples. An alternative method is to rotate the burner head slightly. This has the effect of decreasing the path length, i.e., fewer atoms are in the light beam at any one instant, and therefore the linearity may be extended (at the expense of sensitivity). In such a case, it will still be necessary to prepare another standard that contains an analyte concentration closer to that expected in the sample to ensure linearity. The precision expected from a flame instrument (as with any instrumental method) will obviously depend on the concentration being measured. If the concentration is close to the LOD, then precision will be poor when compared with a concentration further up the linear range. For the latter example, a precision of 0.1 –2% relative standard deviation (RSD) is typical. As discussed previously, the majority of samples introduced via conventional nebulization must be liquid based and that the transport efficiency is usually between 10 and 15% for aqueous based samples. This figure will depend, however, on the nature of the sample. The presence of appreciable amounts of dissolved solid is likely to decrease this value. If the sample is present in an organic solvent, then the nebulization characteristics will differ markedly. Water has a fairly high surface tension and viscosity and a low vapor pressure. Organic solvents tend to have a lower surface tension and viscosity and a higher vapor pressure. This means that they more efficiently form an aerosol, resulting in an increased transport efficiency to the flame and enhanced sensitivity. It is therefore extremely important to prepare standards in the same solvent as that used to dissolve the samples. The presence of organic solvents in

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L. Ebdon et al. TABLE 5.1 Limits of detection using flame AAS under optimum conditions with the most sensitive line Analyte

LOD (mg l21)

Analyte

Ag Al As Ba Be Bi Ca Cd Co Cr Cs Cu Dy Er Eu Fe Ga Gd Ge Hf Hg Ho In Ir K La Li Lu Mg Mn

2 30 300 20 1 50 1 2 5 6 4 3 30 50 1.5 6 100 2000 200 2000 200 40 40 500 3 2000 2 300 0.3 2

Mo Na Nb Nd Ni P Pb Pd Pr Pt Rb Ru Sb Sc Se Si Sn Sr Ta Tb Te Th Ti Tl Tm U V W Y Zn

LOD (mg l21) 20 0.2 2000 1000 10 40,000 10 10 10,000 100 10 100 40 50 500 300 100 2 2000 700 30 100 20 20 40,000 100 1000 200 1

the flame will radically change the flame chemistry and it is therefore important to optimize the flame conditions using the solvent of interest if optimal sensitivity is to be obtained. Organometallic standards, which are often based on the cyclohexylbutyrates, are available commercially and are soluble in many organic solvents. If the solvent is methanol, then many inorganic standards that are stabilized in nitric or hydrochloric acid are soluble.

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Although the large majority of samples introduced into flame atomic spectrometric instrumentation is liquid based, the introduction of solid materials is possible. This is usually achieved through the preparation of slurries. A slurry is a suspension of a very finely ground sample in a liquid medium which is usually a dispersant to prevent the particles from flocculating. The subject will be dealt with in far more detail in a later section (Section 5.3.6.6). Briefly, the sample is ground so that the particle size is equivalent to the droplet size in the aerosol formed by the nebulizer. The transport efficiency of the slurry particles should therefore be equivalent to aqueous standards and therefore, simple aqueous standards (or standards matrix matched with the dispersant used for the slurry) can be used for calibration. A plethora of other pre-concentration and matrix separation techniques, including solid phase extractions, liquid –liquid extraction, co-precipitation, flotation and evaporation are available and these will be discussed in more detail in other chapters throughout the book. 5.3.6

Alternative methods of sample introduction

There are a number of alternative methods that may be used to introduce samples for flame spectrometry. Some of these offer increased sensitivity and others help overcome potential interferences, thereby yielding more reliable results or better long-term stability. 5.3.6.1 Chemical vapor generation Chemical vapor generation as a method of sample introduction is discussed in detail by Cai in this volume. The topic has also been reviewed by Tsalev [5] and by Howard [7]. Although primarily applicable to elements such as arsenic, selenium, antimony, tellurium and germanium, which are capable of forming gaseous hydrides at room temperature by reaction with sodium tetrahydroborate, and elemental mercury, it has been reported that several other analytes, including Ag, Au, Cd, Co, Cu, Ni, Sn and Zn, have also been determined by vapor generation [6]. There are also alternative reagents that may be used to form volatile vapors, including various salts of tetraethylborates. Mercury may be reduced to its elemental state by stannous chloride. There are several advantages of introducing analytes as a gas rather than as a liquid. The first is that the analyte is separated from the bulk of the matrix. Together with higher sample uptake rates this means that spectroscopic interferences are minimal. Also, gases are more easily transported than liquids, and hence the transport efficiency of the vapors to the atom cell is closer to 100 than 10– 15% obtained for liquids. This will obviously lead to a sensitivity improvement by a factor of 30–50. Frequently, the atom cell is a quartz T-piece placed on top of the burner head that is heated by the flame and the light beam from the HCL passes

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through it. As the gaseous analytes enter the heated T-piece, the compounds dissociate, forming analyte atoms. The advantage of using the T-piece rather than allowing the analyte vapors to simply enter the flame is that it acts as a sort of trap, increasing the analyte residence time in the optical path. Further, the T-piece may also provide a longer absorption path length (15 cm). There are disadvantages associated with vapor generation techniques. Only relatively few analytes form gaseous compounds at room temperature and not all of the oxidation states react with the same efficiency. Arsenic in its þ3 state forms a hydride far more efficiently and with a different sensitivity than does As(V). Similarly Se(IV) forms a hydride with relative ease whereas Se(VI) does not form a hydride at all. In addition, when the analyte is an integral part of an organic molecule, e.g., selenium in the form of selenomethionine or arsenic as arsenobetaine (AsB), a hydride is not formed. As such, an underestimate of the total concentration of the element of interest will be obtained unless steps are taken to transform all species of the analyte into a state that will form a hydride. There have been numerous chemical and physical methods used to accomplish this, including the use of L -cysteine to reduce As(V) and monomethylarsonic acid (MMAA) and dimethylarsinic acid (DMAA) to As(III) [8], alkaline persulfate to oxidize arsenobetaine [9], iodide/ iodate reactions [10], etc. Included in the physical methods used are photolysis [9] and the use of microwave energy to accelerate the action of acids [11]. Another potential problem with the technique is the presence in the samples of transition metals such as zinc, copper and iron and of precious group metals such as gold, palladium and platinum. These elements interfere with the hydride formation process and often result in an underestimate of the analyte’s concentration. These potential interferences may be overcome by the addition of a chelating agent, such as 1,10-phenanthroline [12], 8 hydroxyquinoline [13] or picolinic acid [14]. Limits of detection for the vapor generating analytes can be improved by a factor of over 100 compared with their conventional nebulization, with LODs for many of the analytes being at the low ng ml21 level. Precision should again be at the 0.5 –3% RSD level. Sample consumption will depend on the mode of vapor generation. In the continuous mode, a typical analysis is likely to use 10– 12 ml of sample for a measurement time of approximately 30 s. This type of operation is therefore slightly more wasteful of sample than conventional nebulization, but sensitivity is improved still further. The other mode of hydride generation is the “batch” mode. Here, a discrete volume of sample is used and the signal will appear as a transient, i.e., a peak. This method uses far less sample, although several injections will have to be performed so that an estimate of precision can be made. The other drawback is that it may be necessary to have a chart recorder or integrator output to the spectrometer so that measurement of the peak height or area may be performed more accurately. Sample throughput for hydride generation introduction to flame

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spectrometry is less than for conventional nebulization, but it should be possible to analyze 20–30 replicates in an hour. 5.3.6.2 Sampling cups and flow injection If a very limited sample volume is available, e.g., , 2 ml, it will probably not be possible to determine any more than one analyte if conventional nebulization is used. Also, if a sample contains a very high concentration of dissolved solids, there is a chance of both nebulizer and/or burner head blockage. Sampling cups and flow injection (FI) methods are both means of introducing discrete volumes of sample, thereby decreasing the volume of sample introduced and hence the amount of dissolved solid entering the instrumentation. As with the batch mode of vapor generation, a transient signal is obtained. For maximum signal to be obtained, an injection volume of approximately 0.5 ml is required, but smaller volumes may be introduced with a concomitant drop in response. This occurs because approximately 0.5 ml is the minimum volume required to obtain a signal equivalent to that generated with conventional nebulization. The overall result is that 0.5 ml injection volumes are likely to lead to improved precision when compared with smaller volumes. The sampling cup is a device that has a small hole in the bottom of a cup of volume of approximately 1 ml. The nebulizer uptake tube is inserted into the hole, so when sample is dispensed into the cup via a high accuracy and high precision micropipette, it is immediately aspirated into the flame. In between sample replicates, several volumes of water or dilute acid may be injected to ensure that no carry over effect occurs. The use of a micropipette to introduce the sample is a potential source of imprecision, since a worn seal will prevent reproducible volumes from being taken up and dispensed. The method of sample introduction using the sample cup is also called pulse or gulp nebulization. Numerous FI methods have been reported and an overview of the relevant literature from 1972 to 1995 has been presented by Fang et al. [15], with a current treatment available in this volume. The simplest of FI methods requires just a sample injection valve to be coupled to the nebulizer uptake tube, permitting discrete volumes of 0.01 ml upwards to be introduced via a sample loop, although sample introduction via direct injection with a syringe is also possible, but this leads to poorer precision. Flow injection frequently makes use of mini- or micro-columns of an ion exchange or chelating resin to retain the analytes of interest and eliminate or minimize concomitant element interference effects. Pre-concentration may also be readily achieved using FI techniques and will improve the LOD for flame detection (and any other technique) considerably. The pre-concentration factor achievable by FI techniques will depend on the analyte, the sample volume available, time constraints and, in some cases, by the purity of the chemicals used for buffers, etc. Time constraints must also be considered. If the sample is pumped through the column at 3–4 ml min21, it will still take 25 –30 min to introduce 100 ml.

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The volume and concentration of eluent required will have to be optimized, but typically 0.25– 1.0 ml is used. Therefore, large pre-concentration factors are possible theoretically, but it will be at the expense of time. Inevitably, a busy laboratory with 100 samples to analyze cannot afford to take 30 min per sample replicate; especially when typically three replicates per sample are required so that an estimation of precision can be made. In practice, pre-concentration factors of 10 –40 are more common. The precision of FI techniques that use a column of resin to afford matrix removal/pre-concentration will depend on the reproducibility with which the analyte is retained and then eluted from the column. Successful methods have a precision of typically , 5% RSD. Flow injection techniques that simply use a valve to introduce small volumes of sample into the spectrometer should have a precision of 1–2% but, again, this will depend on the concentration of the analyte within the sample. Sample throughput for sampling cups would typically be 60 samples (assuming three or four replicates) per hour. Flow injection methods tend to be slower, but simple FI methods may analyze 20 –30 samples per hour. Methods involving matrix separation/pre-concentration are the slowest and will depend on the pre-concentration factor, but 5–10 samples per hour is typical. 5.3.6.3 Slotted tube atom trap (STAT) The STAT acts in a very similar way to the quartz T-piece used for vapor generation. The tube is placed on the burner head, ensuring that a slot carved into the side of it is directly over the flame slot in the burner head; a smaller slot on the top and/or ends of the tube allow exit of flame gases from the tube. The flame ensures the tube then acts as a heated atom trap. The analyte molecules introduced via conventional nebulization, or often by FI, then enter the tube through the slot, become thermally dissociated into atoms and atomic absorption occurs. An increase in sensitivity by a factor of three- to fivefold is obtained. Precision has also been found to improve through the use of a STAT. A review of the atom trapping procedures in flame spectrometry has been presented by Matusiewicz [16]. Closely related to the STAT is the application of a water cooled atom trap (WCAT) that consists of a water cooled single or dual silica tube suspended in the flame which serves as a condensation site for atoms/molecules introduced into the flame. Following a suitable collection period, the water cooling is terminated by use of a pulse of gas through the tubing, which then rapidly heats to flame temperature and results in volatilization of the collected analyte. Typical 2-minute collection periods can improve the detection limit by an order of magnitude, but the technique is clearly most favorable for volatile elements such as Ag, Cd, Cu, Zn, Pb and Tl. Recently, the STAT and the WCAT have been combined in a synergistic arrangement and used for sample analysis [17]. In general, the sample throughput and sample consumption will be governed by whatever sample introduction method is used.

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5.3.6.4 Chromatography Simply determining the “total” concentration of an analyte does not give any information on the overall toxicity of the sample. Therefore, speciation analysis, the determination of different forms of the analyte, is becoming increasingly frequent. One of the most common methods of achieving speciation analysis is to couple a separation technique, e.g., high performance liquid chromatography (HPLC) or gas chromatography (GC), with an element specific detector. Flame spectrometry is one of the least sensitive methods of atomic spectrometry and therefore the number of speciation analyzes that may be performed with it is somewhat limited. However, despite this obvious drawback, a large number of applications have been presented. Several reviews of chromatography coupled with flame spectrometry have been published. These include those by Ebdon et al., who covered the earlier literature for liquid [18], and GC [19], and a more recent one by Szpunar Lobinska et al. [20]. Several speciation approaches are discussed in detail in other chapters of this book. Gas chromatography depends on the analyte being volatile. If the analytes are not naturally volatile, it may be necessary to resort to use of derivatization reactions, such as use of a Grignard reagent [21]. For HPLC couplings, the end of the column may simply be attached to the nebulizer uptake tube. The flow rate through the chromatography column is typically 1– 2 ml min21, which is less than the natural uptake rate for the nebulizer. It may therefore be necessary to insert a small air bleed to compensate for this mismatch [22]. For GC couplings, it is usually necessary to utilize a heated transfer line from the GC oven to the atom cell. The end of the heated transfer line is usually placed in one of the slots of a STAT so that extra sensitivity is obtained. Sample throughput will depend largely on the chromatographic stage. Both liquid and gas chromatograms frequently take in excess of 10 min per sample, and so sample throughput is very limited. Instrumental precision will depend on the method of sample introduction. If a sample loop is used for HPLC, then precision should be less than 5% RSD. If a syringe is used to inject 1–10 ml into a gas chromatograph, then precision can be . 10%. This may be improved substantially if an internal standard is used. For speciation techniques, this is normally a compound that has similar properties to the analyte compounds, but is not found naturally in the sample. Ideally, the internal standard should elute in the middle of the chromatogram, and not co-elute with any of the species of interest. Another potential source of error, inaccuracy and imprecision, is the extraction technique used to remove the analyte species in an unchanged state from solid samples. This topic will be discussed in later chapters, but it is worth noting here that an inadequate extraction method (one that changes the speciation or that does not yield reproducible recovery, etc.) will render the entire analysis irrelevant. One point that is worth noting for all speciation analyzes is that the concentrations quoted should specify whether the values are related to the

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concentration of the species or of the analyte element. For instance, if during the analysis of fish extracts a concentration of 5 mg kg21 is quoted, the report should specify whether this is 5 mg of arsenic kg21 or 5 mg of arsenobetaine kg21, etc. Failure to do this is likely to lead to confusion and error. Similarly, LODs should also be quoted with the same qualifications. 5.3.6.5 Multiple couplings Occasionally, when extra sensitivity is required for speciation analysis and when instrumental costs preclude the purchase of a more sensitive detector, it is necessary to couple together several techniques. A technique that has become relatively common (in the research literature) is the coupling of HPLC with HG-AAS [23,24]. After the species have been separated using HPLC, either a chemical or physical process is used on-line to convert each of the species to a state that will form a hydride and may require a chemical oxidation using alkaline persulfate or photolysis. In the example given in Ref. [23], on-line microwave assisted oxidation yielded LODs of 2.5, 5.3, 3.3 and 5.9 ng ml21 AsB, DMAA, MMAA and As(V), respectively. 5.3.6.6 Slurries As discussed earlier, a slurry is a suspension of solid sample in a liquid medium. The advantages of slurry sample introduction include the ease of preparation, the non-requirement of powerful reagents such as hydrofluoric acid, no possibility of losing volatile elements and, for most analytes, minimal contamination. Fuller et al. [25] reported very early on the relative merits of flame, electrothermal and ICP atomization techniques for the direct analysis of slurries. Several slurry preparation techniques have been reported, but the fundamental necessity is that it be representative of the sample, i.e., be homogeneous. This usually means that the powdered sample must be ground using either the bottle and bead method or in a micronizer. In the bottle and bead method, a sub-sample is placed in a small plastic bottle, a small volume (e.g., 5 ml) of aqueous dispersant are added together with 10 g of zirconia beads (2 mm diameter) and then the bottle is sealed and placed on a mechanical flask shaker for a period of time that is dependent on the sample type. Blanks are prepared in the same way, but omitting the sample. The drawback with the technique is that the blanks tend to give a “worst case scenario”, because the beads have no sample to cushion the impact of the collisions between them during the grinding process. This means that the beads will grind themselves to a greater extent than when the sample is present. The concentration of the contaminants in the blank is therefore often slightly larger than that found in the samples. The process is, however, suitable for the determination of a great number of analytes, with the obvious exceptions being zirconium and hafnium (which is often a substantial contaminant in the beads). The micronizer uses agate rods to grind the sample instead of zirconia beads. This will give rise to a different set of contaminants (i.e., Na, Mn, Si, etc.). The choice of which

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grinding procedure to use will therefore, depend on the analytes to be determined and on the nature of the sample. The zirconia beads are fairly hard [measure of hardness (MOH) ¼ 8 þ ], whereas the agate rods are softer ðMOH ¼ 7Þ: If a particularly hard sample is to be slurried, then the zirconia beads are a more suitable grinding medium because otherwise the agate rods may end up actually being ground by the sample. For exceptionally hard samples, it may be necessary to use a tungsten carbide swing mill to affect grinding. After grinding is complete, the beads (or agate rods) may be removed by simple filtration through a coarse Buchner funnel, without the presence of a filter paper. The beads may then be washed with more dispersant and the washings collected and combined with the sample. Apart from the problems associated with insufficient grinding and contamination, another problem is that some samples are very soft and have a tendency to become squashed or flattened during the grinding process rather than being smashed into smaller fragments. The overall effect, therefore, is that particle size is not reduced sufficiently. This problem is more common with organic based samples such as plants, etc. The dispersant used will depend on the nature of the sample. For inorganic matrices such as soils, rocks, ceramics and other refractory materials, sodium hexametaphosphate or sodium pyrophosphate is suitable. For more organic based samples such as plant material, blood, food samples, etc., then Triton X-100 or aerosol OT are more appropriate. In either case, it is necessary to inspect the ground sample under a microscope to ensure that the particles are sufficiently dispersed, i.e., they have not flocculated together. If sample particles do flocculate together to form an agglomeration, then they will act as a much larger particle and the slurry will no longer be homogeneous. Slurries may be aspirated into either flame [26] or plasma-based instruments, introduced to ET-AAS instruments or they may even be analyzed using a hydride generation technique. If the slurry is to be aspirated into a flame or a plasma via a conventional nebulizer/spray chamber assembly, it is necessary to ensure that the particle size is extremely small and that, ideally, the particle size distribution covers only a small range. If this is the case, then the sample particles will act in a similar manner to aerosol droplets, enabling calibration against standards prepared in the aqueous dispersant. If the slurry particles are too large, the nebulizer and spray chamber select against them and they will preferentially be passed to waste so that the sample that reaches the atom cell is not representative of the whole, leading to inaccuracy and poor precision. In addition, the larger the particle size, the more difficult it will be to ensure homogeneity, i.e., even if the slurry is stirred, the larger droplets will sink to the bottom of the container at a faster rate than the smaller ones. The fundamental parameters required for slurry nebulization into plasmas were discussed in a paper by Goodall et al. [27]. These authors determined that an upper particle size diameter of 2.0 –2.5 mm was necessary for accurate results to be obtained, but the maximum particle size that yielded accurate results was

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also dependent upon the sample density. For a sample having a density of 1 g cm23, a particle size of 2.9 mm could still be transported efficiently to the plasma; however, for a sample with a density of 7 g cm23, the size had to decrease to 1.5 mm. For very refractory samples, even if the particle size of the slurry is sufficiently small to pass through the nebulizer/spray chamber assembly and reach the plasma, complete dissociation may not occur and an inaccurate concentration value will be determined. To overcome such problems, alternative gases have been used. Ebdon and Goodall [28] introduced hydrogen to the nebulizer gas flow to yield more accurate results when slurries of refractory certified reference materials (CRMs) were analyzed. This was attributed to the increased thermal conductivity of the hydrogen improving the energy transfer from the toroidal part of the plasma to the annulus, thereby increasing the rotational temperature and, hence, improved dissociation of the particles. A review of slurry nebulization into plasmas has been prepared by Ebdon et al. [29]. Even when the slurry is to be analyzed by ET-AAS, sample homogeneity must be maintained. Since the particulate material of slurries will settle with time, it is necessary to agitate the slurries vigorously to ensure complete homogeneity before the sample is introduced. Failure to do this will lead to exceptionally poor precision and accuracy. Therefore, hand-held pipettes are often used so that sample introduction takes place immediately after homogenization. The introduction of slurries using an autosampler is a possibility provided that there is a mechanism by which homogeneity is ensured. Miller-Ihli developed an ultrasonic probe that is inserted into the autosampler cups to mix the slurry and hence ensure homogeneity [30]. Using such a device, the analysis of slurries can become completely automated. A review of the slurry sampling for ET-AAS applications between 1990 and 2000 has been presented by Cal-Prieto et al. [31]. For many slurry types and some biological liquids such as blood, it may be necessary to introduce air or oxygen during the pyrolysis stage to ensure a more efficient oxidative combustion process that decomposes the organic material more efficiently and hence helps reduce interferences arising from smoke. In addition, for the blood samples, it will also prevent the build-up of a carbonaceous residue that will, in time, start to obscure the light beam. If a reactive gas is introduced during the pyrolysis stage, it is normally necessary to use a second pyrolysis stage with just an inert gas passing through the tube to remove all traces of the air before atomization. Failure to do this will lead to accelerated tube wear. Precision for slurry analysis by ET-AAS is dependent upon the homogeneity of the slurry, but could be as low as 3–5% RSD. Occasionally, the slurry will be mixed with nitric acid or some other reasonably strong reagent to help leach the analytes from the solid matrix into the liquid phase. This will often have the effect of increasing accuracy of the analysis, because some of the analyte is in solution and will therefore act in

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a similar manner to the standard. Additionally, the particle size is likely to be decreased, enabling more efficient transport of these to the atom cell. An example of a procedure that has used acid leaching as an aid to slurry nebulization into inductively coupled plasma-mass spectrometry (ICP-MS) has been published by Persaud et al. [32]. If the sample is biological in origin, the analytes may well be at a low concentration. It is sometimes possible to place the sample in a muffle furnace and then char it at 4508C for several hours until only ash remains. This ash may then be slurried in the normal way. Such a pre-treatment will enable an effective pre-concentration to be achieved since, on ashing, many biological samples will lose 90% of their mass and hence a larger quantity of sample may be introduced before problems associated with excessive amounts of dissolved/suspended solids occur. The usefulness of the dry-ashing pre-concentration technique is, however, analyte selective and will be inappropriate for very volatile analytes such as mercury, cadmium and possibly lead and zinc. 5.4 5.4.1

ELECTROTHERMAL AAS Introduction

Electrothermal AAS shares the same fundamental principles as flame AAS, the major differences being the atom cell and the method of sample introduction. In ET-AAS, the liquid sample is dispensed into a graphite tube, which is heated resistively, undergoing a temperature programme that first dries the sample, pyrolyzes it so that as many matrix concomitants (i.e., potential interferences) are removed as possible, and then heats it to a temperature that is sufficiently high to vaporize and atomize the analyte so that it can absorb the HCL light beam. There is then usually a cleaning stage to prevent analyte carry over between samples. The temperature of each of these stages is dependent upon the analyte of interest. The drying temperature should be sufficient to ensure smooth evaporation of the solvent. If it is too high, the sample may froth and spit out of the tube, decreasing precision. The pyrolysis temperature should be high enough to remove as many interferences as possible, but not too high so that the analyte is lost through volatilization. This temperature can range between , 2508C for mercury through to 17008C for very refractory analytes such as erbium. The atomization temperature should be sufficient to ensure complete atomization of the analyte whilst not being excessively high so as to cause accelerated tube wear. A temperature between 1200 and 28008C may be used, depending on the analyte and on the capability of the instrument. The tube is protected from atmospheric oxidation by purging the entire system with argon, although nitrogen may be used for some analytes, excepting those that form a refractory nitride. The gas usually surrounds the graphite tube and flows at a rate of 1–3 l min21. Many modern instruments also have a flow of argon (200 –300 ml min21) internally through the tube to aid

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the flushing of smoke and solvent vapor from it. This internal flow is normally switched off during atomization to prevent dilution of the analyte atoms and from flushing the atoms out of the light beam too rapidly. The speed of analysis is much poorer compared with flame AAS, as a typical ET-AAS temperature programme can exceed 2 min and frequently three replicates will be analyzed per sample. It is therefore unlikely that many more than 10 samples can be analyzed per hour. The initial cost of the instrumentation is substantially higher than simple flame spectrometers, with the least expensive electrothermal (also called graphite furnace) instruments being double the price. More complex instrumentation will cost much more. The running costs also tend to be higher, with the graphite tubes costing up to US $70 each, as well as the supply of argon gas. The lifetime of the tube depends largely on how corrosive the sample is and what analytes are of interest (i.e., how high the atomization temperature needs to be). The advantages of using ET-AAS rather than many other detection techniques include the requirement of only a very small volume of sample. Typically, other instrumentation requires at least 0.5 –1 ml of sample unless dilution is performed (which may put the analyte below the LOD of the technique) or unless specialized sample introduction methods are used, e.g., pulse nebulization, etc. The typical injection volume for ET-AAS is 10 –30 ml, so even if triplicate measurements are made, less than 100 ml would be sufficient. In addition, the sensitivity is 100 –1000 times superior to flame AAS and for many elements it is also superior to inductively coupled plasma-atomic (optical) emission spectrometry (ICP-OES). 5.4.2

Conventional ET-AAS

There are several types of tube available commercially but most are manufactured from some type of graphite (although there are a few applications where metal atomizers have been used). Many of the applications of this latter type of atomizer have been reviewed by Nobrega et al. [33]. Of the graphite-based tubes, electrolytic (electro)graphite is the least expensive of the materials, but is very porous and samples can soak into the graphite lattice leading to interactions between the graphite and the analytes. For analytes such as chromium and other refractory carbide forming elements, this can be problematic. Pyrolytic graphite is much less porous (more dense) and is far less reactive than electrographite. Therefore, there is less interaction between the tube and the analytes and the lifetime is extended. The tube can either be coated with pyrolytic graphite or some may even be manufactured totally from it. There are also several different styles of tube available commercially. Some are heated longitudinally from the ends, leading to a temperature gradient along the tube with the middle being hottest. This is not a favorable scenario, since the analyte may be atomized from the hot central region of the tube and

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then condense at the cooler ends. Other tubes are heated transversely from the sides and do not exhibit a temperature gradient. Slightly lower atomization temperatures may be used without fear of condensation problems. Some tubes have an in-built platform onto which the sample is introduced. The platform ensures that the sample is heated both by the hot internal gas (convectively) and radiatively rather than by conduction from the tube walls, facilitating a process known as isothermal (or stabilized temperature) operation. This leads to fewer interference problems (these will be discussed in a later section). Sample is usually dispensed as a liquid into the graphite tube or onto the platform. An autosampler can dispense the sample to the same part of the graphite tube in a more reproducible way than a hand-held pipette. If the sample is placed in the same place more reproducibly, better precision should be obtained, especially for longitudinally heated tubes. Ideally, the sample should be dispensed from the same height each time, so that the sample drop is not disturbed by the autosampler introduction arm. If it were disturbed, it may spread over a larger area of the tube, again leading to impaired precision. In general, precision obtained with ET-AAS determinations are 1–3% RSD if an autosampler is used and 3–5% RSD if the sample is dispensed using a handheld micropipette. Studies on the behaviour of various arsenic species in ETAAS have ben reported [34]. Interferences are far more problematic for ET-AAS than for flame spectroscopy. Although true spectral interferences are equally as rare, chemical interferences and non-specific absorption (smoke) problems are exacerbated. The presence of some chemical species, e.g., chlorides, often increases the volatility of the analyte and may lead to loss at a lower pyrolysis temperature than occurs for aqueous standards. If a temperature optimization experiment is performed on standards and these “optimum” temperatures are used during the analysis, significant losses of the analyte may occur from the samples, leading to erroneous data. Other interferences have already been discussed, such as the formation of refractory carbides and nitrides (if nitrogen is used as the inert gas). Careful optimization of the temperature programme can overcome some of these problems, especially if matrix modifiers (also known as chemical modifiers) are used. Chemical modifiers are reagents that are introduced with the sample that interact either with the analyte, stabilizing it thermally, or with the matrix, to make it relatively more volatile. This means that a higher pyrolysis temperature may be used before analyte loss occurs through volatilization. If a higher pyrolysis temperature can be used, then more potential interferences may be removed. An assortment of chemical modifiers has been used; including phosphate based ones, magnesium nitrate (this helps occlude analytes within its matrix, hence preventing their loss) and a mix of magnesium nitrate and palladium nitrate. In general, their use can extend the temperature range used during the pyrolysis stage by 300 –6008C. A review of many of the interferences encountered in ET-AAS was published by Slavin and Manning [35]. Slavin developed the stabilized temperature platform

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furnace (STPF) concept. This was a protocol based on recommended precautions to take to minimize the effects of interferences [36] and included the use of an appropriate matrix modifier, integration of signals (area rather than peak height measurements), isothermal operation, rapid heating during atomization and the use of one of the more powerful background correction systems, such as the Zeeman effect. Virtually all modern analyzes made by ET-AAS use most or all of the recommendations of the STPF concept. Modern autosamplers achieve far more than simply delivering the sample to the atom cell. They may be used to perform on-line dilution, they mix the sample with appropriate matrix modifiers and they may be programmed to run quality control standards, re-calibrate if necessary, etc. As noted previously, isothermal operation using a platform usually helps decrease the extent of interferences because the analyte is vaporized from the platform when the temperature of the tube wall is otherwise higher. There is, therefore, less chance that the analyte will re-condense on a cooler part of the tube or recombine with cooler gas phase species, hence becoming unavailable for atomic absorption. An alternative to the use of a platform is probe atomization. This requires a specialized tube style that has a small slot machined into its side. It is through this slot that a mechanically operated graphite probe is inserted. The sample is introduced onto the probe, which is then inserted into the heated furnace through the slot wherein the sample is then dried and pyrolyzed in the normal way. The probe is then withdrawn from the tube, which is then heated to the atomization temperature, and the probe re-inserted. Again, the analytes will be radiatively and conductively heated within the tube and will therefore be less vulnerable to interferences. As discussed previously, the majority of samples introduced to ET-AAS are in the form of a liquid. However, the introduction of slurries is also possible. The relative advantages and pitfalls of slurry atomization have been discussed earlier (Section 5.3.6.6). Solids may be analyzed directly using ET-AAS if specialized tubes are used. The solid material (a few milligram) is usually weighed into a sample boat and then this is placed through a slot into a specialized tube. The furnace programme may then be run, although a drying stage may not be necessary. The technique is not common because precision can be poor. The technique is dependent upon the sample being completely homogeneous and, if only a few milligrams of sample are weighed into the boat, then homogeneity issues are of paramount importance and these may directly influence precision. There is also the possibility of some of the sample blowing from the boat prior to insertion into the atom cell. Using this technique, a precision of 10% RSD would normally be regarded as being good. The limits of detection (quoted as a concentration) obtainable using ET-AAS are relatively meaningless unless the injection volume is stated. Typically, 10– 30 ml is injected, but inevitably, 30 ml will yield a better LOD than a smaller volume. Instead, the LOD quoted as an absolute mass is usually given. As an

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alternative, the characteristic concentration (or mass), i.e., the concentration (or mass) that gives rise to 0.0044 absorbance (absorbance-s for integrated measurements) is also often quoted. In general, it is possible to say that for most elements, the concentration LOD for ET-AAS is often approximately 2–3 orders of magnitude lower than flame AAS. The exceptions are for elements that form refractory carbides. A list of approximate characteristic masses obtainable by ET-AAS is given in Table 5.2. As well as many of the standard pre-concentration techniques discussed in Section 5.3.5 and in other chapters of the book, preconcentration is also possible using ET-AAS. If a sample aliquot of 20 ml is introduced and dried in the normal way, a second aliquot may then be introduced on top of it. If this sample introduction and drying cycle continues for four or five aliquots and then the normal pyrolysis and atomize stages are performed, an effective fivefold pre-concentration may be achieved. It should be noted, of course, that there is also a fivefold increase in the amount of matrix present, so unless it is a very simple matrix, such as fresh water, or unless the matrix is easily removed during the pyrolysis stage, then severe interferences may result. Although this is a very time-consuming process, the presence of an autosampler can achieve this unattended and the analyst is free to perform other tasks. It has been emphasized previously that a typical ET-AAS cycle can take in excess of 2 min to achieve. Occasionally, this may be decreased if the method of “hot injection” is used. This is achieved when the sample is introduced at a slow rate into a furnace that has been pre-heated to 120 –1308C, such that the solvent evaporates as soon as it is introduced. This can reduce the overall time of analysis dramatically. Occasionally, higher drying temperatures may be used, e.g., 4008C, although this is rare. Methods for minimizing the time required for ET-AAS determinations have been summarized by Halls [37]. Using these accelerated programmes, the overall analysis time per replicate may occasionally be decreased to 20 –30 s. 5.4.3

Multi-element ET-AAS

The majority of ET-AAS instruments is capable of detecting only one analyte at any given time. However, instrumentation is now available that uses an echelle spectrometer and a solid-state detector capable of multi-element determinations that offers considerable savings in terms of time, costs and sample and reagent consumption. The one drawback is that the analytes to be determined must have similar physico-chemical properties. This is a result of the temperature programme used being a compromise, rather than an optimum, for any individual analyte. This means that analytes that are quite volatile and that require the same chemical modifier, such as As, Se, Te and Ge, may be determined together. It would, however, be inappropriate to combine one or more of these in the same determination as a much more refractory analyte such as chromium, that requires a different chemical modifier and a very different temperature programme for optimum sensitivity/reduction of

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Characteristic mass (pg)

Analyte


0.7 5 10 17 0.5 9 0.6 0.2 4.2 1.5 11 6 45 100 25 2 4.5


9 150 7 135 0.4 4 0.2 0.6

Characteristic mass (pg) 7 0.1

5 2200 6 9 70 1 15 10 14 15 10 2 4 9 50 15

22

0.15

interferences. At present, usually only three or four elements are determined simultaneously, although there are facilities for up to six. 5.4.4

Chemical vapor generation – ET-AAS

Chemical vapor generation has been coupled with ET-AAS. Generation phase interferences using this technique are basically the same as for any vapor

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generation determination. If the tube of the ET-AAS system is treated with a semi-permanent modifier such as iridium, zirconium or tungsten, and heated to 300 –8008C, hydrides of selenium, arsenic and several other analytes may be collected quantitatively [38]. The technique is known as “in-atomizer trapping” or “in situ trapping”. The subject of in situ trapping has been reviewed by Matusiewicz and Sturgeon [39]. Since typically 5–12 ml of sample is consumed for continuous vapor generation determinations, effectively the analyte from 5 ml of sample rather than 20 ml is deposited in the tube and, hence, the sensitivity is greatly improved. For batch HG-ET-AAS determinations, a sample loop of 500 ml has been used which yielded LODs of 0.82, 0.04, 0.26 and 0.29 mg l21 for As, Bi, Sb and Se, respectively [38], representing an improvement of over 10-fold compared with conventional ET-AAS on the same instrument. Precision at the 5 mg l21 level was typically less than 3.5% RSD. The use of this method will limit the linear dynamic range accordingly. As well as improving the limits of detection, this technique also separates the analytes from potential matrix interferences. This means that lengthy drying and pyrolysis stages are not required. As a result, the time required for HG-ET-AAS is not dissimilar to conventional ET-AAS.

5.4.5

Speciation

On-line speciation analysis using liquid chromatography and ET-AAS as a detection system is relatively rare, because the atom cell is often required to be heated continuously at the atomization temperature. Since chromatograms may take several minutes to be complete, this leads to very rapid tube wear and great expense. Examples do exist, however, where very rapid temperature programmes are used that have achieved on-line speciation [40]. The majority of speciation analyzes undertaken using ET-AAS as a detector have therefore been off-line, wherein fractions (typically 0.5 ml aliquots) of the eluant are collected at the end of the chromatographic column, which are then subjected to normal ET-AAS temperature programmes so that any analytes present may be determined. The concentration of the analyte in each fraction is then plotted so that a composite chromatogram is obtained. Since the separation and detection stages are not coupled directly, there is a greater chance of contamination and mislabeling of a particular fraction. This could potentially lead to great error if a fraction containing an analyte species is present, because the transient signal obtained from this species would be “moved” to a different retention time. In addition, closely eluting species may not be fully resolved, and hence would appear as only one peak. There are also major difficulties in optimizing the chromatography. Despite these drawbacks, numerous examples have appeared in the literature [41].

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5.5

5.5.1

INDUCTIVELY COUPLED PLASMA-ATOMIC EMISSION SPECTROMETRY Introduction

Inductively coupled plasma-atomic (optical) emission spectrometry is often the method of choice of most laboratories when several analytes need to be determined in a batch of samples. This is because the technique can detect analytes in either a rapid sequential manner or, for some instrumentation, detection of several analytes can be simultaneous. The cost of instrumentation varies, but is typically in the range US $50,000–80,000. 5.5.2

Theory and interferences

5.5.2.1 Theory The basic theory of emission from an ICP is identical to that for flame emission except that the ICP is an atom cell consisting of a very high temperature (6000– 10,000 K) ionized gas. The theory behind the formation of the ICP is discussed in detail elsewhere [42,43]. Since the plasma is at such a high temperature, any sample entering it will be desolvated; molecules will be dissociated forming atoms and, depending on the individual analyte’s ionization energy, these will become (partially) ionized. The atoms and/or ions then become thermally excited and emit light, the wavelengths of which may be separated from other wavelengths by an appropriate line isolation device and then detected. 5.5.2.2 Interferences Since the ICP is such a good excitation source, there are many species, both naturally present in the plasma and those that are introduced to it with the sample that emit light. The resulting emission spectrum can be far more complex than that produced using flame techniques and the chances of line co-incidence are much greater. The line isolation devices used in ICP-OES, therefore, tend to be more highly resolving than those required for the flame techniques. Despite the improved resolution, interferences are still common. The choice of which analytical line to use is therefore governed by both the potential interferences and by the sensitivity required. A more comprehensive discussion of interference effects in ICP-OES has been given in Ref. [42], and will therefore, only be dealt with briefly here. Overlap from other spectral lines (atomic, ionic and molecular) is common. The high temperature of the plasma causes species that are normally not problematic in flame spectroscopy to emit light. The high temperature exacerbates the problem because it causes line broadening, and broader lines are more likely to lead to spectral overlap than narrow ones. The argon that forms the plasma emits at approximately 200 different wavelengths and these emissions, together with the emission from

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assorted molecular species derived from water, entrained gases and the sample matrix, e.g., OH, N2þ, NH and NO, which produce a series of molecular bands that are spread throughout the wavelength range, can clutter the emission spectrum considerably. Line overlap by concomitant metallic species may also exist. This problem is especially severe when line rich elements such as iron, the lanthanides or uranium are present at an appreciable concentration in the sample. A background emission continuum is also present, the intensity and characteristics of which will vary, depending on the solvent loading, the solvent type and the matrix elements. Stray light, i.e., light that unintentionally reaches the detector, may also be a problem. This often arises from imperfections in the dispersing device, but many modern instruments suffer from this problem far less because of the quality of the optics. It should still be noted, however, that an analyte line that has a low intensity may be interfered with by a nearby very strongly emitting species. Most modern instruments have a software library giving a list of the relevant lines of the analytes, together with the potential interferences that may be experienced at each line. Inspection of the line tables and prior knowledge of the sample chemistry usually enables an analyst to pick a suitable “interference free” wavelength. Many instruments also enable background correction methods to be used in an attempt to compensate for any interference effects. There are a number of correction methods that may be used and these are discussed elsewhere [42]. 5.5.3

Instrumentation

5.5.3.1 RF generators The radio frequency (RF) generator may be of several types, e.g., crystal controlled or free running, 27.12 or 40.68 MHz. A far more detailed discussion on RF generators has been given elsewhere [42]. Both 27.12 and 40.68 MHz generators are used commercially and both normally produce RF power at up to 2000 W, although for normal usage, a power of between 1000 and 1500 W is typical. In general, the 40 MHz generators are regarded as being more stable, to couple more efficiently and to produce a lower background signal. Therefore, slightly improved LODs may be achieved for these instruments compared with those obtained from an instrument equipped with a 27 MHz generator. 5.5.3.2 Torches The plasma is formed in a torch, which is a concentric arrangement of quartz tubes that permits delivery of independently adjustable flow rates of argon to one end which is located in the RF load coil. The plasma is formed from argon gas flowing at a rate typically between 11 and 15 l min21. This flow is called the coolant or plasma gas flow. The auxiliary (intermediate) gas flow (typically 1 l min21) prevents the plasma from sitting too low in the torch and melting the innermost tube (the injector). The nebulizer (carrier) gas flow passes through

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the injector and punches a hole through the fireball, forming an annular, doughnut-shaped plasma. Technically, this is termed the annulus and the surrounding fireball the torus. Several types of torch exist, but most now conform to the basic Fassel style, which consumes substantially less gas (14– 17 l min21) when compared with the much larger Greenfield style torch (typically 12 –38 l min21 argon and 20–70 l min21 nitrogen). Similarly, the powers required to operate the Fassel torch are 1.0 –1.5 kW, compared with several kW for the Greenfield torch. The advantage of the Fassel style torch is that it is less expensive to operate, but its drawback is that it is less robust than the Greenfield torch and is less tolerant of gases other than argon. Many torches are demountable or partially demountable. This usually means that the coolant and auxiliary tubes are fixed, but that a different injector may be introduced. The shape and bore of the injector of the torch may have a large effect on the stability of the signal. Wider bore injectors (e.g., 2 or even 2.5 mm) are less likely to block than normal injectors (1.5 mm) if samples with a high dissolved salt content are introduced. If the injector is too wide, however, problems may be experienced in “punching” the plasma and the plasma may simply extinguish. Some injectors are made from ceramic or alumina and are therefore more resistant to hydrofluoric acid than quartz ones. Some injectors taper gently from wide to narrow bore and these are less likely to become blocked than injectors that have a step reduction in bore. A demountable or partially demountable torch therefore gives the analyst more freedom of choice to use an appropriate injector type. Several variations of the Fassel style torch exist. These include low flow torches that are much smaller (i.d. 13 mm compared with 18 mm for a conventional torch) and operate at a lower power (,1 kW) and with a lower consumption of gas (8 l min21) [44]. Micro-torches that operate at even lower power and gas flow also exist [45]. These torches are reported to offer similar sensitivity to their larger counterparts, but are more easily blocked because of their smaller diameter injectors. 5.5.3.3 Radial and axial plasmas The majority of instruments use a radial configuration wherein the plasma is viewed from the side. Axial instruments have the torch turned at a right angle so that it lies horizontally and the plasma is viewed end-on. There is a great deal of discussion as to which orientation offers the best performance. Some workers state that the axial instruments offer improved limits of detection by a factor of nearly 10, because the light from a much larger area may be detected. However, others have stated that they are prone to far more interferences, because they are less “optically thin” than the radial instruments. This means that the emitted light has to pass through a much larger distance, wherein it may be absorbed by other analyte atoms/ions, or that effects from molecular interferences are greater. A recent paper has addressed this problem and stated that self-absorption effects in axially viewed plasmas are partially controllable

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by careful optimization of the operating conditions [46]. The relative merits of both axial and radial instruments have been discussed by Brenner and Zander [47]. For axially viewed plasmas, a shear gas is often required to prevent thermal damage to the collection optics. It has the additional effect of removing the ICP tail flame that, under normal circumstances, would be rich in interfering species. 5.5.3.4 Wavelength isolation and detection systems Traditionally, ICP spectrometers utilize a monochromator and photomultiplier tube arrangement similar to AAS instrumentation. The monochromators, however, tend to have a much longer focal length and are of higher resolving power than those used for AAS. The higher resolution is required because the high temperature of the plasma excites many more species than does a flame and hence the emission from ion lines and some molecular species may become problematic if low-resolution monochromators were to be used. As its name suggests, instruments that utilize a monochromator can only interrogate one wavelength at any one time, and must scan over several wavelengths sequentially if more than one analyte is to be determined. The speed with which it can achieve this will govern the overall time required for analysis. Also, the accuracy and repeatability with which it finds each wavelength will have a large effect on the accuracy and precision of the analysis. Fortunately, once optimized, most modern instruments tend not to drift significantly (unless physical parameters, such as the room temperature, change). Polychromators have been developed commercially that may determine several analytes simultaneously. Here, several PMTs are arranged at intervals around a circle (known as a Rowland Circle) and as the light emitted from the plasma is diffracted from the grating, the wavelengths are separated and each PMT may detect one particular wavelength. Since the PMTs are not easily moved, the instrument is usually prepared in the factory to determine only specific wavelengths that the customer requires. Therefore, although simultaneous analyte determinations are possible using such instrumentation, it is cumbersome and extremely inflexible. Many modern instruments use an echelle-based spectrometer and specialized charge transfer device detectors. These may be either charge coupled devices, segmented charge coupled devices or charge injection devices. The theory behind their operation is beyond the scope of this chapter, but may be found in Refs. [48,49]. These devices function as an “electronic photographic plate” and are therefore truly simultaneous and may be used to determine several analytes together with suitable background correction points at once. There are a few drawbacks associated with their use, including the possibility of “blooming”, which occurs when an analyte may be so concentrated that the individual pixels detecting that wavelength may become saturated so that the charge spills over into adjacent pixels, thereby giving erroneously high signals for other analytes. Modern electronics have gone a long way in overcoming this

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problem. Also, these instruments do not have the flexibility of PMT based spectrometers, because they may have only a limited number of lines that can be detected. However, this number reaches into the hundreds and, therefore a suitable line should be available for most analytes. 5.5.3.5 Sample introduction systems Nebulizers and spray chambers The nebulizer/spray chamber assembly performs the same functions as those in the flame systems, i.e., to form an aerosol and then segregate large droplets from smaller ones. There are, however, a very wide variety of nebulizers and spray chambers. A description of many types has been given recently by Thomas [50]. Although this paper describes sample introduction for ICP-MS analyzes, the principles and most of the instrumentation are identical. The processes occurring within them have been discussed in two papers by Sharp [51,52]. Some nebulizers, such as the Meinhard style ones, are self-aspirating, i.e., they draw liquid samples up in a similar fashion to flame AAS nebulizers, and others require the sample to be pumped to them. Some nebulizers are easily blocked by the presence of dissolved or suspended solids, whereas others are far more tolerant. Examples of dissolved solids tolerant nebulizers include the crossflow, the Ebdon, the Burgener, the Hildebrand grid and assorted specialized pneumatic ones. The Ebdon, crossflow and some of the pneumatic ones are also tolerant of suspended solids such as those found in slurries. The choice of nebulizer will depend largely on the application. Some nebulizers are manufactured from inert polymers and are therefore more resistant to corrosive samples, such as those containing hydrofluoric acid. As well as acting as a droplet size filter, for those nebulizers that require sample to be pumped to them, the spray chamber acts as a pump noise dampener. A typical ICP nebulizer/spray chamber assembly will have a transport efficiency of 1–2%. Much more than this is likely to lead to severe plasma perturbation and its possible extinction. The problem is exacerbated by the aspiration of organic solvents. As discussed previously, these tend to have lower viscosity, lower surface tension and higher volatility (higher vapor pressure), leading to transport efficiencies substantially higher than 1–2%. Many of the more modern generators (especially the more robust 40 MHz ones) may be able to cope with the increased solvent loading, however, many of the older ones cannot and plasma extinction occurs. Boorn and Browner discussed introduction of organic solvents to ICPs [53]. If larger volumes of solvent are likely to reach the plasma, a desolvation device should be used. These come in several different forms, including membrane drier tubes [54], desolvation devices made in-house [55] and commercial equipment. Many of these devices decrease the amount of solvent reaching the plasma, whilst not significantly decreasing the analyte transport efficiency. The reduction in the solvent loading often leads to greater stability and, hence, improved LODs.

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An ultrasonic nebulizer increases transport efficiency to approximately 25–30%. These usually have built in heating and cooling devices to desolvate the aerosol and prevent plasma perturbation. Such a device, by increasing analyte transport and decreasing the solvent loading, improves LODs by typically 10-fold. The disadvantages of the nebulizer include its cost (approximately US $10,000) and the need to optimize the operating conditions carefully. Failure to optimize the heating and chilling temperatures is likely to lead to inconsistent nebulization and a noisy signal, which degrades the limits of detection. The whole device may be used without the need of a spray chamber. Spray chambers are available in assorted shapes and sizes. Their function is to separate large aerosol droplets from smaller ones and to act as a noise dampener. The efficiency with which the spray chamber achieves the latter function is often dependent on its internal volume. Larger spray chambers, such as the Scott double pass, dampen the noise quite effectively, whereas the numerous reduced volume ones are less effective. Conversely, the larger volume Scott style has a greater internal surface area and regions of dead volume, i.e., areas where the nebulizer gas flow does not rapidly flush any sample entering it away. Some analytes may exhibit a much longer memory effect or “washout” period in such a spray chamber, because it may become adsorbed to the glass walls (e.g., for lead) or may simply become trapped in an area of dead volume. For routine analysis, this problem is little more than annoying, because the main result is that a longer washout period is required between samples, which obviously extends the analysis time and increases the cost of analysis. However, when transient signals are obtained, especially those arising from chromatography, the memory effects can broaden the analyte peaks to the extent where they may start to merge. This is obviously undesirable, as confusion between different analyte species may result. Broader peaks also lead to lower signal-to-noise ratios and inferior limits of detection. The broadening effects are reduced for low volume spray chambers such as the cyclone and single pass styles. If a high quality liquid chromatography pump is used, the pump noise should be minimal and so the reduced volume spray chambers often offer the best resolution and sensitivity with adequate noise characteristics. Some corrosion resistant spray chambers manufactured from polymers (e.g., Ryton) are also available. Many spray chambers come with a jacket surrounding them, through which a cooling fluid is pumped to maintain the spray chamber at a constant temperature and improve stability. The cooling fluid is usually water, although anti-freeze may be used at a temperature of 25 to 2 108C to decrease the vapor pressure of organic solvents. This may help to decrease plasma perturbation by minimizing plasma loading. Sample throughput will depend on the type of instrument used. If a sequential spectrometer is used, then the determination of each analyte may take 20 s and so if 10 analytes are to be determined, then the analysis time for

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one sample may be 3–5 min; leading to a sample throughput of approximately 12–20 samples per hour. Inevitably, the sample throughput will be dependent on the number of analytes, with throughput increasing with a decreasing number of analytes. If a simultaneous spectrometer is used, then the same time will be required for one analyte as for 10, and so sample throughput is likely to be greatly increased. Assuming that the instrument has been set up properly, with the analyte lines “trimmed” so that measurement is made from the top of the peak rather than the rapidly sloping sides, then precision can be 1–5% RSD. The limits of detection for many analytes using optimal conditions are given in Table 5.3. It should be noted, however, that since the ICP has a definite structure and each region has a different temperature, ionizing properties, etc., then each analyte has an optimal set of conditions which yield the best sensitivity. The most critical parameters that govern analyte sensitivity are the viewing height, the nebulizer (carrier) gas flow rate, the power and, to a lesser extent, the auxiliary (intermediate) gas flow rate. If several analytes are to be determined, then compromise conditions will probably have to be used. Since compromise conditions are the “best overall”, but may not actually be optimal for any given analyte, the LODs obtained will be inferior to those shown in the table. Occasionally, other specialized nebulizers are used, e.g., the thermospray, the electrospray and the direct injection nebulizers. The theory of these is beyond the scope of this chapter, but may be found in Refs. [56,57]. Each of these nebulizers produces a very fine aerosol and usually operates at low flow rate (typically 10–50 ml min21). They can therefore be placed at the base of the plasma torch, omitting the spray chamber. Transport efficiency to the plasma is virtually 100%, but plasma extinction is prevented because only a similar absolute volume of sample reaches the plasma in any given time period as for a conventional nebulizer. Other sample introduction methods There is a plethora of alternative sample introduction methods for ICP spectrometry, including those described for flame spectrometry, i.e., FI and chromatography. A typical sample flow rate for HPLC is 1– 2 ml min21, which is compatible with the sample uptake of an ICP. However, when organic solvents are to be introduced, a desolvation device may be required to prevent plasma extinction. Similarly, for HPLC applications that use a mobile phase containing a high dissolved salt content, nebulizers and torch injectors that are tolerant of this must be used. The use of ICP-OES as a detector for elemental speciation studies has been described recently [58]. Liquid chromatography is normally coupled with ICP spectrometry via the nebulizer and spray chamber assembly, although the electrospray, thermospray and direct injection nebulizers have also been used. Gas chromatography has occasionally been coupled with ICP spectrometry, but few routine applications exist. The problem (as with GC –AAS coupling) is the transfer of the analyte to the atom cell in a

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Detection methods for the quantitation of trace elements TABLE 5.3 Limits of detection for ICP-OES under optimum conditions with conventional nebulization Analyte

LOD (mg l21)

Analyte

LOD (mg l21)


3 1.5 12 0.07 0.2 12 0.03 1.5 5 4 3200 2 0.3 0.7 0.3 1.5 6.5 3 13 4 8.5 0.5 18 4 10 0.02 0.6 0.05 0.1 0.3


4 1 4 2 6 18 14 7 0.8 20 3 6 18 0.4 37 5 15 0.02 9 5 27 17 0.6 16 1.5 18 2 17 0.2 0.9

sufficiently hot form to prevent condensation. Coupling of a heated transfer line to the ICP torch can be problematic because any metal components within the transfer line may act as an aerial for the RF power, leading to a potential hazard. The transfer line must normally be placed as far up the torch as possible to prevent analyte condensation, whilst ensuring that the end of it does not melt and that potential hazards are avoided. A further problem is that the

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transport gas flow rate typical of GC separations is not sufficient to punch a sample channel into the plasma. A make up gas is therefore usually required. This too often requires heating to prevent analyte condensation. Sample throughput is obviously dependent upon the length of time required for the chromatography, but for many HPLC and GC applications, only 3–8 samples per hour may be analyzed. Precision is also dependent on the method of sample introduction, but often lies in the region of 3 –10% RSD, and may be improved if an appropriate internal standard is used. Chemical vapor generation is a popular method of sample introduction for ICP spectrometry as well as flame spectrometry and the benefits of this approach are the same. The one problem that may be encountered is the production of excess hydrogen as a by-product of the hydride generation reaction. As discussed previously, some instruments are relatively intolerant of gases other than argon and so, if large quantities of hydrogen enter the plasma, perturbation may occur. The use of an automated continuous hydride generator is highly recommended, as is careful optimization of both the reagent concentrations, to minimize the excess hydrogen produced, and the instrumental operating conditions. A recent example of HG-ICP-OES is presented by Overduin and Brindle [59]. Some workers have coupled chromatography with HG prior to ICP-OES detection [60]. Again, this would improve the sensitivity when compared with HPLC –ICP-OES. Just as with HG-AAS, sample pretreatment may be necessary to transform some species into a form that produces a hydride. Electrothermal vaporization has also been used to introduce assorted sample types to ICP-OES instrumentation. The principles of ETV are the same as those described previously. Again, it is a useful technique when only limited sample volume is available. It may be used to analyze liquid samples, slurries and solid samples directly. The same heating programs are used, i.e., a dry, a pyrolysis and a vaporization stage followed by a high temperature cleanup step. For ETV–ICP-OES (and -MS), the vaporization stage does not have to atomize the analyte. As long as the temperature is sufficiently high to vaporize the analyte, either as an atom or as a compound, it may be transported in a flow of inert gas to the plasma. Since this is at a temperature of 6000 –10,000 K and the sample arrives in a dry form, the plasma has more energy available for atomization and excitation, so it will dissociate the vast majority of analyte compounds. If adequate pyrolysis temperatures are used, the analyte is separated from the majority of the matrix, thereby facilitating interference free determination. Two reviews of the process of ETV–ICP have been published; an early one by Carey and Caruso [61] and a more recent one that compares ETV with laser ablation (LA) [62]. Sample throughput is again dependent on the type of instrumentation used and the number of analytes to be determined. Since the signal obtained is in the form of a transient, for sequential instruments, a number of replicate analyzes will have to be performed because the instrument will not have sufficient time to scan to more than one

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wavelength before the signal returns to the baseline. Since each replicate analysis may take up to 2 min, and three replicates are normally required per element, then if three elements are to be determined, each sample will take 20 min. This may be longer if solids are weighed directly into the graphite atomizer. Precision should be roughly equivalent to that obtained with conventional ET-AAS. Again, LODs should be measured as an absolute amount rather than as a concentration. In general, sensitivity is improved because transport efficiency of the analyte to the atom cell is substantially larger than for conventional solution sample nebulization, although condensation of the analytes within the transfer line may occasionally decrease the transport efficiency. Direct sample insertion (DSI) and in-torch vaporization (ITV) are off-shoots of ETV. Sample (either liquid or solid) is dispensed onto the tip of a probe, usually made of graphite or a refractory metal. The probe is then inserted directly up the injector of a specialized torch towards the plasma. Sample transport efficiency is close to 100%, but the analyte is not separated from the matrix and determinations are thus more prone to interferences. The technique was reviewed in 1990 by Karanassios and Horlick [63] and again in 1999 by Sing [64]. In LA, a laser beam is focused either onto, or just above, the surface of a sample. The laser vaporizes a small area of the sample and the vapor is transported in a stream of inert gas to the plasma. The laser may be focused onto extremely small areas (, 0.1 mm) and hence may be used, for example, to analyze fluid inclusions in geological materials. If the laser is aimed at the same spot on some types of sample, then depth-profiling is possible, i.e., the top 0.1 mm of surface is analyzed, followed by 0.1 mm below that, etc. This may be of use for some sample types where the depth may be correlated directly with age. There are several problems associated with quantitative analysis using LA –ICP-OES. Since only a very small area of sample is vaporized, if a bulk analysis of a sample has to be performed, it is essential that it is homogeneous, otherwise accuracy and precision will be affected. Since the laser radiation will interact with different types of sample to different extents, it is necessary for calibration to be performed using materials that have an identical matrix. Failure to calibrate properly will lead to the results being, at best, semiquantitative. Laser ablation may also be used to “map” the surface of a sample, i.e., to determine how an analyte concentration varies over the surface of the sample. Laser ablation produces transient signals so, as with ETV sample introduction, a sequential instrument will not be able to determine typically more than one analyte at any given sample site, whereas a simultaneous instrument could potentially do several. A review of the interaction of laser radiation with samples has been given by Darke and Tyson [65]. A more recent review comparing ETV and LA has been made by Kantor [62]. Accuracy and precision of the technique will depend on the sample homogeneity and on how

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closely the standards are matrix matched to the samples. These issues are discussed in more detail by Russo in a later chapter in this book. Solid samples may be introduced directly to the plasma by several techniques, including slurry sampling, ETV, DSI and LA. In addition to these methods, some instruments come with a solid sampling (SS) accessory. The requirement for this is that the sample must conduct electricity and therefore metallurgical samples, including steels, brass, other alloys, wires and even coal fly ash, may be analyzed. An arc or a spark is used to ablate material from the surface of the sample and the dry aerosol produced is transported to the plasma in a stream of argon. The sample may be in the form of rods, powders or briquettes. The technique has a few variants but most deliver a precision of 0.2 –1% RSD for a concentration of 1%. Custom accessories have even been produced commercially that enable the determination of wear metals in lubricating oils [66]. A direct current arc has been reported as giving a precision of 3–10% RSD at the 1% concentration level. One of the drawbacks with this method of sample introduction is the need for very closely matrix-matched standards. 5.5.4

Figures of merit

As noted throughout the text, sample throughput and precision will depend on the instrumentation used, the number of analytes to be determined and the sample introduction method. Assuming a liquid sample (or a digested solid sample) is to be analyzed using conventional nebulization, then a modern simultaneous instrument may analyze 25 –30 samples per hour and use only 1–5 ml of sample. A sequential instrument will analyze fewer than this and will consume substantially more sample. Both types of instrumentation should provide analytical results with a precision of 1–5% RSD. The linear range should extend to at least five orders of magnitude for ICP-OES determinations, although it should be noted that for some applications, e.g., chromatography, the chromatographic column may become overloaded at higher concentrations of analyte. 5.6 5.6.1

INDUCTIVELY COUPLED PLASMA-MASS SPECTROMETRY Introduction

Inductively coupled plasma-mass spectrometry was developed in the 1970s and commercial instrumentation was available in the 1980s. It is a coupling of an ICP with mass spectrometric detection. The principles behind the sample introduction and the processes of plasma formation and of desolvation, dissociation, atomization and ionization within the plasma are the same as those described previously. It has become a very popular method of analysis because it has several advantages over other techniques, i.e., it is extremely

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rapid, with simultaneous and quasi-simultaneous instruments being available; it offers improved sensitivity over many of the other techniques for most analytes and the mass spectrum produced from any sample is far more simple than that obtained from an emission instrument. There are several different types of ICP-MS instrument. Each of these will be discussed in more detail later. The one major drawback is the cost, ranging between US $80,000 and US $400,000, depending on the type. A schematic diagram of a general ICP-MS instrument is shown in Fig. 5.1. 5.6.2

Theory

As discussed previously, the sample introduction systems used for ICP-MS can be identical to those for ICP-OES. Similarly, the processes occurring within the plasma are also identical. It is worth noting, however, that since all ICP-MS instruments detect the analytes according to a mass-to-charge ratio ðm=zÞ, for any signal to be detected, the analytes must become ionized within the plasma. The extent of ionization will depend on several factors, but most importantly on the first IP of the analyte. The plasma consists of ionized argon that has a first IP of 15.76 eV. Therefore, any element that has an IP less than this will be at least partially ionized. Cesium, having a first IP of 3.89 eV, will be 100% ionized, but arsenic has a first IP of 9.81 eV and will be only 30– 40% ionized. Fluorine, with a first IP of 17.42 eV, will not be ionized and therefore cannot be determined directly by ICP-MS (using an argon plasma).

Fig. 5.1. Schematic diagram of an ICP-MS instrument.

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Once the ions have been formed, they must pass from atmospheric pressure through several chambers of increasingly high vacuum to the mass separation and detection stages. Several different types of mass filters and detectors exist, which will be discussed in a later section (Section 5.6.3.1). A more detailed account of how the ions pass from the plasma through the expansion chamber and ion lens system to the mass filter and detector may be found elsewhere [2,67 –69]. 5.6.3

Instrumentation

5.6.3.1 Mass filtration A discussion of ICP-MS instrument types has been given in a recent book chapter [70]. In addition, several tutorials have also been published recently that have discussed the various types of mass filters [71– 73]. The most common mass analyzer is the quadrupole. Four rods (often made from molybdenum) are arranged in a set of two pairs in a square orientation. Two rods have a DC voltage on them and the other two have RF voltage. The magnitude of the voltages will allow one m=z to pass through the rods towards the detector, whilst ensuring that all ions of other m=z collide with one of the rods, hence preventing them from being detected. A short while later (often , 1 ms) the magnitude of the voltages changes and an ion of different m=z is allowed to pass to detection. A quadrupole instrument is therefore not truly simultaneous, but instead is so rapidly sequential as to be regarded as being quasi-simultaneous. This device is relatively inexpensive and robust, but has a relatively poor resolution, i.e., between 0.7 and 1.0 atomic mass units (AMU). This means that it is more prone to spectral interferences than other mass analyzers. The problem of interferences is discussed in a later section (Section 5.6.5). Other, more highly resolving ICP-MS instruments are available commercially, although at much greater cost. An example is double focussing magnetic sector instrumentation. The principle of operation of this instrumentation has been discussed by Thomas [72]. These instruments have a resolving power of up to 10,000 compared with a quadrupole-based instrument that has a resolving power of only 300. This large improvement in resolution will enable distinction to be made between some interfering polyatomic ions and analyte ions (see Section 5.6.5). The resolution of the instrument can be set by the analyst so that individual interferences can be overcome. The resolution required obviously depends on how close the interfering species is in mass to the analyte. A few examples include 34Sþ and 16O18Oþ that may be separated using a resolution of 1300 and 75Asþ and 40Ar35Clþ, which require a resolution of 7725. It should be noted, however, that the higher the resolution required, the lower the sensitivity. It is therefore advisable to use the lowest resolution necessary to achieve interference free determination. As well as specializing in the reduction of interferences, it can be used to gather extremely precise isotope ratio data. If used in low resolution and for interference free analytes, a

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precision of 0.01–0.05% is obtainable. In addition, the sensitivity at low resolution can be at least an order of magnitude superior to quadrupole instruments. Some instruments of this type use several detectors. These multicollector instruments have the capability of detecting and measuring multiple ions simultaneously and are regarded as being capable of producing the ultimate in precision for isotope ratio measurements. The time-of-flight ICP-MS (ICP-TOF-MS) is the most recent development of ICP-MS instrumentation, being commercially available since 1998. Although still relatively immature, it does have several potentially important advantages over other instrument types. This instrumentation permits truly simultaneous detection and therefore has advantages when measuring transient signals; it produces high precision isotope ratios and can decrease the amount of time required to complete an analysis. A more complete discussion of the theory behind ICP-TOF-MS, the instrumentation, and its relative merits, can be found in the literature [73– 75]. Examples of the use of ICP-TOF-MS instruments include the determination of isotope ratios [76] and the detection of rare earth elements in seawater after FI matrix elimination and pre-concentration [77]. 5.6.3.2 Reaction cells The use of reaction cells has been discussed recently by Thomas [78]. These devices are placed between the ion lens system and the mass filter. As the ions from the plasma enter, a quadrupole, hexapole or an octopole cell helps focus them towards the reaction cell gas (usually helium or hydrogen). As the ions and the reaction gas collide, the polyatomic ions fracture, leading to a decrease in the interference observed [79,80]. The use of reaction cell technology has improved the analytical capabilities of quadrupole based ICP-MS instruments in terms of both interference and, for some analytes, limits of detection. Even for analytes that are renowned for being difficult to determine using ICP-MS, such as Fe, LODs significantly below 1 ng ml21 may be obtained when a reaction cell is used to overcome the interference caused by 40Ar16Oþ on 56Feþ. 5.6.3.3 Detectors There are several types of detector available for ICP-MS instruments. The channel electron multiplier is a horn shaped device that is coated with a semiconductor. As an analyte ion impinges on the surface, an electron is ejected which is accelerated down towards the other end of the tube, but on its way down, collides with the wall of the tube ejecting secondary electrons. Each of these is also accelerated towards the other end of the tube and they collide with the wall producing further electrons. An avalanche effect is therefore built up. The number of electrons reaching the pre-amplifier at the far end of the tube is proportional to the number of analyte ions impinging on the detector, i.e., the concentration of the analyte. The discrete dynode electron multiplier functions in a very similar way, but instead of using a continuous tube like dynode, as in

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the channel multiplier, it uses a series of discrete dynodes. The Faraday cup may be used when ultra-trace detection limits are not required. A more detailed description of the different detector types and how they may be used either in combination, or singly, to achieve a linear range spanning nine orders of magnitude, has been given by Thomas [81]. 5.6.4

Different types of analysis

Analysis using ICP-MS offers a wide variety of options. It may be used in the normal, fully quantitative analysis mode, or as a detector for chromatographic separations where perhaps only one or two target elements may be determined. Other time-resolved functions include serving as a multi-element detector for ETV, LA and FI analyzes. Semi-quantitative analysis is also achievable wherein a mass response curve is prepared using one mixed standard of perhaps six elements, each at either 10 or 100 ng ml21. The response from each of these elements is calculated and the line of best fit between each of the points plotted. The software will “assume” that for the same concentration, any other analyte will have a response on that line of best fit. The method is semiquantitative, because only an estimate of the analyte’s concentration can be made, although it is normally accurate to within a factor of two. A suite of nearly 70 elements can have their concentrations estimated within 10–30 s. The method is especially useful when a sample of completely unknown characteristics must be analyzed. A semi-quantitative analysis will enable the analyst to identify an appropriate concentration range for the standards prior to a fully quantitative analysis or it may be used to identify suitable internal standards. Isotope ratio measurements for isotope dilution (ID) is regarded as being a definitive method of analysis. The subject of isotope ratio measurements has been discussed in great detail in a recent book [82]. Although possessing a number of advantages, the greatest drawback is cost; the price of pure isotopes or even isotopically enriched metals can be prohibitive. For species specific ID, the isotopically enriched compound will probably have to be prepared in-house and the cost of enriched isotopes is high. Once prepared, the isotopically enriched compound should be analyzed using an assortment of instrumental methods, such as nuclear magnetic resonance, so that an estimate of its purity can be made. A discussion of ID methods for trace metal speciation has been published recently [83]. 5.6.5

Interferences

There are several types of interference that may occur in ICP-MS analyzes. Those attributable to sample transport effects are the same as for ICP-OES analyzes. The presence of 0.5% dissolved solid and high concentrations of acid in a sample will not nebulize with the same efficiency as a standard prepared in

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2% nitric acid, and hence a different signal may be obtained for the same concentration of analyte. Additionally, space charge interferences may arise when the ion current in the sampled analyte beam exceeds the capacity of the ion lens systems to maintain focusing and the transmission efficiency is changed. This usually occurs as a consequence of the presence of high concentrations of concomitant elements and tends to favor the transmission of higher mass analytes from the beam. These effects may be partially overcome by the use of at least one internal standard. As usual, the internal standard should not be present naturally at a significant concentration in the sample and should match, as closely as possible, the ionization energy and mass of the analyte. If a range of analytes is to be determined, e.g., 65Cuþ and 66Znþ, 111Cdþ and 208Pbþ, then it may be necessary to have up to three internal standards, one at the lower m=z range such as 59Coþ, one in the middle, such as 115Inþ and one at the higher m=z range, such as 205Tlþ. The use of more than one internal standard may lead to greater long-term instrument stability. Since the mass response curve (i.e., the signal obtained per unit concentration over the mass range) may change with time, the use of a single internal standard, such as 115 Inþ, may be insufficient. For example, if after 50 samples have been analyzed, a standard containing 100 ng ml21 of analytes is analyzed as a check standard, it may be found that the concentrations range from 70 ng ml21 at the lower mass range up to 130 ng ml21 at the higher end. If two or more internal standards are used, instrumental drift can be diminished to , 10% over a whole day’s work. There are several types of spectroscopic interference. The most common is that of polyatomic interferences. This occurs when two (or more) atoms form a molecule that has nominally the same mass as the analyte. The vast majority of this type of interference occurs below m=z 80 (the argon dimer) and these often contain argon (from the plasma) combined with an ion present in the matrix of the sample. Examples include the interference from 40Ar35Clþ on 75Asþ, 32 16 S O2þ on 64Znþ, 40Ar16Oþ on 56Feþ and 23Na40Arþ on 63Cuþ. A far more complete list is given in a review by Evans and Giglio [84]. In a paper by Nonose and Kubota [85], the interferences observed in quadrupole and in high resolution ICP-MS instruments are compared. Isobaric interferences occur when two analytes have isotopes of nominally the same mass, such as for 113Cdþ and 113Inþ. However, most elements have at least one isotope free from such interference. Doubly charged ion interferences also occur, but the only element that suffers from this to any significant degree is barium, because it is the only commonly determined analyte that has a second IP , 15:76 eV: The overall effect is that the signal for 138Baþ decreases whilst the signal for 138Ba2þ (which is equivalent to 69Gaþ) increases. The extent of ionization may be different between samples and standards and so either/or the Ba and Ga determination may be affected. Another type of interference arises due to the formation of metal oxides. This is similar to polyatomic interferences and occurs mainly for the rare earth elements, with

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the lower mass analytes, that are usually there at appreciably higher concentration than the ones with higher m=z, combining with oxygen to give an elevated signal at M þ 16, e.g., 141Pr16Oþ interfering with 157Gdþ. There are several means that may be used to overcome spectroscopic interferences. The easiest is to use an alternative isotope that does not suffer from interferences, although some elements are mono-isotopic. Sometimes, it is possible to perform some chemistry on the sample prior to analysis such that the interferences are separated from the analytes. An example has been the use of a FI technique with a micro-column of a chelating resin to retain the analytes, whilst potential interferences were washed to waste by an appropriate buffer [86]. Some of the other alternative sample introduction methods also succeed in separating the analyte from potential interferences, such as chemical vapor generation and ETV. The introduction of alternative gases has also been demonstrated to overcome some interferences. The introduction of 4% v/v nitrogen to the nebulizer gas flow has been shown to markedly reduce the interference from 40Ar35Clþ on 75Asþ [87]. The mechanism by which this works is uncertain, but a concomitant increase in the signal at m=z 51 (14N37Clþ) and at 49 (14N35Clþ) would appear to indicate that a favorably competitive reaction is occurring. Hydrocarbon gases have also been shown to be beneficial for many analytes [88,89]. As well as chemical methods of interference removal, instrument manufacturers have also produced several hardware and software methods. The software based methods are mathematical algorithms that rely on correction factors. For instance, the extent of 40Ar35Clþ interference on 75Asþ can be estimated by taking into account Se signals at m=z 77 and 82. The Se isotope at m=z 77 is also interfered with by chloride (40Ar37Clþ), but the isotope at m=z 82 is not. Since each isotope’s theoretical relative abundance is known, any deviation from this known ratio can be measured and a correction made. Hardware modifications and accessories offer a more reliable method of overcoming interferences. The collision cell and the dynamic reaction cell have been successfully used to overcome interferences (see Section 5.6.3.2). The use of high resolution mass analyzers also overcomes the vast majority of common interferences. Most polyatomic interferences exist only for quadrupole-based instrumentation. This arises because even though the interferences and analyte ions do have a slightly different mass (e.g., 75Asþ actually has an m=z of 74.926 whereas 40Ar35Clþ has m=z 74.932), as discussed previously, the quadrupole only has unit mass resolution and can therefore not distinguish between the two. Magnetic sector instruments are capable of much higher resolution and can distinguish between the two masses, hence eliminating the interference. 5.6.6

Sample introduction techniques

In general, the principles, advantages, drawbacks and applications of the assorted sample introduction techniques are the same for ICP-MS as for ICP-

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OES. As well as increased sensitivity, the one big advantage of ICP-MS over some ICP-OES instrumentation is that it is simultaneous (or at least far more rapidly sequential). This means that for sample introduction techniques that produce transient signals, such as LA, ETV, FI and chromatography, more than one element may be determined at any one time. For liquid chromatography utilizing a mobile phase with a high dissolved salt content, coupling with ICPMS may lead to additional problems. As well as potentially blocking the nebulizer (if an appropriate one is not used) and the injector of the torch, blocking of the orifice of the sampler cone may also occur. Inevitably, this will lead to significant signal drift, until blockage is complete, at which point no signal will be obtained. A similar problem arises for the introduction of organic solvents. The solvent will pyrolyze within the plasma and will produce large quantities of soot. Whereas in ICP-OES this soot will pass harmlessly to waste via the fume extraction system, with ICP-MS instrumentation it may clog the sampler cone. In addition, the ion lens system also becomes dirty and the instrument will have to be dismantled so that it can be cleaned. The problems arising from soot deposition can be overcome by introducing oxygen (3 –5% v/v) into the nebulizer gas flow. This turns the pyrolysis into an oxidative combustion process, and so the soot is burned off as carbon dioxide, thereby preventing sampler cone blockage. It should be noted, however, that the amount of oxygen used is critical. If too much oxygen is introduced, then the nickel sampler cone itself becomes oxidized away. This process can occur rapidly and a new cone can become unusable within a few minutes. Platinum or platinum tipped sampler cones are also available, and these tend to be more robust and resilient to oxidative attack, but are obviously substantially more expensive. Some workers have coupled GC with ICP-MS [90], but the coupling requires a heated transfer line that has to be constructed to enable safe and simple coupling. A commercial GC –ICP-MS instrument has now been produced, so the overall coupling is more robust. Some workers have also coupled capillary electrophoresis (CE) with ICPMS. The flow rate through a CE instrument is typically at the low ml min21 level, or perhaps even nl min21. A specialized coupling is therefore required to make sure that the flow rate of the CE and the uptake rate of the ICP-MS are compatible, often achieved using a micro-flow nebulizer or a DIN [91]. Occasionally, a gas inlet is used to prevent suction from the nebulizer destroying the chromatographic separation by drawing the sample through at an accelerated rate. Since the injection volume is exceptionally low (again at the nl or ml range), the concentrations detected are normally at the mg ml21 range, although the absolute amount is at the pg or fg level. There are therefore very few applications for this coupling and its use is far from routine. Other sample introduction methods, including LA, ETV and chemical vapor generation, share the same advantages and disadvantages as discussed for analyzes by ICP-OES.

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5.6.7

Figures of merit

The limits of detection obtainable by conventional nebulization ICP-MS are usually at least two orders of magnitude lower than those obtainable by ICPOES. The LOD will depend on a number of factors, including the ionization energy of the particular analyte, the number of isotopes the analyte has (if an element has six or seven isotopes, the signal will be split between these and hence sensitivity will be less than for an element with only one isotope), the acquisition (integration) time and potential interferences. In addition, the operating parameters and the type of instrumentation used will also have a great effect. For a quadrupole based ICP-MS, approximate LODs are shown in Table 5.4. At a low-resolution setting, a magnetic sector instrument may improve these by at least an order of magnitude. Obviously, for sample introduction methods that give increased transport efficiency to the atom cell (ETV and chemical vapor generation), the LODs shown in the table can also be improved by over an order of magnitude. Under standard conditions, the linear dynamic range spans five or six orders of magnitude. If, however, both pulse counting and analogue modes are used, then the linear range may be extended to eight or even nine orders of magnitude. It should be noted though that the standards still have to be prepared in an appropriate range for the individual analytes within the sample. Sample throughput will again depend on the method of sample introduction and the time of acquisition/number of replicate measurements, but for conventional nebulization and a 10 s acquisition (integration time) for each of three replicates, potentially up to 50 or 60 samples may be analyzed in an hour. The number will also be affected by the speed of washout from the spray chamber, and so a fast clearing spray chamber will enable more samples to be analyzed per hour than a slower one. Since the instruments are simultaneous (or quasi-simultaneous), a large number of analytes may be determined simultaneously and an enormous amount of data may be collected in a short period of time. Precision will depend on the application. For conventional nebulization a precision better than 1% RSD may be obtained. For isotope ratio and ID measurements, precision would normally be expected to be better than 0.1% RSD. For other sample introduction techniques, such as LA or ETV, precision will depend on the homogeneity of the sample rather than the detection technique. 5.7 5.7.1

ATOMIC FLUORESCENCE SPECTROMETRY Introduction

Atomic fluorescence spectrometry (AFS) is theoretically applicable to all of the commonly determined analytes. Modifications to standard flame instruments

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Detection methods for the quantitation of trace elements TABLE 5.4 Limits of detection for quadrupole ICP-MS under optimum conditions and using conventional nebulization Analyte

LOD (mg l21)

Analyte

LOD (mg l21)

Ag Al Au Ba Be Bi Ca Cd Co Cr Cs Cu Dy Er Eu Fe Ga Gd Ge Hf Hg Ho In Ir K La Li Lu Mg Mn

0.005 0.05 0.005 0.001 0.001 0.001 0.5 0.005 0.001 0.005 0.001 0.005 0.01 0.001 0.001 0.05 0.001 0.001 0.05 0.005 0.001 0.001 0.001 0.005 0.5 0.05 0.001 0.001 0.05 0.0004


0.005 0.05 0.005 0.001 0.005 0.5 0.001 0.005 0.001 0.005 0.001 0.005 0.005 0.05 0.05 0.5 0.005 0.001 0.005 0.001 0.05 0.001 0.05 0.001 0.001 0.001 0.005 0.005 0.001 0.005

may be used to obtain one capable of detecting atomic fluorescence. Despite its general applicability, in recent times AFS has been used almost entirely for the vapor generating elements. Indeed, commercial instrumentation has been produced that specializes in detecting As, Sb, Se and Te and another that detects Hg. The specialized commercial AFS detectors are relatively cheap, costing ,US $5000, although fully automated systems are also available at

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greater cost. Some research papers have been published concerning laser induced fluorescence (LIF) or laser excited atomic fluorescence (LEAF). These, however, are research methods at present and are not used routinely. A review of LEAF spectrometry has been published recently [92].

5.7.2

Theory

The theory of AFS may be found elsewhere in the literature [93]. Briefly, radiation from an intense light source (line sources are used rather than continuum ones, although high intensity light emitting diodes that have a bandwidth of 20–40 nm may also be used) is used to excite the analyte which, upon relaxation back to a lower energy state, emits light of discrete wavelengths, depending on the transition involved. The intensity of the light source has a large impact on the sensitivity, as the fluorescence intensity is proportional to the intensity of the source. Standard HCLs may be used, but greater sensitivity is obtained from boosted HCLs. A laser would provide the most intense source and a number of these have been used for this purpose, including standard YAGs, diodes, dye lasers and optical paramagnetic oscillators but, apart from diode lasers, most are difficult to operate and costly to maintain. Atomic fluorescence is exceptionally specific, ensuring that spectral interferences are minimal.

5.7.3

Instrumentation

The instrumentation used can be basically the same as for F-AAS, although the light source must be positioned at a right angle to the detector so that emission from the lamp is not detected as fluorescence. Since AFS is so specific, it does not require a complex line isolation device such as a monochromator. Instead, simple filters will suffice, although some high throughput multi-reflectance (interference) filters have also been used. These reportedly transmit 80% of the wavelengths of interest whilst virtually eliminating background noise. The atom cell in commercial AFS detectors is usually an argon/hydrogen diffusion flame. This is a low temperature flame that is used to dissociate the hydrides of these analytes. Both argon and hydrogen have low quenching crosssections for fluorescence. For the commercial Hg detector, a simple quartz cell or open argon sheathed chimney is used. Since atomic vapor is introduced, there is no need for a heat source. Detection is usually with a PMT. It should also be noted that atomic fluorescence has also been achieved using an ICP as an atom cell [94]. Although a commercial instrument was marketed briefly, this too has only really been of research interest. A review of ICP-AFS has been produced by Greenfield [95].

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5.7.4

Sample introduction

The majority of AFS techniques utilize chemical vapor generation to introduce the sample. The usual problems with chemical vapor generation are observed and therefore optimization of the generation chemistry to decrease interferences and to transform non-vapor forming species into ones that can is required. Often, a membrane drier tube is used to prevent the ingress of water vapor into the atom cell, since its presence may lead to light diffraction, quenching of the fluorescence and possible interference. Chromatography, coupled with chemical vapor generation and AFS detection, has been used frequently as an alternative to ICP-MS detection, because the LOD is comparable whilst the overall cost of the instrumentation is substantially less. An example of HPLC – HG-AFS that also incorporated an on-line microwave transformation of inert species into forms that can generate a hydride has been published by Gomez-Ariza et al. [96]. Several studies have coupled chromatography directly with atomic fluorescence. As an example, Puskel et al. used a specialized type of nebulizer (a hydraulic high pressure nebulizer) to introduce assorted selenium species [97]. 5.7.5

Interferences

Since the majority of applications of AFS utilize chemical vapor generation sample introduction, many of the interferences observed occur in the vapor generation step. Methods of overcoming these have been described previously. Once the vapor enters the atom cell, several types of interference may occur, including quenching by molecular gases (and other species), leading to a dramatic reduction in sensitivity. To minimize or prevent this, commercial AFS detectors use a gentle argon purge flow. Similarly, if water vapor enters the atom cell, quenching or diffraction/scattering of the light may occur. The ideal flame is the argon/hydrogen diffusion flame, but this has a temperature that is too low to prevent chemical interferences and is another reason why the vapor generation technique is the preferred method of sample introduction. 5.7.6

Figures of merit

Atomic fluorescence, especially when the sample is introduced by a chemical vapor technique, is exceptionally sensitive. For mercury, the detection limit is reported to be less than 1 ng l21 and for other analytes, such as As, Se, Sb and Te, the LOD is approximately 10 ng l21. The technique has a linear range spanning five orders of magnitude. Sample consumption will depend on the mode of chemical vapor generation used, i.e., batch or continuous; but is likely to be several milliliter. Precision is comparable to other common detection techniques and is typically better than 5% RSD.

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5.8

OTHER ATOMIC ABSORPTION, EMISSION AND FLUORESCENCE METHODS OF DETECTION

There are a number of other detection methods that have been used to determine trace analyte concentrations, although many are either research methods or have fallen virtually into disuse. 5.8.1

Microwave induced plasma

The microwave induced plasma (MIP) is the most commonly used of the other methods. This plasma is formed from microwave radiation and usually helium as the support gas, although other gases have also been used. The normal helium MIP is not a robust plasma and analytes must usually be introduced in a gaseous form since the plasma will be extinguished by the presence of any solvent. Recently, however, a very high power MIP (up to 1500 W) has been sustained while liquids were aspirated [98]. Since helium has much higher ionization energy than argon, the MIP is capable of detecting several analytes with greater sensitivity than argon based plasmas. Examples include the halogens (including fluorine), sulfur and nitrogen. The MIP is used mainly in the atomic emission mode, although in the reference given above, a mass spectrometric detection method was used. In this latter mode, a helium-based plasma is useful because the argon polyatomic interferences observed in argon plasmas are largely eliminated. Detection limits were at the ng level. For MIP-AES, a number of systems have been used. These include different types of microwave cavity, e.g., Beenakker, slab-line and surfatron, and different types of line isolation and detection devices, e.g., Czerny –Turner monochromators, Rowland circle style polychromators and oscillating bandpass filters. The development of a commercial instrument that has coupled together GC and MIP-AES has ensured that this has become the most common method of sample introduction. The chromatography coupled with the MIP detection means that the vast majority of analyzes performed are speciation-based techniques. Speciation analyzes with MIPAES detection, and many of the fundamentals of the technique, have been reviewed recently [99]. The technique yields LODs in the range of 0.1 –5 pg s21 and linear ranges extend over four orders of magnitude. Sample throughput depends on the length of time required for the chromatography to be complete, but is unlikely to exceed 10 analyzes per hour. The sample throughput will also be dependent upon whether a temperature gradient was used to achieve the separation. If the chromatography is not isothermal, the GC oven will require time to cool to its starting temperature before another sample can be introduced. Precision is typically around 5% RSD.

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5.8.2

Direct current plasma

The direct current plasma (DCP) is an economical plasma to operate because it uses approximately 8 l min21 of argon and runs using a power of approximately 1000 W. However, it suffers very badly from interferences caused by easily ionized elements. Most applications require blanks, samples and standards to be spiked with high concentrations of lithium or barium to offset these effects. Although it is stable to the introduction of both aqueous and organic solvents, its use has declined almost to the point of non-existence. Commercial instrumentation produced nearly two decades ago used a similar line isolation device as found in many ICP instruments (an echelle spectrometer) and therefore had excellent resolution. Sample throughput and consumption is similar to that with the ICP. Precision is similar to that obtained with F-AES, but LODs are usually superior, especially for the hard to excite elements. In one relatively recent paper, the DCP was used to determine B in soils [100]. The linear dynamic range was reported as having five orders of magnitude and the LOD was 0.1 mg l21. 5.9 5.9.1

SECONDARY ION MASS SPECTROMETRY Introduction

Secondary ion mass spectrometry (SIMS) is based on the mass spectrometry of ionized particles that are emitted when a surface, usually a solid, is bombarded by energetic primary particles, which may be electrons, ions, neutrals or photons. The emitted or “secondary” particles will be electrons, neutral species, atoms or molecules or atomic and cluster ions. The large majority of species emitted are neutral, but it is the secondary ions that are detected and analyzed by the mass spectrometer. This is a process that provides a mass spectrum of a surface and enables a detailed chemical analysis of a surface or solid to be performed. The first mention of sputtered secondary ions in the literature was made in 1910 by J.J. Thomson [101]. The first regular secondary ion mass spectrometer was based on a patent by Herzog in 1942 [102,103], and the first successful studies of surface compositions using mass-analyzed sputtered ions were made by several teams in the early 1950s [104,105]. An accelerated development of the field was stimulated by new efficient designs of narrow-beam primary ion columns [106] and of ion optics for “direct” imaging [107]. The late 1960s saw the emergence of the first commercial instrumentation [108,109] and the coining of the SIMS acronym [110]. Static SIMS emerged as a technique of potential importance in surface analysis in the late 1960s and early 1970s as a consequence of the work of Benninghoven and his group in Mu¨nster [110]. Whilst the SIMS technique is basically destructive, the Mu¨nster group demonstrated that using a very low

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primary particle flux density (,1 nA cm22), spectral data could be generated in a very short time scale compared to the lifetime of the surface layer. The information so derived would be characteristic of the chemistry of the surface layer because, statistically, no point on the surface would be impacted more than once by a primary particle during the analysis. Today, SIMS has an acknowledged place among the major techniques of surface analysis and microstructural characterization of solids. Profiling or other applications of SIMS that are not static are referred to as dynamic. Dynamic SIMS has found extensive application throughout the semiconductor industry where the technique had a unique capability to identify chemically the ultra-low levels of charge carriers in semiconductor materials and to characterize the layer structure of devices. Secondary ion mass spectrometry is particularly noted for its outstanding sensitivity of chemical and isotopic detection. Quantitative or semi-quantitative analysis can be performed for small concentrations of most elements in the periodic table, including the lightest. However, the high versatility of SIMS is mainly due to the combination of high sensitivity with good topographic resolution, both in depth and (for imaging SIMS) laterally. Its generally superior trace element sensitivity, capability for spatial resolution in three dimensions and for isotope measurements, as well as potential for identification of chemical compounds in many cases, make SIMS the preferred method for the solution of an analytical problem. Deficiencies, however, still exist in the capability of SIMS for quantitative elemental analysis compared to other surface techniques (Auger, X-ray photoelectron spectroscopy, electron microprobe techniques, etc.). These deficiencies can be traced to the extreme dependence of relative and absolute secondary ion yields on several parameters. Among these the following are the most important: † † † † † † †

matrix effects; surface coverage of reactive elements; angle of incidence of primary beam with respect to the sample surface; angle of emission of detected ions; mass-dependent transmission of the mass spectrometer; energy band-pass of the mass spectrometer; dependence of detector efficiency on element.

Quantitative elemental SIMS analysis poses a twofold problem. Firstly, spectral interpretation, namely, the extraction of total detected isotopic ion currents assignable to elemental and molecular ions from a complete SIMS spectrum of the sample; secondly, spectral quantification, namely the calculation of elemental concentration from total isotopic elemental (and molecular) ion currents. Difficulties in spectral interpretation are considerably reduced if high resolution mass analyzers ðM=DM . 3000Þ are used for mass analysis of secondary ions because most of the commonly occurring isotopic and molecular interferences (e.g., hydrocarbons, oxides and hydrides) can be resolved.

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5.9.2

Practical principles

A diagram of a SIMS instrument with a double-focusing mass analyzer is represented in Fig. 5.2. Secondary ion mass spectrometry is based on: † † †

bombardment of the sample surface by focused primary ions, with sputtering of the outmost atomic layers; mass spectrometric separation of the ionized secondary species (sputtered atoms, molecules, clusters) according to their mass-to-charge ratios; collection of separated secondary ions as quantifiable mass spectra, as indepth or along-surface profiles, or as distribution images of the sputtered surface.

The primary ions are normally produced by a duoplasmatron type of gas þ 2 þ þ þ source such as Oþ 2 , O , N2 , Ar ; by surface ionization as for Cs and Rb ; or by þ þ liquid-metal field ion emission as Ga and In . The most common primary ions used are the oxygen ions, Csþ and Gaþ. The ions are accelerated and focused to a selected impact area on the specimen. The collision cascade following the incidence of a primary ion results in the implantation of the primary particle, reshuffling of some 50–500 matrix atoms, and emission of secondary particles, neutral or ionized. Secondary ions from the specimen are extracted into the mass spectrometer, which can consist of electric (ESA)/magnetic deflection fields or be of the quadrupole or time-of-flight design (see Section 5.6.3.1). Secondary ions with a given mass-to-charge ratio and within a certain interval

Fig. 5.2. Diagram of double focusing SIMS (adapted from Ref. [111]).

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of kinetic energy are collected for pulse or current measurement, ion-optic imaging and data processing. The different ways of operating a SIMS instrument are presented in Fig. 5.3. In the microscope mode, a defocused primary ion beam (5 –300 mm) is used for investigating a large surface. In the microprobe mode, a focused primary ion beam (,10 mm) is used for investigating a very small portion of the surface and detecting inclusions in bulk material. The lateral resolution is defined by the primary ion beam size. 5.9.3

Sensitivity and quantification

Figure 5.4 shows schematically the types of analytical information that can be obtained by SIMS analysis. A SIMS spectrum normally shows mass peaks that are characteristic of the sputtered solid but affected by experimental factors. For instance, among these factors the following should be mentioned: type, intensity, energy and incidence angle of the primary ions; the transmission of the secondary ions and the selectivity for them in the mass analyzer; the type of detector. There are, effectively, two spectra: that of the matrix and that of the impurities. The task of analytical SIMS is to quantify the secondary ion currents, that is to convert the intensity of one or several peaks characteristic of an element to

Fig. 5.3. Operating modes of SIMS (adapted from Ref. [112]).

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Fig. 5.4. Analytical information obtainable from SIMS analysis (adapted from Ref. [113]).

its corresponding concentration ce . Assuming that a primary ion beam with a current density, ip ; strikes the sample; collision cascades are initiated, resulting in, among other things, the emission of secondary ions, which are partially detected with an instrument transmission, h, as a mass spectrum of ions from an analyzed area, A. The detected positive or negative current of an ionic species M at the mass number m will be: IM ¼ Ip SPM hM gM bM ce

ð5:1Þ

where Ip is equal to ip A and P is the probability that the particle (atomic or molecular) will emerge as the last step of the sputtering and recombination cascade. S is the sputtering yield (secondary particles per primary ions), gM is the positive or negative ionizability of M (ions per atom or molecule), and bM is the isotopic abundance of M in the element. 5.9.3.1 Absolute sensitivity In a situation where the prime goal is to detect trace elements of as low a concentration as possible, without consideration of sample consumption and analytical volume (e.g., in bulk analysis), the suitable figure of merit is the detected secondary ion current of an element E per unit of atomic concentration cðAÞ; that is the absolute sensitivity Sa ðEÞ : Sa ðEÞ ¼ N q ðEÞ=cðE

5:2Þ

where N q is the detected current (in counts per second) of element E in charge state q.

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5.9.3.2 Practical sensitivity The practical sensitivity, Sp ðEÞ; takes into account the fact that in different analytical situations different primary beam currents may be appropriate: Sp ðEÞ ¼ N q ðEÞ=Ip cðE

5:3Þ

This definition of practical sensitivity does not provide a figure of merit independent of material consumption. The same value would be obtained on different samples for the element E if, at the same primary beam current, the secondary ion currents of element E are identical, even if X sputters much faster than Y. 5.9.3.3 Useful yield If the amount of sample is limited or the sampling volume has to be small, the appropriate figure of merit is the useful yield, tu : It is defined as the number of detected secondary ions/s, N q ðEÞ; of element E per number, NðEÞ; of sputtered E (atoms/s) from the same sampling volume: tu ðAÞ ¼ N q ðEÞ=NðEÞ ¼ Sp ðEÞ=Ytot

ð5:4Þ

Using the previously introduced figures of merit, the fundamental SIMS formula can be alternatively written as: N q ðEÞ ¼ Sa ðEÞcðEÞ ½cps ¼ Sp ðEÞNp cðEÞ ½cps ¼ tu ðEÞNp Ytot cðEÞ ½cps

ð5:5Þ

where Sa is measured in counts per second (cps) and dimensionless units have to be chosen for Sp and tu : When Sa ; Sp or tu are known, Eq. (5.5) provides a simple means for calculation of elemental concentration, cðEÞ; from the measured secondary particle current, N q : 5.9.3.4 Sensitivity factors Quantitation in SIMS can be achieved by external standards or by utilizing the concept of sensitivity factors. Under scrupulously reproducible conditions of analysis, and using external standards with composition and microstructures not too different from the analyzed samples, useful calibration factors may be obtained. However, long-term instabilities in analysis (instrumental drift, changes in primary beam conditions, vacuum effects, crystalline effects) make the use of absolute sensitivity factors hazardous. It is generally found to be both very feasible and more reliable to utilize the simultaneously measured ion current, IR ; of a matrix reference element, R. It has been found that relative sensitivity factors (RSFs) remain practically constant within quite wide ranges of concentrations, i.e., the differences are only weakly dependent on concentration. Excellent quantitation with RSFs has been reported, for example, for steels, binary alloys, glasses and semiconductors. The dominant sources of variation and irreproducibility in absolute and RSFs are connected with the ionizability of the elements.

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5.10

GLOW DISCHARGE MASS SPECTROMETRY

5.10.1 Introduction Glow discharge mass spectrometry (GD-MS) consists of the coupling of a glow discharge atomization/ionization source with a mass spectrometer. As noted earlier, the relative simplicity of mass spectra compared with optical spectra makes mass spectrometry an attractive alternative to optical spectrometry for trace element analysis. Moreover, mass spectrometry permits the coverage of essentially the entire periodic table and, since the spectral background can be very low, detection limits are usually 2–3 orders of magnitude better by mass spectrometry than for optical atomic emission using a glow discharge. For over 50 years glow discharges have been known as ion sources for mass spectrometry. The capability of generating a stable analyte ion population directly from a solid sample, thereby precluding the problems of dissolution, dilution and contamination that may arise for techniques requiring solution samples, makes the glow discharge an attractive ion source for elemental mass spectrometry of solids. The ability to obtain isotopic information across the periodic table down to ng g21 detection limits, along with the developments of improved mass spectrometers with more reliable data acquisition and control systems, has made GD-MS a powerful tool, not only for research laboratories but also for routine applications. A wide variety of analytical glow discharge geometries have been investigated as ion sources. Most GD sources, particularly the commercial versions, have used a direct insertion probe that permits certain flexibility in sample shape, although pins or discs are normally used. In this configuration, the sample serves as the cathode of the glow discharge system and the cell housing as the anode. Ions are sampled from the negative glow region through an exit orifice. In Table 5.5, a comparison of the different sources is given. Hollow cathode glow discharges were coupled with a magnetic sector analyzer in preliminary investigations of analytical GD-MS [114,115]. Commercial instruments employ a modified coaxial cathode geometry [116,117]. This is also the most widely characterized glow discharge ion source. Whereas different glow discharge ion sources have not exhibited any significant performance differences, different methods of powering the sources show specific performance differences. DC-powered sources are the most common, even though RF-powered sources have been studied [118] and applied as well as the pulsed sources [119]. The most widely used commercial GD-MS instrument is the VG9000 that consists of a DC-powered source, a double-focusing mass analyzer of the reverse Nier-Johnson geometry and Daly and Faraday cup detectors. Its cost is around US $600,000.

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172 TABLE 5.5 Comparison of glow discharge sources Voltage (V)

Current (mA)

Pressure (Torr)

Cathode

Advantages

Disadvantages

Hollow cathode

250 –500

10– 100

0.1 –1.0

High sputter Intense ion beam Useable for powders

Charge exchange Complicated sample geometry

Grimm

500 –1000

25– 100

1 –5

23 mm deep cylinder with 5 mm diameter base 6.5 mm diameter circle

Only flat samples

Jet-enhanced

900

28

2.5

12 mm diameter circle

Coaxial cathode

800 –1500

1 –5

0.2 –2.0

1.5 –2.0 mm diameter £ 4– 8 mm long rod

Depth profiling Easy for compacted powders High sputter rate Easy for compacted powders Useable for various sample shapes Ionization dominated by Penning process

Only flat samples Higher discharge gas flow rate Powders need to be converted into solid samples

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Source


5.10.2 Glow discharge processes A glow discharge is a partially ionized gas consisting of approximately equal concentrations of positive and negative charges plus a large number of neutral species. It consists of a cathode and anode immersed in a low-pressure (< 0.1– 10 Torr) gas medium. Application of an electric field across the electrodes causes breakdown of the gas (normally one of the rare gases is used, typically argon) and the acceleration of electrons and positive ions towards the oppositely charged electrodes. Detailed description of the phenomena can be found in Refs. [120 –122]. As an ion source for elemental mass spectrometry, the glow discharge is characterized by two attractive attributes, cathodic sputtering and Penning ionization, that are inherent to its operation. Cathodic sputtering generates a representative atomic population directly from the solid sample. Penning ionization selectively ionizes these sputtered atoms, permitting detection on the basis of their characteristic mass-to-charge ratios by mass spectrometry. 5.10.2.1 Atomization Cathodic sputtering is the phenomenon that makes a glow discharge useful in analytical spectrometry, providing the means of obtaining directly from a solid sample an atomic population for subsequent excitation and ionization. The sputtering involves directing an energetic particle onto a surface where, after collision, it transfers its kinetic energy in a series of lattice collisions. Atoms near the surface can receive sufficient energy to overcome the lattice binding and be ejected, generally as neutral atoms with energies in the range of 5–15 eV. The bombarding particles are normally ions, easily accelerated by electrical fields. The sputter yield, defined as the number of ejected atoms per bombarding ion, depends critically on the mass and energy of the incoming ions. Under the operating conditions of most analytical glow discharges, the sputter yield can be described as a function of kinetic energy and mass of the bombarding atom as well as of the lattice binding energy and mass of the target atoms. A related value is the sputtering rate, namely the number of target ions sputtered per unit time. This value is determined by the discharge operating current as well as the factors affecting the sputter yield. 5.10.2.2 Ionization The glow discharge sputtering can introduce into the plasma a representative population of the sample (cathode) ions. A fraction of them needs to be ionized for further elemental analysis by mass spectrometry. The discharge must then act as ionizing medium and must, of course, sustain itself. The fact that GD-MS does not utilize optical transitions of the analyte atoms, rather the mass-to-charge ratio of the atoms that have been ionized, shifts the emphasis from excitation mechanisms to ionization mechanisms, specifically

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simplifying, to some extent, the relationship between analyte signal and analyte concentration in the sample. Figure 5.5 shows schematically the processes in a GD [123] and in Table 5.6 ionization processes in glow discharges are summarized. Whereas we can assume that atomization does not differ significantly between elements in a given matrix, we cannot assume the same to be true for ionization. Therefore, RSFs used for quantitative analysis are most likely controlled by differences in the probability of ionization among the elements. The RSF of an analyte element, E, is the ratio of its sensitivity to the sensitivity of some reference element. Sensitivity is defined as the intensity (I) of the signal per unit of concentration (C): RSFE=R ¼ ðIE =CE Þ=ðIR =CR

5:6Þ

The RSFs consider the contributions arising from instrumental factors, such as ion transmission and sensitivity, and glow discharge processes, such as differential atomization and differential ionization. The dominant contribution is related to the glow discharge processes and varies from sample to sample. 5.10.2.3 Quantification The mass spectrum obtained by GD-MS can be used directly for a semiquantitative measurement of the sample composition. One method is based on the ion-beam ratio (IBR) [124]. In this procedure, the ion signals for all

Fig. 5.5. Processes in a glow discharge (Reprinted from Spectroscopy Europe, 15(3) (2003) 15).

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Detection methods for the quantitation of trace elements TABLE 5.6 Ionization processes in the glow discharge Electron impact ionizationa Penning ionizationb Associative ionizationb Symmetric charge exchangeb Asymmetric charge exchangeb

A þ e2 ! Aþ þ 2e2 Arm þ X ! Ar þ Xþ þ e2 Arm þ X ! ArXþ þ e2 Aþ þ A ! AþAþ Aþ þ B ! AþBþ

a

Collisions of the first kind. Collisions of the second kind.

b

ionized sputtered species are summed and then the ratio of the ion signal for individual species is calculated, which corresponds to the concentration of the species in the bulk. Since the IBR depends on the sensitivity of the different elements, which varies by less than a factor of 10, this method can only provide reliable means for semiquantitative analysis. The signal intensity of the plasma species is influenced by several factors. Among these are: sample composition, matrix type, discharge power, cathode geometry, cooling effects, discharge gas, source pressure, ion transmission, the type of mass spectrometer and the detection system. Because of all this, for quantitative analysis, the use of standards is required for calibration. This can be performed in two ways. The first consists of the construction of a calibration curve, based on a set of similar standards [125,126]. The second possibility is based on the analysis of a reference material as similar as possible in composition and behavior to the unknown sample, which allows the calculation of the RSFs [127]. Since suitable certified standards are not always available, powdered samples may be doped with an element of known concentration to be used as an internal standard.

5.10.3 Applications to trace element analysis GD-MS has taken the place of spark source mass spectrometry (SS-MS) for the analysis of trace elements in solid samples. In comparison to SS-MS, GD-MS presents many advantages, for instance, a simple source producing a stable supply of low energy ions characteristic of the sample and minimal matrix effects. In a DC powered GD-MS instrument, the samples must be conductive; therefore, bulk metals are the most ideal samples even though non-conductive samples can also be analyzed. In this case, the samples need to be mixed with a binder material [127] (Ag, Ti or Ta) or the technique of the secondary cathode can be applied [128]. Sample spectra may be obtained in a short time (min) and rapid qualitative analysis can be performed by the examination of the

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isotopic lines. Quantitative analysis can also be achieved, as previously explained. Semiconductors are another important category of bulk samples that can be analyzed by GD-MS. The electrical properties of these materials are critically dependent on the intrinsic and doped levels of impurities, making it essential to know, not only qualitatively their chemical composition, but also the concentration level of each element. Even extremely low concentrations of a specific element can alter semiconductor properties. GD-MS is always a surface analysis technique, even though it permits measurement of bulk concentrations. That is, the sputtering process central to the glow discharge acts as an atomic mill that regularly erodes away the surface of the bombarded sample. Whatever atoms are sputtered away from the surface are measured and, because GD-MS consumes significant quantities of material (up to milligrams per minute), these sequential layer analyzes combine to yield an averaged composition that is typical of the bulk concentration. By slowing down the ablation process limiting measurement to a shorter duration, data indicative of the surface concentrations can be obtained. GD-MS and its optical analogue have found considerable application for the analysis of layered samples. Environmental samples can also be analyzed by GD-MS. In these cases the samples need to be compacted with or without conducting material [129]. Where a binder of conducting material is not added during the compaction of the samples, a secondary cathode has been used for their analysis [126]. 5.11

X-RAY FLUORESCENCE SPECTROMETRY

5.11.1 Introduction X-ray spectrometric techniques have been very useful in providing important information for theoretical physicists and have found increasing exploitation in the field of material science characterization. Today, most stand-alone X-ray spectrometers use X-ray excitation sources rather than electron excitation. X-ray fluorescence spectrometers typically use a polychromatic beam of short wavelength X-radiation to excite longer wavelengths, characteristic lines, from the sample to be analyzed. In modern X-ray spectrometers, single crystals are used to isolate a narrow energy band from the polychromatic radiation excited from the sample. This method is called “wavelength” dispersive spectrometry. The other possibility is to use a proportional detector to isolate a narrow energy band from those obtained from the excited sample. This method is called “energy” dispersive spectrometry. Since the relationship between emission wavelength and atomic number is known, isolation of individual characteristic lines permits the unique identification of an element and elemental concentrations can be estimated from characteristic line intensities [130].

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X-ray fluorescence spectrometry provides the means for the identification of an element by measurement of its characteristic emission wavelength or energy. The quantitative estimation of an element is possible by first measuring the emitted characteristic line intensity and then relating this intensity to the elemental concentration. A benefit of using X-ray emission spectra for qualitative analysis is that, because the transitions arise from inner orbitals, the effect of chemical combination or valence state is almost negligible. 5.11.2 Instrumentation Several different types of sources have been employed for the excitation of characteristic X-radiation, including those based on electrons, X-rays, g-rays, proton and synchrotron radiation. By far, the most common source today is an X-ray photon source. This source is used in primary mode in the wavelength and primary energy dispersive system, and in secondary fluorescer mode in secondary target energy dispersive spectrometers. An X-ray detector is a transducer for converting X-ray photon energy into voltage pulses. Detectors work by a process of photoionization, in which interaction between the incoming X-ray photon and the active detector material produces a number of electrons. The current produced by these electrons is converted to a voltage pulse by capacitors and resistors, such that one digital voltage pulse is produced for each entering X-ray photon. The most important characteristics of the detector are proportionality and linearity [131]. In the case of wavelength dispersive spectrometers, a gas flow proportional counter is generally employed for the measurement of longer wavelengths, and a scintillation counter is generally used for shorter wavelengths. Neither of these two detectors has sufficient resolution to separate multiple wavelengths on its own, and has to be employed with an analyzing crystal. However, in the case of energy dispersive spectrometry, where no dispersing crystal is used, a detector of higher resolution must be used, generally the Si(Li) detector [132]. 5.11.3 Matrix effects In the conversion of net line intensity to analyte concentration, it may be necessary to correct for absorption and/or enhancement effects. As for absorption, primary and secondary absorption needs to be considered. Primary absorption occurs because all atoms of the sample matrix absorb photons from the primary source. Since there is a competition for these primary photons by the atoms making up the sample, the wavelength distribution of intensity of the photons available for excitation of a given analyte element may be modified by other matrix elements. Secondary absorption refers to the absorption of characteristic analyte radiation by the specimen matrix. As characteristic radiation is released from the sample in which it is generated, it is absorbed by all matrix elements in amounts relative to their mass attenuation coefficient.

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Enhancement effects of the total absorption occur when a non-analyte matrix element emits a characteristic line of energy just in excess of the absorption edge of the analyte element. This means that the non-analyte element is able to excite the analyte, giving characteristic photons in addition to those produced by the primary continuum. This results in an enhanced signal from the analyte [133]. 5.11.4 Quantitative and trace analysis The simplest challenge for quantitative analysis is the determination of a single element in a known matrix. In this case, calibration curves can be obtained. When the matrix is unknown, quantitative analysis is based on the use of internal standards, addition of standards and use of a scattered line from the Xray source. The most complicated case is the analysis of all, or most, of the elements in a sample whose matrix is unknown. In this case, a full qualitative analysis would be required before any attempt is made to quantitate the matrix elements. Once the qualitative composition of the sample is known, one of the following three techniques can be applied: type standardization, influence coefficient methods, fundamental parameter techniques. The last two methods require a computer for their application [134,135]. One of the problems with any X-ray spectrometer system is that the absolute sensitivity (i.e., the measured cps per% of analyte element) decreases significantly as the lower atomic number region is approached. This is, above all, due to the fact that the fluorescence yield decreases with the atomic number, the absolute number of useful long-wavelength X-ray photons from the radiation source decreases with increasing wavelength and absorption effects become more severe with increasing wavelength of the analyte. The X-ray fluorescence method is particularly applicable to the qualitative and quantitative analysis of low concentrations of elements in a wide range of samples as well as to the analysis of elements at higher concentration in a small amount of sample. Moreover, X-ray fluorescence is often used as a nondestructive qualitative method for a multi-element content evaluation prior to quantitative analysis with another method. X-ray spectrometric methods based on total reflection geometry [136] have gained widespread strength in the past decade, principally because of their detection power (92–10 pg, 100 pg ml21 relative), quasi matrix independent calibration (internal standard), multi-element capability and non-destructive nature. For trace element analysis the sample is prepared on a totally reflecting sample carrier as a small quantity of residue from solutions or fine-grained suspensions from the evaporation of a solvent and forms a layer of a few microns thick. It is thus a micro technique, achieving its performance advantage as a consequence of the attenuation of background due to the high angle reflection geometry employed.

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5.12

UV/VISIBLE SPECTROPHOTOMETRIC AND CHEMILUMINESCENCE TECHNIQUES

5.12.1 UV/Visible spectrophotometric techniques Many metals form compounds or complexes that give rise to an ultraviolet or visible absorbance spectrum. It is therefore sometimes possible to react the metal ion within a liquid sample (or a solid sample that has previously undergone a digestion or dissolution stage) with a reagent that will selectively change the optical properties of the sample. Many of the basic components required for such an instrumental based technique are similar to those in an atomic absorption instrument. However, the light source is different. Rather than using a HCL that produces a spectrum containing a series of discrete lines, a continuum source produced by a tungsten –halogen lamp, for example, is used that has a broad band of light output. For UV applications (,340 nm), a deuterium lamp is usually used as a source. The sample cell is frequently a cuvette, manufactured from glass or plastic (for wavelengths above 340 nm) or quartz for the UV region. The sample may simply be poured into the cuvette for absorbance readings to be made. Instead of a cuvette, gas cells are also available. The monochromator is usually fairly basic, but operates in the same way as the atomic absorption spectrometers. Again, detection is usually via a photomultiplier tube. The theory behind molecular absorption is analogous to atomic absorption, i.e., analyte molecules absorb light and electronic transitions occur within the molecule. A greater emphasis on the theory may be found in the literature [137]. It should be noted, however, that molecular absorbance occurs over a wide band of wavelengths rather than a very narrow line, as in atomic absorption. This means that, in many cases, even if detection is not at exactly the most intense wavelength, some results may be obtained, although the system will not give optimal sensitivity. As with all absorbance techniques, the Beer–Lambert law (Eq. (5.7)) applies and, since this law is true over only a relatively small concentration range, the calibration will be linear over a range of perhaps two orders of magnitude. A ¼ 1CL

ð5:7Þ

where A is absorbance, 1 is the molar extinction coefficient (also called the molar absorptivity constant), C is the concentration and L is the path length. It can be seen from Eq. (5.7) that, in addition to concentration, the absorbance is dependent on the path length and the molar extinction coefficient. The path length is usually 1 cm for most liquid cuvettes, but may be substantially larger for gas cells. The molar extinction coefficient is a number whose numerical value is different for each metal –complex system, but is regarded as being a constant for each. The higher the value of 1, the greater

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the absorption of light by the compound formed, and hence the better the sensitivity. One of the major problems with UV/Visible spectrophotometric methods is that of interferences. Often, more than one metal ion will combine with a complexing agent and the absorption spectra from the two species may overlap. This is shown diagrammatically in Fig. 5.6. It can be seen that even a small contribution from one interfering species could potentially lead to inaccuracy and since the interferences are additive, the presence of several concomitants could lead to large errors. Assuming that all of the potential interferences within a sample are known, it is possible to use simultaneous equations or mathematical algorithms to correct for the relative contributions from each. An example is the simultaneous determination of chromium and manganese in steel samples [1]. It should also be noted that pH may affect the absorption profile of the analyte complex and it is thus often necessary to buffer the samples and standards to the same pH. If the analyte forms a very stable compound with another concomitant, then it may not be available to form the complex and a low analytical result would occur. It may occasionally be necessary to treat the sample in some way to selectively bind the interfering species or to destroy it. The linear range for these methods usually covers no more than two orders of magnitude. The LOD will depend on the molar extinction coefficient, but for some of the more sensitive methods may be as low as 10–50 ng ml21. Sample throughput can be very rapid, with up to 5–10 samples per minute being determined. Although gaseous samples may be analyzed, the norm is for the sample to be in a liquid form. Since the method of detection is through light absorption, it is necessary for the samples to be free from particulate matter. This may be achieved either by centrifugation or by filtering. Since the light beam in most instruments will pass through the bottom third of the cuvette, a volume of 1 ml is usually sufficient for the measurement step. It is necessary to

Fig. 5.6. Overlap of absorption spectra.

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remember that the sample must often be combined with a known volume of complexing reagent and buffer prior to the measurement step, and therefore the actual amount of sample used may frequently be less than 1 ml. The UV/Visible instrumentation described thus far has concerned standalone spectrometers that may also be used to scan over a wavelength range so that absorption maxima may be identified. If a flow-through UV/Visible detector is used, then transient signals may be detected. Such instrumentation may be used as a detector for chromatography, but is predominantly used for automated high throughput analyzes, e.g., with FI techniques. If the sample, compleximetric reagent and buffer are mixed on-line and allowed to pass through the detector, a transient signal will be obtained which may be recorded using either a chart recorder (if peak height is to be measured) or an integrator. The same problems arise with interferences, but the major advantage of this approach is that the instrumentation is more portable and may be taken into the field (assuming an adequate power supply is available) or aboard a ship. Therefore, savings in time are made and the disadvantages associated with the transport of samples back to a laboratory (with possible loss of analyte through adsorption or breakage of storage vessels, contamination from preserving agents, etc.) may be overcome. An example of such a method has been published by Hernandez et al. [138]. If a micro-column of resin is used to entrap the analyte prior to mixing with the compleximetric agent, then matrix removal and pre-concentration may be achieved. Sample consumption will be dependent upon the size of the sample loop, but is typically 100 –200 ml per injection, but this will increase markedly if a pre-concentration technique is used. If several analytes require determination and they all form a complex with a particular reagent, it may be possible to determine them using liquid chromatography coupled with UV/Visible detection. A recently published paper has outlined the determination of several analytes (Th, V, Bi, U, Hf and Zr) using a 10 cm column of neutral polystyrene loaded with dipicolinic acid and 1 M potassium nitrate as an eluent [139]. This application yielded LODs of substantially less than 1 mg l21 for several analytes. A similar study reported the determination of a selection of transition and heavy metal ions using a polystyrene –divinyl benzene column loaded with 4-chlorodipicolinic acid and an eluent of 0.25 mM chlorodipicolinic acid in 1 M potassium nitrate at pH 2.2 [140]. 5.12.2 Molecular fluorescence and chemiluminescence detection Molecular fluorescence detection is less commonly used because it is necessary to combine the analyte with a suitable reagent capable of fluorescing, whilst ensuring that other metal species present in the sample do not. Despite these disadvantages, the technique can give LODs in the ng ml21 range, is more selective than absorption and, when a chromatographic technique is used to separate potential interferences from the analyte, excellent selectivity may be achieved and sample consumption need only be 50 –200 ml. A recent example of

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such an application compared ICP-MS and fluorescence detection for Al species that had been separated using chromatography [141]. Chemiluminescence is a form of emission, but is produced from the energy originating from a chemical reaction rather than the absorption of light. Since it is related to fluorescence, it possesses the same analytical advantages, including long linear ranges and very good sensitivity. Most methodologies use either FI or chromatography as a sample introduction method. As usual, FI methods tend to be very inexpensive and easily portable, although for this type of work they are also usually designed to determine only single analytes. Again, the sample must be in a liquid form, so soils and other solid materials must first be digested. Typically, the analyte is isolated on a mini- or micro-column of a resin. This will often mean that the sample must be buffered to a specific pH and reagents added to ensure that other concomitant species are not retained since, on elution, these may react with the chemiluminescent reagents and potentially interfere. An example of a FI method has been published by Achterberg et al. [142], who determined Cu in seawater. This application also emphasized a method for overcoming another common problem experienced with this type of methodology. The presence of humic acids or other chelating compounds will usually mean that the analyte will complex with these in preference to the resin in the micro-column, resulting in poor analyte retention and its passage directly through the system undetected. The use of UV photolysis (possibly in the presence of hydrogen peroxide) prior to analysis can help destroy the organic material and will therefore render the Cu (or other analytes) available for detection. In this particular application, the UV digestion was performed on-line and, although less efficient than batch irradiation, was sufficient to enable successful determination whilst being substantially more rapid. The use of a UV digestion will inevitably increase the length of time required for the analysis, but even with a digestion time of 5 or 6 min, 5–10 samples per hour may be analyzed. As with all FI techniques, preconcentration is a possibility, and the pre-concentration process may limit the sample throughput further. An example of simple FI–chemiluminescence detection without the need for UV digestion has been reported by Bowie et al. [143]. These workers determined Fe in seawater by reducing Fe(III) to Fe(II) using sulphite, and then retaining/pre-concentrating the whole Fe content of the sample on a column of 8-hydroxyquinoline. On elution, the Fe was reacted with luminol for the chemiluminescence detection. Limits of detection were found to be sub-nM, precision was 3.2% RSD and the whole analytical cycle took 3 min, enabling a sample throughput of close to 20 samples per hour. The portability of the technique was demonstrated by the shipboard determination of Fe in the Atlantic. Chemiluminescence may also be used as a means of detection for liquid chromatography. The relative advantages of this are the same as those discussed for UV/Visible detection, with the added bonus of the extra sensitivity. Several papers have reported the determination of trace metals

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using chemiluminescence as a method of detection. Included amongst these is the determination of silver [144]. After elution from a cation exchange column, the silver was detected by a novel post-column reaction system involving the oxidation of luminol by peroxodisulfate. The system yielded a LOD of 0.5 mg l21. A similar paper by the same authors managed to speciate Cr(III) and Cr(VI) in fresh waters [145]. This system enabled LODs of 0.05 and 0.1 mg l21 to be obtained for Cr(III) and Cr(VI), respectively, whilst yielding a linear range of 0.1 –500 mg l21. Precision at the 10 mg l21 level was 5% RSD. Multi-element determinations are also possible using such systems. An example that yielded mg l21 LODs for several analytes in 15 min has been reported [146]. 5.13

ELECTROCHEMICAL METHODS

5.13.1 Differential pulse anodic stripping voltammetry A liquid sample is placed in a sample cell along with a suitable buffer, and then a hanging mercury drop electrode, a reference electrode (usually a saturated calomel electrode) and a platinum wire counter electrode are immersed in the sample. The sample is purged with an inert gas to de-oxygenate it because oxygen causes an interference effect. A negative voltage may then be applied to the mercury drop. The sample is stirred magnetically, and some of the positively charged metal ions in the sample will diffuse to the mercury drop and plate onto and diffuse into it. The period during which this occurs is termed the plating time. After this, a brief relaxation period occurs, followed by a period in which the potential applied to the mercury drop becomes increasingly less negative. Each analyte ion will be stripped or oxidized from the mercury drop at its own reduction potential and will re-enter the solution. As they re-enter the liquid phase, they are detected by the counter and standard electrodes and will appear as a series of peaks. The area under each of the peaks is proportional to the concentration of that particular ion in the sample. It is the length of the plating time that determines the overall sensitivity of the analysis. Short plating times will be insufficient for many of the analyte ions within the sample to become significantly accumulated in the mercury drop, whilst longer times will enable greater sensitivity to be obtained, but at the expense of decreased sample throughput. The overall sensitivity will be limited by the contamination within the buffer system and by time constraints. Detection limits substantially below 1 ng ml21 may readily be obtained for Cd, Cu, Pb and Zn using a plating time of just a few minutes, but plating times of over an hour are known, which yield exceptionally low LODs. The technique is multi-elemental, with approximately 20 metallic ions being detected. Despite this, the four aforementioned ions are most commonly determined by this method. Another advantage of the technique is that it is capable of determining different species of an element. An example has been the determination of assorted tin species,

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although an ion exchange procedure was also required for full speciation because many of the tin species produced only one peak [147]. Since only a very small portion of the analyte ions in the sample plate onto the mercury drop, the method is very vulnerable to errors caused by differences in plating times, stirring rates, temperatures and current density between samples and standards. It is this last potential problem that often leads to the method of standard additions being used to calibrate the process. The plating time is obviously the limiting factor for sample throughput, but if three standard additions are made to each sample, then rarely more than 5–10 samples may be analyzed per hour. Sample consumption is typically 1–10 ml, depending on the volume of the sample cell and on the volume of buffer/diluent added. Since many instruments possess a spiking port, the standard additions may be made to the same sub-sample. Precision is typically ,5% RSD. Care must be taken with the stirring of the sample, as the hanging mercury drop is easily dislodged. If stirring is too vigorous and the drop is dislodged it may be necessary (depending on whether plating has begun) to start the analysis again with a new sub-sample. For this reason, some workers prefer glassy carbon working electrodes and co-plating mercury from mercury (II) chloride added to the sample solution to produce the thin film mercury electrode. Another drawback with the technique is that the presence of organic matter, that may complex with many of the ions, will lead to a decrease in the plating efficiency (i.e., the analytes are kept in solution as a complex and are not available for analysis). This will potentially lead to an underestimate of the true analyte concentration. It is possible to differentiate between the free ions and those complexed with organic matter if an analysis is first made on an untreated sub-sample to yield the concentration of the free ions. A second analysis made on another subsample that has been treated with UV radiation, destroying the organic matter will yield the “total” concentration of the analytes. The presence of high concentrations of some metal ions may also lead to overlap of the peaks or to the formation of intermetallic compounds.1 5.13.2 Cathodic and adsorptive stripping voltammetry This is analogous to ASV, and may usually be achieved using the same instrumentation. Cathodic stripping voltammetry (CSV) is used less for metal ion determinations than ASV simply because there are fewer negatively charged metal ions, but metals may be adsorbed as their complexes (e.g., Ni with dimethylglyoxime). The obvious exceptions are the metalloids, e.g., arsenic, selenium, etc., and some of the transition elements (i.e., those that 1 Methods for the determination of the metals aluminum, cadmium, chromium, cobalt, iron, lead, nickel, uranium, vanadium and zinc in marine, estuarine and other waters by stripping voltammetry or concentration and atomic absorption spectrophotometry, HMSO, London, 1987.

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form negatively charged complexes). This technique shares many of the relative advantages and disadvantages of ASV. Again, speciation is possible with As(III) and As(V) species having been determined [148]. 5.13.3 Ion selective electrodes Ion selective electrodes (ISE) are so termed because they are selective for a specific ion. Some texts describe them as ion specific electrodes, but this is misleading because they are not specific. Most suffer from interferences in that they respond to the presence of other species in solution, although to a lesser extent than to the ion they are designed to detect. Examples of these interferences include Hþ, Csþ, Liþ and Kþ on the Na ISE and Zn2þ, Pb2þand Mg2þ on the Ca ISE. Additionally, the presence of organic matter, such as humic acids that may complex with the analytes of interest, may prevent them from being detected and hence, an underestimate of the true concentration will be made, although it should be noted that the electrode is truly responding to activity. Several of these electrodes exist, but they do not exist for every metallic element. Their response is based on the Nernst equation (Eq. (5.8)). E ¼ Eu þ

2:303 RT log ½ion zF

5:8Þ

where R is the gas constant, T is temperature, z is the charge of the species of interest, F is the Faraday constant and [ion] is the concentration of the analyte. As can be seen from Eq. (5.8), the response is dependent upon temperature and on the charge of the species under investigation, although at 258C and for a singly charged analyte, a change of 59 mV per decade of concentration is obtained. For a doubly charged analyte, e.g., Ca2þ at the same temperature, a change of 29.5 mV per decade should be generated if the electrode is Nernstian in response. Calibration curves can be plotted on semi-log paper, and can cover five or six orders of magnitude. Detection limits will depend on the individual ISE, but are often at the ng ml21 range; with copper, for example, the ISE is linear over the range 1028 –0.1 M (i.e., 6.4 £ 1024 –6350 mg l21). The electrodes are readily portable and may easily be used in conjunction with a data logger for unattended operation in the field. Assuming the temperature of the water body is monitored simultaneously, a simple algorithm can be used to correct the data. It should be noted however, that any changes in ionic strength of the sample may lead to interferences (depending on the ISE). Therefore, for some ISEs, most samples (and standards) are mixed with total ionic strength adjustment buffer (TISAB), a pH buffer that also contains an inert salt to fix the ionic strength. Normally, liquid samples are required and so solid samples have to be extracted, digested or dissolved such that the analytes are present in a solution. The volume of sample can be low (5 ml or less), but one of the advantages of the

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technique is that it does not consume any of the sample. Assuming that measures are taken to prevent contamination, the sub-sample used for this analysis may be used for other analyzes. The technique is also relatively rapid; once immersed in the sample, a brief equilibration time is allowed so that a stable signal is obtained, but this still allows two determinations to be made per minute. An example in which ISEs have been used to determine metal ions has been published by Vazquez et al. [149]. In this paper, Na, K and Ca were determined in wood pulp suspensions and the results compared with those obtained by ICP-OES and XRF.

REFERENCES 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

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SECTION - 1

Chapter 6

Wet digestion methods Henryk Matusiewicz

6.1

INTRODUCTION AND BRIEF HISTORY

Sample (matrix) digestion plays a central role in almost all analytical processes, but it is not often recognized as an important step in analytical chemistry, with primary attention being directed to the determination step. This sense of priorities is reflected all too conspicuously in the equipment and investment planning of many analytical laboratories. However, a welcome trend in recent years points toward fuller recognition of the true importance of sample digestion (decomposition, dissolution) in the quest for high-quality analytical results and valid conclusions. Wet digestion with oxidizing acids is the most common sample preparation procedure. Many of the sample preparation methods currently in use were actually developed during the 19th century. In the early 1800s, Berzelius developed test tubes, separatory funnels, and platinum crucibles; in 1831, he first made use of the conversion of SiO2 to SiF4 by reaction with HF for analytical purposes. In 1834, Henry and Zeise developed methods for the gravimetric determination of sulfur as sulfate in organic samples. Their method called for the sample to be digested with fuming nitric acid or aqua regia and fused with potassium hydroxide or potassium nitrate. The first published wet digestion reagent was chloric acid from HCl þ KClO3, as described in 1838 by Duflos [1] as well as by Fresenius and Babo [2] in 1844. The classical wet digestion reagent HNO3 þ H2SO4 (the most important and most versatile of the so-called wet-oxidation mixtures) was investigated by Danger and Flandin in 1841 [3], for the destruction of organic matter. The use of pure concentrated HNO3 in a closed system under elevated temperatures and pressure is well known since 1860 from Carius [4]. Kjeldahl [5] digested organic biological material in 1883 with boiling concentrated H2SO4 in an open system. Hydrogen peroxide was introduced by Classen and Bauer [6] in 1884, and HClO4 was used at elevated temperatures by Stcherbak [7] in 1893. Relatively new is Van Slyke’s [8] mixture of H2SO4 þ H3PO4 þ KIO3 þ K2Cr2Comprehensive Analytical Chemistry XLI Mester and Sturgeon (Eds.) q 2003 Elsevier B.V. All rights reserved

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O7 (1954). In 1955, Polley and Miller [9] introduced a mixture of 50% H2O2 þ conc. H2SO4 as a most powerful oxidizing reagent. Rediscovery of the high oxidizing power of OHz radicals (Fenton’s reagent H2O2/Fe2þ) [10] for biological materials in 1961 and 1968, by Sansoni et al. [11,12] led to a technique for wet digestion at temperatures below 1108C. Since the beginning of the 1970s, a large increase of general interest in different digestion techniques and in publications dedicated especially to wet digestion methods has been evident. This chapter gives an overview of wet digestion methods and recent developments and applications of the digestion of different materials. Other sample preparation methods, such as chemical extraction and leaching, solubilization with bases, enzymatic decomposition, thermal decomposition, and anodic oxidation, are beyond the scope of this contribution and will not be discussed here. 6.2

NOMENCLATURE

For some methods of analysis, it may be required that the analytical sample be in a liquid form—the sample solution. Thus, standard procedures to convert solid (or solid containing) samples to solutions prior to detection are required. However, this conventional designation is often imprecise or even misleading with respect to the actual mechanism of the process. Several very different names are sometimes applied to a single technique, which presents a considerable obstacle for anyone (particularly a non-specialist) interested in acquiring a quick overview of systems applicable to a specific task. The terms decomposition (of organic materials), dissolution (of inorganic materials), destruction, digestion, ashing, mineralization, acid-digestion, wet-ashing, and even oxidative acid digestion all refer to this process. In this chapter, the general expression will be digestion, which is specified as wet digestion; therefore, wet digestion will be the term used for obtaining the resulting acid solution. It should be mentioned that guidelines for terms used in sample digestion are provided by the International Union of Pure and Applied Chemistry (IUPAC) Analytical Chemistry Division [13,14]. 6.3

BIBLIOGRAPHY

There are numerous publications giving useful information on the digestion (dissolution and/or decomposition) of any conceivable combination of matrix and analyte. Some comprehensive books and general review articles (and references cited therein) contain material pertinent to either organic [15–18] or inorganic [19– 22] matrices; others, to both [23–29]. Within the scope of this chapter, a comprehensive discussion on digestion techniques is not feasible. For more comprehensive information, the following

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reviews and books are available: books by Sˇulcek and Povondra [20], by Bock [23] and by Krakovska´ and Kuss [29] are dedicated solely to digestion methods. Other books deal exclusively with a single technique: microwave-assisted sample preparation [30,31], this topic has also been reviewed elsewhere [32– 39]; even the literature on the use of microwave-assisted digestion procedures for subsequent sample analysis by means of electrothermal atomic absorption spectrometry (ET-AAS) is reviewed [40]. In 1997, the establishment of a site on the World Wide Web (WWW) for information transfer and education in the areas of sample preparation and microwave chemistry (http://www. sampleprep.duq.edu/sampleprep) was announced. Recommended guidelines for sample preparation (methods of digestion) of different matrices are also available from the Encyclopedia of Analytical Chemistry [41]. Although it is very difficult to refer to every paper published in this area, the enlisted bibliography of this chapter gives comprehensive coverage of advance of the topic made to date. To follow the latest development and new applications in this field, the reader may consult the annual reviews in Analytical Chemistry and Journal of Analytical Atomic Spectrometry. Relevant material is to be found under the headings “Sample preparation”, “Sample digestion”, and “Sample dissolution”, for the appropriate topics. Literature cited herein is not intended to be comprehensive, but has been selected with a view to relevance, as a pertinent review or seminal topic paper, or for its potential application, novel developments, and progress in wet digestion techniques. 6.4

REAGENTS AND VESSEL MATERIALS FOR WET DIGESTION PROCEDURES

Sample wet digestion is a method of converting the components of a matrix into simple chemical forms. This digestion is produced by supplying energy, such as heat; by using a chemical reagent, such as an acid; or by a combination of the two methods. Where a reagent is used, its nature will depend on that of the matrix. The amount of reagent used is dictated by the sample size which, in turn, depends on the sensitivity of the method of determination. However, the process of putting a material into solution is often the most critical step of the analytical process, because there are many sources of potential errors, i.e., partial digestion of the analytes present, or some type of contamination from the vessels of chemical products used. It is beyond the scope of this contribution to discuss all possible systematic errors; therefore, further details on how to avoid systematic errors during sample digestion that cannot be referred to in detail here are discussed by Knapp and Schramel in Chapter 2 of this book. The majority of wet digestion methods (total decomposition and strong attack) involves the use of some combination of oxidizing acids (HNO3, hot conc. HClO4, hot conc. H2SO4) and non-oxidizing acids (HCl, HF, H3PO4, dilute

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H2SO4, dilute HClO4) and hydrogen peroxide. All of these acids are corrosive in nature, especially when hot and concentrated, and should be handled with caution to avert injury and accidents. Concentrated acids with the requisite high degree of purity are available commercially, but they can be purified further by sub-boiling distillation [42]. Detailed discussion of the properties and applications of these reagents may be found elsewhere [20,22–25]. Wet digestion has the advantage of being effective on both inorganic and organic materials. It often destroys or removes the sample matrix, thus helping to reduce or eliminate some types of interference. The physical properties of the common mineral acids used in sample preparation are summarized in Table 6.1. Most wet digestion procedures are conducted under conditions that, in terms of temperature or reagents used, must be considered as extreme. Thus, the material of which the flasks, crucibles, etc., are made must be chosen carefully according to the particular procedure to be employed. The material from which the digestion device is fabricated is also a frequent source of elevated blanks. Elements can be either dissolved from the material or they can be desorbed from the surface. Very important in this respect is the nature of the material. The suitability of materials may be estimated according to the following criteria: heat resistance and conductance, mechanical strength, resistance to acids and alkalis, surface properties, reactivity, and contamination, whereby the specific characteristics of the organic and inorganic material must also be given special consideration. Table 6.2 shows preferred materials for digestion vessels. The apparatus and containers that are used for the wet digestion procedures must be scrupulously cleaned and tested for any possible contamination. Usually it is sufficient to boil the flasks in concentrated nitric acid followed by rinsing several times with ultrapure water before use. In cases where this procedure is not adequate, one of the most powerful cleaning procedures is steaming the vessels with nitric or hydrochloric acid with assembly in a microwave-heated sealed Teflon vessel [43]. This procedure is particularly recommended for quartz, borosilicate glass, and polytetrafluorethylene (PTFE) vessels. To generalize this section, nitric acid is an almost universal digestion reagent and the most widely used primary oxidant for the decomposition of organic matter, because it does not interfere with most determinations and it is available commercially in sufficient purity. Hydrogen peroxide and hydrochloric acid can usefully be employed in conjunction with nitric acid as a means of improving the quality of a digestion. Hydrochloric acid and sulfuric acid may interfere with the determination of stable compounds. Mixtures with hydrochloric acid are generally used for samples containing principally inorganic matrices, and combinations with hydrofluoric acid are used to decompose silicate insoluble in the other acids. Safety considerations are particularly important when using perchloric acid.

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TABLE 6.1 Physical properties of common mineral acids and oxidizing agents used for wet digestion

Nitric acid Hydrochloric acid Hydrofluoric acid Perchloric acid Sulfuric acid Phosphoric acid Hydrogen peroxide

Formula

HNO3 HCl HF HClO4 H2SO4 H3PO4 H2O2

Molecular weight

63.01 36.46 20.01 100.46 98.08 98.00 34.01

Concentration w/w (%)

Molarity

68 36 48 70 98 85 30

16 12 29 12 18 15 10

Density (kg/l)

Boiling point (8C)

Comments

1.42 1.19 1.16 1.67 1.84 1.71 1.12

122 110 112 203 338 213 106

68% HNO3, azeotrope 20.4% HCl, azeotrope 38.3% HF, azeotrope 72.4% HClO4, azeotrope 98.3% H2SO4 Decomposes to HPO3


Compound

197

198

TABLE 6.2 Preferred materials for wet digestion vessels Chemical name

Working temperature (8C)

Borosilicate glass

SiO2a

,800b

Fused quartz

SiO2c

,1200

Glassy carbon

Graphite

,500

PE PP PTFE

Polyethylene Polypropylene Polytetrafluorethylene

,60 ,130 ,250

107 150

,0.02 ,0.03

PFA FEP TFM

Perfluoralkoxy Tetrafluorperethylene Tetrafluormetoxil

,240 ,200

166 158

,0.03 ,0.01 ,0.01

a

SiO2 content between 81 and 96%. Softens at a temperature of 8008C. c SiO2 99.8%. b

Heat deflection temperature (8C)

Water absorption (%)

Comments Ordinary laboratory glass is not suitable for use in wet digestion procedures For all procedures involving wet digestion of organic material, quartz is the most suitable material for vessels Glassy carbon is used in the form of crucibles and dishes for alkaline melts and as receptacles for wet digestion procedures

PTFE is generally used only for digestion vessels in pressure digestion systems

H. Matusiewicz

Material


6.5

WET ACID DIGESTION (DECOMPOSITION AND DISSOLUTION) PROCEDURES

The task of preparing samples with acid treatment to release the elements of interest from the sample matrix and transfer them to a liquid matrix for subsequent analysis is commonplace in many laboratories. A variety of techniques are employed—from ambient-pressure wet digestion in a beaker on a hot plate (or hot block) to specialized high-pressure microwave heating. Dissolution is usually defined as the simple process of dissolving a substance in a suitable liquid at relatively low temperature, with or without a chemical reaction. The term decomposition denotes a more complex process that is usually performed at higher temperature and/or at increased pressure, with the aid of reagents and special apparatus. A clear distinction between these terms cannot, however, be made. Table 6.3 gives an overview of the wet digestion methods, one of the oldest and still most frequently used techniques, for organic and inorganic samples. The intent is not to present the procedural details for the various sample matrices, but rather to highlight those methods that are unique to each technique and sample. 6.5.1

Open systems

Open vessel acid digestions, one of the oldest techniques, are undoubtedly the most common method of sample decomposition or dissolution of organic and TABLE 6.3 Schemes for wet digestion methods Digestion technique

Required reagents

Application

Open systems Conventional heating Microwave heating Ultraviolet digestion

HNO3, HCl, HF, H2SO4, HClO4, H2O2 HNO3, HCl, HF, H2SO4, HClO4, H2O2 H2O2, K2S2O8

Inorganic/organic Inorganic/organic Waters, slurries

Closed systems Conventional heating Microwave heating

HNO3, HCl, HF, H2O2 HNO3, HCl, HF, H2O2

Inorganic/organic Inorganic/organic

Flow systems Conventional heating UV on-line decomposition Microwave heating

HNO3, H2SO4, H2O2 H2O2, K2S2O8 HNO3, H2SO4, H2O2

Inorganic/organic Waters, slurries ? Inorganic/organic

Vapor-phase acid digestion

HNO3, HCl, HF, H2O2

Inorganic/organic

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inorganic sample materials used in chemical laboratories. This very inexpensive technique is of inestimable value for routine analysis because it can easily be automated; all the relevant parameters (time, temperature, introduction of digestion reagents) lend themselves to straightforward control. Thousands of different methods and minor variations on these methods have been described in the literature. The main advantage of wet digestion (ashing) over dry ashing is its speed. However, systems of this type are limited by a low maximum digestion temperature, which cannot exceed the ambient-pressure boiling point of the corresponding acid or acid mixture. For instance, the oxidizing power of nitric acid with respect to many matrices is insufficient at such low temperatures (boiling point 1228C). One possible remedy is the addition of sulfuric acid, which significantly increases the temperature of a digestion solution. Whether or not this expedient is practical depends on the matrix and the determination method. High-fat and high-protein samples are generally not subject to complete digestion at atmospheric pressure. Other disadvantages relate to the risk of contamination through laboratory air, the necessarily rather large amounts of required reagents (very often employing expensive reagents), and the danger of losses of trace elements. Losses can be kept low by using an excess of acid (mainly nitric) combined with a reflux condenser and by optimization of temperature and duration. Nevertheless, systems operated at atmospheric pressure are preferred from the standpoint of workplace safety. 6.5.1.1 Conventional heating (thermally convective wet digestion) The conventional approach to wet digestion, which has proven its worth over many years, entails a system equipped with heated conventional source (Bunsen burner, heating plate, sand bath, etc.) operating either at a fixed temperature or in response to a temperature program. Acid digestions are often accomplished in any vessel, usually in glass or PTFE (beaker, conical flask, etc.) with or without a refluxing condenser. However, when a sample is decomposed by open wet digestion, refluxing is compulsory. The necessary apparatus has been described by Bethge [44]. Open block digestion systems have been popular in sample analysis over the past decades, but have consistently suffered from the major drawback of their sensitivity against corrosion and subsequent risk of contamination. Therefore, block digestion systems (hotplate techniques) have not been considered state-of-the-art technology in trace and ultratrace sample preparation. Graphite block digestion systems are becoming more frequently considered. These systems overcome the deficiencies of the traditional systems, made from stainless steel or aluminum, because the block is manufactured from graphite and typically coated with a fluoro-polymer to prevent the possibility of metallic contamination from the surface of the system during the handling of the samples. Graphite block systems present an alternative to the current mainstream technology of open- and closed-vessel (“classical” or microwave-assisted) digestion systems, as they allow large

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numbers of samples to be digested simultaneously, thus overcoming one of the major weaknesses of closed-vessel systems. Commonly employed digestion agents include nitric acid, sulfuric acid, hydrofluoric acid, perchloric acid, and hydrogen peroxide, as well as various combinations of these. Most applications of wet digestion involve aqueous or organic matrices, such as surface waters, waste water, biological and clinical samples, food samples, as well as soil, sediment and sewage sludge, coal, high-purity materials, and various technical materials. More recently, open systems have progressed: the usual digestion ramps consist of several vessels equipped with reflux condensers to limit possible volatilization losses of some analytes and to avoid the evaporation of the reactive mixture. Such assembling is entirely satisfactory for ensuring concurrent digestions of large series of samples. Modern commercially available Hach Digesdahl Digestion Apparatus (Hach Comp., USA) is designed to digest organic and mineral samples for subsequent analysis. 6.5.1.2 Microwave heating (microwave-assisted wet digestion) The most innovative source of energy for wet digestion procedures is microwaves. Because the heating takes place inside the digestion mixture, microwave digestion is more efficient than with conventional means of heating. Using microwaves, both the speed and the efficiency of digestion for some types of samples considered difficult to solubilize are often improved. Additionally, automation becomes possible with some instrumentation. This technique is discussed in detail in Chapter 8 and will therefore only be briefly summarized here. Several different names are applied to this technique. An example of incorrect terminology involves uncritical use of the expression “microwave digestion” for acid digestion with microwave excitation. Although this technique makes use of microwave radiation, the direct effects of this radiation are of minor importance, at most. Microwaves cannot rupture molecular bonds directly because the corresponding energy is too low to excite electronic or vibrational states. Rotational excitation of dipoles and molecular motion associated with the migration of ions are the only processes that are observed in this microwave field [45]. For this reason, an expression such as “microwave-assisted digestion” is preferable and recommended. Since Abu-Samra et al. [46] reported on the application of microwave techniques to wet digestion of biological samples in 1975 (the first paper published on microwave-assisted digestion), there has been a rapid development in microwave-assisted digestion for elemental analysis. Recent reviews [26–41] detail the application of microwave-assisted digestion to a wide variety of sample types, such as geological, biological, clinical, botanical, food, environmental, sludge, coal and ash, metallic, and synthetic materials and mixed samples and present specific experimental conditions as a function of the matrix to be digested. The earliest attempts at microwave-assisted digestion were performed using home appliance microwave ovens. This was necessary because commercial devices were not available at the time. The use of domestic

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microwave ovens in laboratory experiments should be discouraged because of safety and performance. Microwave-assisted digestion in open systems at atmospheric pressure (focused microwaves using open vessels fitted with refluxing facilities made of borosilicate glass, quartz, or PTFE) is generally applicable only with simple matrices or for strictly defined objectives, and the results are reproducible only if the specified digestion parameters are strictly observed. The performance of the focused-microwave-assisted systems and a wealth of applications have been reviewed by White and Mermet [30,31] and very recently by No´brega et al. [47]. Focused-microwave-assisted sample preparation is a suitable strategy for dealing with high masses of organic samples (up to 10 g). Losses may be encountered with mercury and possibly also with organometallic compounds (e.g. those containing arsenic, antimony, or tin). Addition of sulfuric acid is essential in order to achieve a sufficiently high digestion temperature using atmospheric pressure equipment, where the boiling point of the acid establishes the maximum digestion temperature, although it is important to remember that the presence of sulfate interferes with many procedures for metal determination (e.g. graphite furnace atomic absorption spectrometry or electrochemical techniques). New equipment, the non-pressurized digestion systems (STARPlus Systems, CEM Corp., USA; QLAB6000 System, Questron Technologies Corporation, Canada) are at the frontier of this field. They are designed for routine use and can easily be automated. In addition, automated evaporation-to-dryness can be accomplished in an open vessel by attaching a pump to evacuate the fumes while the container is heated and multiple methods for different samples can be simultaneously applied owing to the possibility of operating each reaction vessel independently. All relevant parameters, such as reagent volume, digestion time, applied power, temperature, and addition of reagent composition, lend themselves to straightforward control. Although non-pressurized microwave systems are limited by a low maximum digestion temperature, which cannot exceed the ambient-pressure boiling point of the acid (or the acid mixture), they provide the best option with regard to the safety of personnel, because no overpressure can occur. Moreover, non-pressurized microwaveassisted digestion is suitable for on-line digestions in continuous-flow systems (cf. Section 6.5.3.2). A compact apparatus in which a specific position can be irradiated by microwaves (MW) and ultrasound (US) simultaneously has been developed [48]. The combination of these two types of irradiation, electromagnetic (2.45 GHz) and mechanical (20 KHz), and their application to physical processes like digestion appears interesting. The MW–US reactor has been designed for atmospheric pressure decomposition and dissolution of biological (olive oil) and chemical products (refractory mineral material Co3O4) in nitric acid and hydrogen peroxide. Simultaneous MW and US irradiation is shown as a new technique for atmospheric pressure digestion of solid and liquid samples suitable for chemical and biological analysis.

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6.5.1.3 Ultraviolet digestion (photolysis) Ultraviolet (UV) digestion is utilized mainly in conjunction with uncontaminated or slightly contaminated natural water matrices (aqueous solutions), such as sea, surface, fresh, river, lake, ground, estuarine and coastal water. Liquids or slurries of solids are decomposed by UV radiation (light) in the presence of small amounts of hydrogen peroxide, acids (mainly HNO3) or peroxodisulphate (i.e. beverages, special industrial waste water, water of sewage treatment plants, soil extracts) [49]. Dissolved organic matter (DOM) and complexes of the analyte elements are decomposed to yield free metal ions. The corresponding digestion vessel should be placed in the closest possible proximity to the UV lamp (low- or high-pressure) to ensure a high photon flux. In photolysis, the digestion mechanism can be characterized by the formation of OHz radicals from both water and hydrogen peroxide that is initialized by the aid of the UV radiation [49]. These reactive radicals are able to oxidize, to carbon dioxide and water, the organic matter present in simple matrices containing up to about 100 mg/l of carbon. Complete elimination of the matrix is, of course, possible only with simple matrices or by combining photolysis with other digestion techniques [50]. The method does not oxidize all organic components possibly present in water; chlorinated phenols, nitrophenols, hexachlorobenzene and similar compounds are only partly oxidized. Effective cooling of the sample is essential, because losses might otherwise be incurred with highly volatile elements. Hydrogen peroxide addition may need to be repeated several times to produce a clear sample solution. Modern UV digestion systems are commercially available (see Ref. [49], Table 1). 6.5.2

Closed systems

During the last few decades, methods of wet sample preparation using closed vessels have become widely applied. Closed systems offer the advantage that the operation is essentially isolated from the laboratory atmosphere, thereby minimizing contamination. Digestion of the sample is essentially ensured by a common wet digestion procedure, which is performed under the synergistic effects of elevated temperature and pressure; digestion occurs at relatively high temperature due to boiling-point elevation. The pressure itself is, in fact, nothing more than an undesirable—but unavoidable—side effect. These techniques are generally much more efficient than conventional wet digestion in open systems, the loss of volatile elements is avoided, any contribution to blank values may be reduced and the digestion of more difficult samples is possible. The principal argument in favor of this form of digestion is the vast amount of relevant experience acquired in recent decades. The literature is a treasure trove of practical information with respect to virtually every important matrix and a great number of elements. Closed system digestion is particularly suitable for trace and ultratrace analysis, especially when the supply of sample is limited.

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Because the oxidizing power of a digestion reagent shows a marked dependence on temperature, an arbitrary distinction should be made between low (simple) pressure digestion and high-pressure digestion. Low-pressure digestions (,20 bar) are limited to a temperature of ca. 1808C, whereas with high-pressure apparatus (. 70 bar) the digestion temperature may exceed 3008C. 6.5.2.1 Conventional heating (thermally convective wet pressure digestion) The expression “pressure digestion” is another—very timely—example of incorrect terminology, giving the impression that pressure is critical for the digestion process. Indeed, it is a relatively high boiling temperature that ensures more effective digestion, not the associated high pressure. However, it is important to note that the pressure buildup, amongst other things, does introduce some dangers to the application of these methods. These methods are better described if they are grouped under the title, “wet digestion methods in closed systems”. However, the conventional designation should, in the author’s opinion, nevertheless be retained in this case, if only because of its wide acceptance; attempting to rename the procedure now would introduce more confusion than clarity. The digestion of inorganic and organic substances in sealed tubes was the method first proposed for pressure digestion at the end of 19th century, and some of these applications are still difficult to replace by other digestion methods. The use of sealed glass tubes goes back to Mitscherlich [51] and Carius [4,52], often referred to as the Carius’ technique, first described in 1860. Carius undertook digestion of organic materials with concentrated nitric acid at 250– 3008C. The sample and the acid were mixed in a strong (thick)-walled quartz ampule and sealed. The ampule was transferred to a “bomb canister” and heated in what was called a “bomb oven” for several hours, after which it was cooled, opened, and the contents analyzed. Carius tube digestion involves the generation of internal pressure in excess of 100 bar (atm) at 2408C. The modern redesign and employment of the Carius combustion tube for the digestion of some refractory materials was accomplished at the US National Bureau of Standards during the 1940s [53–55]. Extreme care should be used in the handling and venting of pressurized tubes. A discussion of Carius tube design for minimizing losses and hazards by explosion has been provided by Gordon [54]. For safety, any stainless steel sleeve jacket (along with solid CO2 pellets, to maintain equal pressure across the tube wall when heated) that is large enough to contain the Carius tube will suffice as an external pressure vessel [56]. With the development of the so-called Carius tube, the field of closed-vessel digestion was born. Digestion in autoclaves with metal inner reaction vessels was originally proposed in 1894 by Jannasch [57], but was not widely employed because of a number of drawbacks (such as strong corrosion of the platinum vessel). Seventy years later, May and Rowe [58] designed a new type of

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autoclave with an inner lining made of Pt–Ir alloy (platinum-lined crucible and bomb), which is more resistant than platinum alone, for digestion with hydrofluoric acid. It is difficult to construct an autoclave with an inner metal reaction vessel and it is thus very expensive. Therefore, this technique has not been used extensively in analytical practice and thus the apparatus is not manufactured commercially. Extensive use of pressure digestion in analytical procedures began in 1960, as a result of the considerable technological progress in the manufacture of organic polymers. Convectively heated pressure vessel systems have proved to be the most valuable systems for guaranteeing complete, or almost complete, digestion of solid samples because they provide elevated digestion temperatures (about 200 –2308C [59]). Most sample vessels (containers) for use in thermally convective pressure digestion are constructed from PTFE [60 –62], PFA [63], or PVDF [64], although special quartz vessels with PTFE holders [65] or glassy carbon vessels [66] are available for trace analysis purposes. The sample vessel is mounted in a stainless steel pressure autoclave and then heated, usually in a laboratory drying oven, furnace or heating block, to the desired temperature. Because of the necessity to examine numerous samples, mechanized multisample pressure digestion systems able to process rather large sample numbers of the same matrix type were developed [67]. A cooling circuit can be fitted into the metal casing (jacket) to permit rapid manipulation of the solution formed immediately after removing the “digestion bomb” from the oven or heating block [68]. Dissolution can be also accelerated by mixing the reactants, preferably by using a stirring bar (covered with PTFE) [69]. An alternative design has been proposed by Uchida et al. [70], wherein a small screw cap vial for sample digestion is placed inside the Teflon digestion vessel. This digestion system (Teflon double vessel) can reduce the risk of sample leakage and contamination with extraneous materials. To improve dealing with pressure –temperature evaluation and the carbon balance for some materials, a system with a Teflon-lined membrane pressure meter and a thermocouple was designed [71]. Recently, a digestion vessel for use with a convection oven was proposed [72], which has an unusual design in which the vessel consists of three nested structures: an innermost PTFE container of 30 ml capacity, an intermediate PTFE container of 100 ml capacity, and an outer stainless steel shell. It should be stressed here that digestion in a Teflon-lined pressure vessel using one or a mixture of acids does not result in complete decomposition (see Section 6.5.5), because of the limited temperature. Pressure digestion systems are all feasible below ca. 2008C, but above this temperature PTFE begins to “flow”, rendering it unsuitable for use in high-pressure applications, and consequently at higher temperatures. All thermally initiated digestions have the disadvantage that a considerable amount of time is consumed in preheating and cooling the digestion solutions and sample vessel [73], the limited sample size, and the inability to visually check the progress of the digestion.

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The contributions of Langmyhr, Bernas, To¨lg, and co-workers are worth mentioning with regard to the commercialization of the digestion vessels or “digestion bombs”, as they are often called. Today, there are a number of digestion bombs covering the whole market range, including: the popular Parr acid digestion bombs (Parr Instrument Company, USA), Uniseal decomposition vessels (Uniseal Decomposition Vessels Ltd, Israel), stainless steel pressure vessels with Teflon inserts (Berghof Laborprodukte GmbH, Germany), the pressure decomposition system CAL 130FEP (CAL Laborgera¨te GmbH, Germany), and the pressure digestion system (PRAWOL, Germany). To avoid the problem of loss of mechanical stability at high temperatures, vessels made of quartz are now being used in a new pressure digestion system [74,75]. The introduction of a high-pressure ashing (HPA) technique by Knapp [76] has not only reduced the effective digestion time but also opened the way to digestion of extremely resistant materials, such as carbon, carbon fibers, mineral oils, etc. In academic terms an HPA currently represents the highest standard in pressurized wet digestion techniques, combining the advantages of the Carius technique with easy and safe handling. Essentially complete digestion can be accomplished with the vast majority of samples so far investigated. Nitric acid alone is a sufficiently powerful reagent in many cases. High-pressure digestion is conducted in quartz vessels, with a maximum digestion temperature as high as 3208C at a pressure of ca. 130 bar. For dissolution requiring HF, glassy carbon vessels are used instead of quartz. The quartz vessel (or glassy carbon) is stabilized during the digestion process by subjecting it to an external pressure roughly equivalent to or higher than that developed internally. Because the pressure within the vessel is lower than the pressure applied, the vessel is protected from explosion. A perfected system of wet digestion under high temperature and pressure developed by Knapp is commercially available, the HPA-S High Pressure Asher system (Anton Paar GmbH, Austria). Very recently, again in respect to complete digestion of organic waste materials, a potent digestion technique was developed [77] based on a prototype of an HPA device using infrared heating (IR-HP-asher). High-pressure digestion is conducted in six quartz vessels inside a steel autoclave, with a maximum digestion temperature as high as 3008C at a pressure of 130 bar. The novelty of this approach lies in the design of an HPA system with IR heating. In comparison to open vessel digestion, closed-vessel digestion methods have many advantages, but there is one disadvantage—complex and expensive vessel designs. A new technique—pressurized wet digestion in open vessels— combines the advantages of closed-vessel sample digestion with the application of simple and inexpensive open vessels made of quartz or PFA [78]. The vessels are placed in a High Pressure Asher HPA, which is adapted with a Teflon liner and partially filled with water. The vessels (in principle any shape of vessel can be used) are loaded with sample material and digestion reagent and are simply covered with PTFE stoppers and not sealed. The vessels are transferred to

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a specially adapted HPA and digested at temperatures up to 2708C. The digestion time is ca. 90 min, and cooling to room temperature requires a further 30 min. As metal autoclaves are expensive, a pressure vessel without an outer metal casing has been designed. The technique of digestion at slightly increased pressure has been found to be very useful for routine laboratory analysis, primarily because of its simplicity. Almost an unlimited number of samples can be digested simultaneously. The vessel can be sufficiently well sealed by using a screw cap [79]. Volatile components are not lost during heating and the laboratory atmosphere is thus not contaminated by acid vapors. All-Teflon thick-walled PTFE vessels (bombs) have been used in the dissolution of refractory oceanic suspended matter using HCl, HNO3, and HF [80]. Translucent Nalgene-sealed bottles have been proposed for the “wet pressure digestion” of biological materials (fish, bird, plant tissue) using a combination of HClO4 and HNO3 [81]. A method utilizing a pressure digestion technique for real sample matrices using linear polyethylene bottles has been proposed [82]. Vessels of polyethylene are transparent, permitting observation of the whole digestion process and reduction of the reaction time to a minimum. A complete digestion of fatty material with slight overpressure (,4 bar) was possible in a closed system completely made from quartz [83]. A closed PTFE bomb (30 ml capacity, screw-cap vessel machined from molded, stress-relieved Teflon-TFE rod) was designed for the digestion of materials using a conventional heating (drying) oven [84]. 6.5.2.2 Microwave heating (microwave-assisted pressurized wet digestion) Closed-vessel microwave-assisted digestion technology has been acknowledged as one of the best solutions for clean chemistry applications and has a unique advantage over other closed-vessel technologies. The vessels used for microwave acid digestion are either low-pressure or high-pressure bombs. The current generation of microwavable-closed vessels consists of a two-piece design, liners and caps composed of high-purity Teflon or PFA with casings (outer jacket) made of polyetherimide and polyetherethereketone or other strong microwave transparent composite material. Their practical working temperature is 2608C (softening point of Teflon), and their pressure limit is 60–100 bar. Closed-vessel digestion is ideal for those samples that are being dissolved in HNO3 and/or HCl. However, for those digestions where H2SO4 is required, such as digestions of petroleum products, there is little advantage in using the regular closed-vessel approach because the boiling point of H2SO4 (3308C) exceeds the temperature available in a Teflon-lined vessel. The use of closed-vessel microwave-assisted digestion techniques minimizes the analytical blank by minimizing the amount of reagents used and by controlling the digestion environment, as well as through augmentation of the operator’s skills. Microwaves only heat the liquid phase, while vapors do not absorb microwave energy. The temperature of the vapor phase is therefore lower

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than the temperature of the liquid phase and vapor condensation on cool vessel walls takes place. As a result, the actual vapor pressure is lower than the predicted vapor pressure. This sort of sustained dynamic, thermal nonequilibrium is a key advantage of microwave technology, as very high temperatures (and, in turn, short digestion times) can be reached at relatively low pressures. The inspiration for pressure digestion studies came from a US Bureau of Mines report [85], which described how rapid dissolution of some mineral samples had been achieved using a microwave oven to heat samples and an acid mixture contained in polycarbonate bottles. To overcome the presence of “hotspots” in the oven, which result in uneven heating, they designed a polypropylene rack to fit on top of the standard microwave carousel. Although sealed polycarbonate bottles were used as pressure vessels, the plastic quickly became opaque and brittle (the melting point of polycarbonate is 1358C). Smith et al. [86] substituted Teflon PFA fluorocarbon resin (a tetrafluoroethylene with a fully fluorinated alkoxy side-chain) vessels for polycarbonate because of its superior chemical and mechanical properties. Buresch et al. [87] used low pressure-relief type containers made of PTFE or quartz. Alvarado et al. [88] exploited modified thick-walled Pyrex glass test tubes fitted with polypropylene screw caps as pressurizable vessels. Kojima et al. [89] modified a Teflon digestion bomb by using a double Teflon vessel with a polypropylene jacket to permit leak-free and safe digestion of samples. A closed-vessel microwave digestion system was described [90]. In situ measurement of elevated temperatures and pressures in closed Teflon PFA vessels during acid decomposition of organic samples was demonstrated. Temperature and pressure monitoring permitted controlled digestions, studies of digestion mechanisms, and the development of transferable standard microwave sample preparation methods. Laboratory-made all Teflon bombs, used for low- or medium-pressure work, are also appropriate for microwave-heated digestion purposes [91], some fitted with pressure-relief holes, valves, or membranes (rupture discs). Low-volume microwave-assisted digestion methods have found applications for studies involving small sample sizes where loss of sample in large digestion equipment is inevitable. Small quantities of tissue (5 –100 mg dry weight) are digested in high-purity nitric acid by use of a modified Parr microwave acid digestion bomb with modified Teflon liner [92]. The use of low-volume (7 ml) Teflon-PFA closed vessels designed for the preparation of small-sized (,100 mg dry mass) biological tissues has been described [93]. In order to prevent excessive pressure rises during closed microwave acid digestion of fairly large (1 g) samples having high organic content, an openvessel pre-digestion technique under reflux was designed to allow the escape of oxidation products, such as carbon dioxide, without incurring evaporation losses of acid or analytes. Following pre-digestion, the vessels were capped and subjected to microwaves to complete the digestion under pressure [94].

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In an attempt to minimize the delay in opening Teflon pressure vessels following microwave acid digestion, and thus significantly reduce sample preparation time, digestions with the pressure vessels immersed in liquid nitrogen and the use of liquid nitrogen as a pre- and post digestion coolant were applied [95]. In other developments, a special type of Teflon bomb was constructed in which the vapor pressure can be maintained at a moderate level (up to 5 bar) by means of an internal quartz or Teflon cooling spiral (a water cooling spiral is inserted into a closed space through the cover). During operation, reflux of the condensed acid and water vapors continuously renews the liquid phase over the sample [96]. Several microwave-heating configurations were presented by Pougnet et al. [97,98] based on 500 or 1200 W, 2.45 GHz fundamental-mode microwave waveguide cavities, which heat pressure vessels currently used in laboratories for sample digestion and other applications. The capsule concept was reviewed in detail by Le´ge`re and Salin [99,100]. The sample is handled in an encapsulated form until it is in the digestion solvent. The operation of the capsule-based microwave-assisted digestion system proceeds in several steps, during which temperature and pressure are monitored. The heating in such a system, as in all microwave bomb systems, is from the solution outward, and the system performance is dictated by the same chemical and physical laws governing other microwave-assisted systems. From the previous discussion, it is clear that microwave acid digestion can be easily adapted for closed-vessel digestions; hence, its application has been limited to digestions in closed Teflon-lined vessels made of non-metallic microwave-transparent materials operating with a maximum upper safe pressure of around 60 –100 bar. In response to these limitations, and focusing on the fact that rapid heating of solvents and samples within a polymer vessel can lead to significant advantages over high-pressure steel-jacketed Teflon bombs (which are thermally heated), Matusiewicz [101] developed a focusedmicrowave-heated bomb that would exceed the operational capabilities of existing microwave digestion systems and permit the construction of an integrated microwave source/bomb combination. The combined advantage of having a high-pressure Teflon bomb [59] incorporating microwave heating [31] has produced a focused high-pressure, high-temperature microwave-heated digestion system [101] capable of being water or fluid cooled in situ. Another vessel configuration integrates the microwave chamber around the vessel. These systems consist of one or several microwave transparent vessels (Teflon, quartz), which can either be opened [102] or sealed [103], enclosed in an acidresistant stainless steel chamber. The steel chamber acts as both the pressure vessel and microwave chamber. Modern systems can handle acid decompositions at temperatures up to 3208C and pressures of 130 –200 bar. Very recently, a novel microwave-assisted high-temperature UV digestion for accelerated decomposition of dissolved organic compounds or slurries was developed [104]. The technique is based on a closed, pressurized, microwave

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digestion device wherein UV irradiation is generated by immersed electrodeless Cd discharge lamps (228 nm) operated by the microwave field in the oven cavity. The immersion system enables maximum reaction temperatures up to 250– 2808C, resulting in a tremendous increase of mineralization efficiency. Today, there are a number of microwave-digestion bombs available: Parr bombs (Parr Instrument Company, USA), Berghof all-PTFE digestion vessels (Berghof GmbH, Germany), and TFM digestion vessels (Bohlender, Germany). 6.5.3

Flow systems

Discrete vessel systems, whether at elevated or atmospheric pressure, require a large amount of handling. Processes such as assembling, closing, opening, and positioning the vessel in the ordinary oven or microwave filed are laborious and time consuming. Continuous flow-through thermal digestion, UV digestion, and microwave digestion systems were designed to overcome some of the limitations by replacing the vessels with flow-through tubing (coil). Samples are digested by pumping them through a coil containing a digestion matrix while being heated (by thermal, UV, or microwave). The continuous flow of a carrier stream through these systems washes the system, removing the need for tedious vessel clean-up procedures. These systems can handle reactions that produce sudden increases in temperature and pressure, or unstable samples. Many different designs of flow digestion systems have been published, but very few meet the prerequisites for high-performance sample decomposition. 6.5.3.1 Conventional heating (thermal) Many of the disadvantages of sample digestion can be overcome by automating sample preparation in an enclosed system through the use of flow technology. A well-established digestion system, based on the flow-stream principle, was developed by Technicon [105]. A slowly rotating, very wide glass tube with a spiral-type cavity is heated externally. Liquid sample, together with the decomposition reagent, is continuously pumped in one direction. By rotation of the tube, the decomposition mixture moves through the heated tube within a few minutes. At the other end of the tube, the finished sample is continuously pumped away and is ready for further analytical steps. A disadvantage of this system is that only liquid samples can be used, and the “memory effect” is large. The sample throughput is very high, however, for comparatively small apparatus cost. Another system was presented by Gluodenis and Tyson [106]. Here, the PTFE tubing is loosely embedded in a resistively heated oven. By using PTFE tubing, the maximum digestion temperature is restricted to ca. 2108C. The limited mechanical strength of the material merely allows maximum working pressures of up to 35 bar. Therefore, the usual working pressure is about 10–20 bar. The potential of the system was illustrated by the digestion of cocoa

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powder slurried in 10% HNO3 injected into the manifold and digested under stopped-flow, medium-pressure conditions. In a series of papers [107 –109], Berndt described development of a new high-temperature/high-pressure flow system for the continuous digestion of biological and environmental samples. It was shown [107] that temperatures up to 2608C and pressures up to 300 bar can be reached in a flow system when an electrically heated Teflon lined HPLC tube is used as the digestion capillary. The required back-pressure was obtained by using a restrictor capillary with an inner diameter of 50 mm and a length of about 10 cm. Digested biological samples (blood, liver, leaves) were collected at the outlet of the flow system. In subsequent papers [108,109], an electrically heated Pt/Ir capillary served as a digestion tube at temperatures of 320– 3608C and pressures of about 300 bar, and withstands concentrated acids. Due to the totally glass-free environment, samples having high silicate content can be digested by the addition of hydrofluoric acid. 6.5.3.2 UV on-line decomposition UV digestion is a clean sample preparation method, as it does not require the use of large amounts of oxidants. Furthermore, UV digestion is effective and can be readily incorporated into flow injection manifolds. The sample flows, in the presence of H2O2, H2S2O8 or HNO3, through a tube (PTFE, quartz) coiled around a fixed UV lamp(s). A short review of such flow systems has appeared recently [49]. Analyzers of this kind are produced by SKALAR Analytical, Holland, for example. Fernandes et al. [110] developed a manifold based on a two-stage on-line UV/thermal induced digestion procedure for oxidation purposes. The UV digestion apparatus consisted of a 4 m long PTFE tube tightly wounded directly around the UV source (15 W) to form a spiral reactor. The thermal digestion apparatus consisted of a 2 m long PTFE tube coiled in a helix and submerged in a thermostatic bath at 908C. Flow systems are becoming more popular in analysis, because of their ease of automation, speed, small volume of sample, and elegance, and thus provide for a promising future. 6.5.3.3 Microwave heating (microwave-assisted pressurized flow-through digestion) Many different designs of microwave-assisted flow digestion systems have been published [31,39,111], which open up new possibilities, primarily in fully automated sample preparation for elemental analysis. The earliest work reported in this field was by Burguera et al. [112] who applied a flow injection system for on-line decomposition of samples and determined metals (Cu, Fe, Zn) by flame AAS. The methodology involved the synchronous merging of reagent and sample followed by decomposition of serum, blood, or plasma in a Pyrex coil located inside the microwave oven. This

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approach permits essentially continuous sample digestion and drastically reduces sample processing time, and is suitable for those samples that require mild digestion conditions (especially liquids). According to the location of the digestion unit in the system, there are two types of manifolds described in the literature to date: before and after the injection unit. In the former arrangement, the sample is introduced into the microwave oven in a continuous flow [113] or a stopped flow mode [114]; after digestion, a discrete aliquot is delivered to the detector. In the second arrangement, the injected sample flows to the microwave oven unit together with the reagent(s) to be digested, and is then cooled and degassed prior to its delivery to the detector [115]. In both cases, the measurements can be performed partially or totally off-line or on-line. Solid samples call for more sophisticated flow systems because they need to digested in the presence of highly concentrated acids, which rapidly destroy organic matrices. A first attempt aimed at simplifying manipulation of the digest was reported in 1988 [116]. Lyophilized, finely ground and weighed samples of liver and kidney were placed in test tubes together with mineral acids and the contents shaken before exposing them to microwave radiation to avoid violent reaction with abundant foam formation. The tubes were loaded into a covered Pyrex jar inside a domestic microwave oven operated for a specified time at a given power. Carbonell et al. [113] initiated the determination of metallic elements in solid samples using the slurry approach coupled with microwave oven digestion in a flow injection system for F-AAS determination of lead. Various natural samples (artichoke, chocolate, sewage sludge, tomato leaves), real and certified, were slurried in a mixture of HNO3 and H2O2 using magnetic stirring, followed by continuous pumping around an open recirculating system, part of which (120 cm PTFE tubing) was located in a domestic microwave oven. A microwave-heated, flow-through digestion container (coiled Teflon tubing) was designed for a commercial (Prolabo A300) focused microwave system (instead of microwave oven) and applied to the on-line preparation of biological samples, including milk, blood, and urine [117]. For an extensive oxidation of organic sample constituents with nitric acid, temperatures of more than 2008C are necessary. The PTFE tubes used, however, cannot withstand the vapor pressure of the digestion mixture at 2008C or more. Thus, new alternatives had to be found to overcome this limitation. One way to increase the pressure resistance of the tubes is to wrap them with a plastic tape of high mechanical strength. Results from a digestion system (CEM SpectroPrep system) equipped with such tubes have been published [118]. A CEM SpectroPrep system was used at moderate powers to perform on-line digestion of slurried samples of biological tissues (0.5% m/v) and marine sediment (1% m/v). The pressure thresholds of this system are near 25 bar. To achieve the desired temperatures of approximately 2508C, however, it is necessary to be able to increase the pressure in the system up to 35 bar or

212


so. A recently developed device enables the application of such high temperatures (2508C) by means of a new pressure equilibrium system (with a pressure of 40 bar) [119]. The pressure equilibrium system keeps the pressure inside and outside the digestion tube (PTFE or PFA) equal, even for extremely fast oxidation reactions. Advantages of this high-performance flow digestion device are high sample throughput (up to 60 samples per hour), fast and complete digestion of liquids, emulsions and slurries, and computer-controlled fully automated sample decomposition. The systems ability to handle only up to 1% m/v slurries and lower slurry concentrations for biological materials restricts the type of sample that can be analyzed, unless the most sensitive elemental detection devices are used, such as ICP-MS. Therefore, Mason et al. [120] modified the SpectroPrep oven and developed a wide bore continuous flow microwave digestion system for the determination of trace metals (Cd, Cr, Mn, Ni, Pb) following aqua regia extraction. This device differs from existing commercially available devices as it uses a double pumping action to replace the back-pressure regulator traditionally used to achieve internal pressurization. The described system demonstrated an ability to cope with real soil samples ground to a larger particle size (250 mm) and slurried without the use of surfactants. Perhaps, the current fascination for using microwave heating for on-line digestion has led to the introduction of commercial instruments based on this hybrid technique. CEM developed the SpectroPrep continuous-flow automated microwave-digestion system. Similarly, Questron Technologies Corporation is marketing the QLAB AUTOPREP DISCRETE FLOW SYSTEM. Perkin-Elmer offers an on-line flow injection microwave-digestion system, as does SGT Middelburg BV (The Netherlands, FLOWAVE), a fully automatic continuous flow sample preparation and digestion system based on microwave technology. The advantages of microwave-enhanced flow systems basically include a significant reduction in sample preparation time, the ability to accomplish reactions that would normally be too dangerous in a closed vessel because of sudden increases in temperature and pressure, and the capability to handle transient or readily decomposed samples or intermediates. However, flowthrough systems are a problem area because all samples must be homogeneous and small enough to pass through the tube, and the majority of samples requires some form of processing before they can be put into the tube. 6.5.4

Vapor-phase acid digestion (gas-phase reactions)

An alternative approach to acid digestion of the sample matrix that prevents introduction of impurities exploits gas-phase reactions. In the past four decades, several novel approaches to sample digestion procedures have been considered using inorganic acid vapor produced in one vessel to attack and dissolve material in another. A review by Matusiewicz [121] summarized analytical methods based on vapor-phase attack in promoting the dissolution

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H. Matusiewicz

and decomposition of inorganic and organic materials prior to determination of their trace element content. This approach is currently used (in open, semiclosed, and closed systems) whenever applicable because digestion using gasphase reagents is preferred to the solution. The combination of hydrofluoric acid and nitric acid vapor as a digestion agent has proven effective in the preparation of samples for spectrographic determination of trace impurities in open system. Zilbershtein et al. [122] used this approach to dissolve silicon and to concentrate impurities on a PTFE sheet. The residue and PTFE sheet were transferred to a graphite electrode that subsequently served as one electrode of the dc arc for spectrographic trace analysis. However, dissolution with acids in open systems is unsuitable for attaining a sufficiently low determination when the analyte impurities in the reaction mixture are often far greater than those in the test component. With respect to semi-closed systems, a PTFE apparatus generating HF vapor has been specifically designed to minimize contamination during traceelement determination of ultrapure silicon, quartz, and glass [123]. The sample is placed in a PTFE beaker mounted on a perforated PTFE plate that is kept above the level of liquid HF in the chamber. The apparatus has the advantage of a closed system by preventing air-borne particulates from entering the vessel but is continuously purged by a positive pressure of HF vapor during operation. Thomas and Smythe [124] describe a simple all-glass apparatus for vaporphase oxidation of up to 90% of plant material with nitric acid. Addition of perchloric acid ensured fast and complete oxidation, and the presence of HNO3 during the final HClO4 oxidation step eliminated any danger of explosion. Klitenick et al. [125] used the same technique, with a simplified pressurized PTFE digestion vessel, for determination of zinc in brain tissue. Some materials may not be fully dissolved by acid digestion at atmospheric pressure. A more vigorous treatment involves bomb digestion in pressure vessels designed to incorporate the techniques of a closed pressure vessel and vapor-phase digestion in a single unit. This has the advantage of being easier to construct than the apparatus described in previous papers [122 –125], and it requires considerably smaller volumes of acids. Heating can be accomplished in an ordinary oven (with conductive heating) or using a microwave field. A predecessor of this concept of closed-vessel vapor-phase sample digestion was introduced by Woolley [126]. He described a low-temperature (up to 1108C) and high-temperature (up to 2508C) version of the apparatus. Each device consists of an airtight PTFE vessel containing two concentric chambers: an inner chamber that holds the sample cup and an outer chamber. Both vessels were designed for the digestion of high-purity glass using relatively impure solvent acids: a 50:50 mixture of concentrated HNO3 and HF. A completely closed PTFE bomb or autoclave [127] has been developed with a temperature gradient for digestion of more difficult compounds, such as siliceous material. Marinescu [128] presented an interesting development in which the conventional single-sample pressure digestion bomb was converted for multi-sample

214


vapor-phase digestion. A multiplace holder for field sampling was developed to fit directly into the digestion bomb. This technique has been used for organic and inorganic solid, semisolid, and liquid samples. Kojima et al. [129] modified a sealed PTFE bomb in which the dissolution of highly pure silica with HNO3, HCl, and HF acid vapor was possible using a PTFE vial placed in a PTFE outer vessel. A possible disadvantage of this system is that the vial has to be replaced regularly when used under pressure. This could make the method very costly. A laboratory-made high-pressure digestion bomb with a PTFE microsampling device was developed by Matusiewicz [130]. This simple and inexpensive apparatus was found to be convenient for treating a small number of samples and can be easily made by modifying available PTFE bombs [131]. It should be noted that PTFE microsampling devices can be used for both vapor-phase digestion and discrete nebulization techniques in atomic spectrometry. Vaporphase digestion in a closed system (bomb) of high-purity materials for spectrographic determination of trace elements is a convenient and useful technique [132]. The method uses graphite electrodes with an enlarged cavity and excludes the use of a collector. Contact of the probes and the analytical impurities with laboratory glassware and the atmosphere during the preconcentration process is also avoided. The blank signal is determined only by the purity of the electrode used for spectral analysis. A technique [133] has been developed that employs the vapor-phase acid generated in the quartz vessel of a commercial high-pressure, high-temperature digestion apparatus (High Pressure Asher HPA, Anton Paar, Graz, Austria). Small biological samples (50–165 mg) were digested in a mini-quartz sample holder (3.1 ml volume). When biological standard reference materials were digested at 2308C and 122 bar, the residual carbon content (RCC) in the digested samples was less than 1.8%. Despite methodologies previously proposed for closed systems with conventional heating being successful, very few attempts to employ microwave power for vapor-phase digestions have been described. An early trial with a low-pressure microwave arrangement was unsatisfactory [134], although recently an interesting variant of the digestion vessel design has been proposed for dissolution and decomposition of samples [135]. The method developed was an extension of the acid vapor-phase thermal pressure decomposition of biological materials reported previously by Matusiewicz [136]. Microwaveassisted vapor-phase acid digestion employing a special PTFE microsampling cup, suitable for 250 mg subsamples of marine biological and sediment reference materials were digested with HNO3 and HNO3 –HF, respectively, at a maximum pressure of ca. 14 bar [135]. Very recently, several papers [137–141] discussed the further application and evaluation of this pioneering concept of Matusiewicz et al. [135], employing either commercial pressurized microwave digestion systems and quartz sample containers [137], quartz inserts [138,139], TFM inner vessels [140] or focused microwave ovens operating at atmospheric pressure and PTFE microsampling cups [141].

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H. Matusiewicz

To summarize this section, use of acid vapor-phase digestion and attack of some organic and inorganic matrices as a sample preparation method is a convenient and useful technique. Closed pressure systems are the technique of choice to avoid losses of elements by volatilization while still maintaining extremely low values for the blank (by application of isopiestic distillation of the reagents and technical grade acids). 6.5.5 Efficiency of wet digestion (decomposition and dissolution) procedures Quality control is becoming increasingly more significant in analytical chemistry. However, it is presently applied primarily to measurement techniques and not to sample preparation. For quality control in sample digestion, it is necessary to measure and record certain parameters exactly to be able to subsequently trace the course of the digestion process. In spite of that, complete decomposition of the sample is required to achieve reproducible and accurate elemental results by instrumental analytical methods. This is particularly the case for all voltammetric and polarographic determinations [142–145]. Interferences caused by incomplete decomposed organic compounds also occur, to a certain degree, when using atomic spectrometric methods such as AAS [146,147], ICP-OES [148,149], and ICPMS [150,151]. As noted earlier, nitric acid is the most frequently utilized sample dissolution medium. Unfortunately, the carbon contained in organic materials is only partly converted to CO2 by HNO3 at temperatures of up to 2008C (maximum operating temperature of PTFE vessels) [26]. In these cases, extending the digestion time and increasing the quantity of nitric acid do not improve the extent of decomposition. In principle, temperature and digestion time ultimately determine the effectiveness of a digestion, with RCC serving as a useful measure of quantitative assessment [152–155]; in other words, the highest temperatures are required to achieve a decomposition as complete as possible [156,157]. It should be noted here that the usefulness of the decomposition technique should be judged not from a visual point of view, because it often happens that a clear, colorless solution, indistinguishable from water, still contains significant amounts of carbon. In closed systems the pressure depends not only on the temperature but also on the type and quantity of the sample, the size of the vessel, and the nature and quantity of the decomposition reagent. This pressure is not responsible for the decomposition quality, but nevertheless it should be controlled automatically. Wu¨rfels et al. [157 –159] described the extremely strong impact from residual organic compounds on elemental determinations by means of inverse voltammetry and demonstrated that a temperature of 300 – 3208C is necessary for pressurized sample digestion with pure nitric acid to obtain a solution containing less than 0.1% carbon. Otherwise, trace elements cannot be determined with inverse voltammetry. This was confirmed by

216


Wasilewska et al. [160], who showed that for complete oxidation of organic compounds with nitric acid, the decomposition temperature should be raised to 3008C. The influence of the digestion equipment (either thermal or microwave) is negligible if the digestion time employed is long enough to reach the steadystate temperature. Sample digestion with nitric acid between 220 and 2508C (most commercial equipment is able to fulfill this prerequisite) leads to RCCs in the low percentage range. The mode of heating of the digestion vessels is more and more supplanted by microwave technology; therefore, microwave-assisted wet digestion is a frequently used sample preparation technique for trace element determinations in organic materials. Studies of the RCC as a measure of decomposition efficiency have been undertaken [134,161 –165]. Using gas chromatography, Stoeppler et al. [152] quantified the ashing ability of conventional pressurized decomposition. Differences between the carbon content in the original sample and the carbon converted to CO2 showed that the investigated biological and environmental samples were not completely ashed with nitric acid. Wu¨rfels and Jackwerth [166] determined the residual carbon in samples digested under pressure or evaporated with HNO3. In most cases, microwave digestion of biological material was incomplete. Subsequently, the undigested compounds were identified [156]. Parallel to Wu¨rfels and Jackwerth’s studies [166], the residual organic species in nitric acid digests of bovine liver were identified by Pratt et al. [167]. Kingston and Jassie [168] evaluated the dissolution of several biological and botanical samples wet digested with HNO3. Free amino acid concentrations of human urine samples were typically reduced by a factor of 105. This reflects the comparative efficiency of protein hydrolysis, and is not necessarily equivalent to the total carbon oxidation efficiency. Nakashima et al. [163] investigated the digestion efficiency of HNO3 –HClO4 mixtures. The total RCC in a number of digested marine biological reference material (NRCC TORT-1) solutions was determined and used as a relative measure of the efficiency of various digestion schemes. Two-stage microwave digestions (i.e. conventional digestion followed by cooling, venting of excess gas from the bomb, re-capping, and re-heating) were superior to single-stage digestions. However, even the two-stage procedures were not complete and 24% carbon remained. The determination of residual carbon in digests of biological material with simultaneous ICP-OES analysis was described by Hee and Boyle [164] and Krushevska et al. [165]. The oxidation efficiencies of different dry and wet ashing procedures for milk samples were compared by Krushevska et al. [169], who noted that the residual carbon concentrations obtained with mediumpressure microwave digestions varied between 5 and 15%. Oxidizing mixtures of H2O2 or H2SO4 with HNO3 applied in a medium pressure (11 bar) microwave system did not yield a digestion efficiency higher than that for pure nitric acid (total acid volume was kept constant). Thus, until now, no suitable closed lowor medium-pressure microwave-heated oxidation system has been available to completely decompose biological samples leaving no carbon residue. In spite of

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that, the safest way to obtain total mineralization is to complete these decomposition techniques with the addition of perchloric acid followed by heating until white perchloric fumes appear. However, with the high pressure/ temperature focused-microwave-heated TFM-Teflon bomb device, organic material is totally oxidized with nitric acid in a single-step procedure [101,103] (the closed TFM-Teflon focused-microwave-heated bomb enables very high pressure and temperature to be reached). Continuous or stopped flow on-line microwave-heated digestion appears very attractive because it can increase the sample throughput and extend the automation of sample handling. However, on-line microwave-heated digestion was a priori expected to be associated with the problem of incomplete digestion of organic matter, because complete decomposition can only be achieved under vigorous conditions requiring high temperature and pressure. Matusiewicz and Sturgeon [170] critically evaluated on-line and high-pressure/temperature closed-vessel techniques with regard to efficiency of digestion. The completeness of destruction of biological materials (standard and certified reference materials) was characterized in terms of their RCC in the solution following digestion. Pressurized decomposition in a TFM-Teflon vessel was the most effective procedure (organic material was totally oxidized with nitric acid in a single-step procedure), whereas urine and sewage plant effluent were incompletely decomposed (between 56 and 72%) with on-line microwave-heated digestion using nitric acid, nitric acid and hydrogen peroxide, and peroxydisulphate oxidation. Very recently, the residual weight of a bottom anti-reflective coating (BARC) sample was successfully used as an indicator to evaluate the digestion kinetics [171]. The weight degradation rate was independent of the sample weight under various temperatures, but was strongly dependent on the digestion acid volume and the digestion temperature. Mathematical modeling for prediction of digestion efficiency for the BARC sample was achieved by employing digestion kinetics as the backbone. With empirical fitting of a pre-exponential factor, a novel equation incorporating the temperature, acid digestion volume, digestion time, and sample weight was developed. As a result, appropriate digestion parameters could be logically evaluated using the resultant model to achieve the desired digestion efficiency. Hydrogen peroxide is a very popular oxidizing reagent as it is converted to water and oxygen during the oxidation of biological material [134,172 –174]. No acid corrosion of the digestion vessel PTFE walls, no formation of insoluble salts with an acid anion, and no change of the sample matrix by an acid are additional advantages. Because of its strong oxidation power, only small amounts of H2O2 need be used, so that concentrated sample solutions can be obtained. Furthermore, high purity H2O2 is available and low blank concentration values (and thus low detection limits) can be achieved. However, explosion can occur if too much H2O2 is present. In addition, experiments with HNO3 –H2O2 mixtures conducted by Matusiewicz et al. [134] showed that all versions of pressurized microwave digestion with HNO3 and H2O2 gave

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incomplete digestion. No significant improvement in the efficiency was achieved with 50% H2O2. The extension of this observation to mediumpressure and high-temperature microwave heating provided verification of this observation [175]. Nitric acid digestion with the addition of H2O2 did not enhance digestion efficiency in this study compared to use of only HNO3. Wet acid digestion of aquatic samples is one of the most efficient processes; however, it is time consuming and there is risk of contamination by the reagents used. Thus, an alternative oxidizing reagent is desirable to completely and safely decompose organic carbon residues. The application of ozone seems to have advantages in this respect. It was found that ozone is very effective in destroying natural organic compounds [176 –178], and has the potential to be used as an additional decomposition and/or finishing reagent [179]. Improvement in the pressurized microwave-assisted digestion procedure was achieved by adding optimum concentrations of strong oxidizing agents such as ozone [175]. The digestion efficiency of an optimal nitric acid system was improved by 13% by addition of ozone, with the further advantage that the agent does not contribute to the blank. A single digestion procedure is often insufficient for the complete decomposition of a complex matrix, leading some authors to recommend a combination of two or more techniques. Two examples will suffice to illustrate the principle [104,143]. First, pressure digestion followed by UV photolysis. Thus, it has been shown that analysis of olive leaves for heavy metals by voltammetric methods leads to distorted results after “pressure digestion” alone. Reliable data can be obtained only by supplementing the digestion with UV irradiation to ensure adequate decomposition of the matrix [143]. Second, a novel microwave-assisted high-temperature UV digestion procedure was developed for the accelerated decomposition of interfering dissolved organic carbon prior to trace element determination in liquid samples. This new technique significantly improved the performance of the process of UV digestion (oxidation) and is especially useful for ultratrace analysis due to its extremely low risk of contamination [104]. In order to investigate the completeness of dissolution of inorganic materials, the recovery (or incomplete recovery) and accuracy of major, minor, and trace element determinations are usually applied. If silicates are present, which is usually the major inorganic component of many matrices (i.e. soils, sediments, sludges, ceramics, and other similar samples), the use of HF to achieve complete dissolution is mandatory [180,181]. 6.5.6

Comparison of wet digestion techniques

A careful comparison of several digestion techniques is the only way of assuring accurate results, particularly when little experience is available with respect to the digestion of a specific matrix, or existing reports are contradictory. The analyst must choose the sample preparation technique carefully to ensure that

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the system is optimal for the analyses at hand. However, there is still no universal sample preparation system. With respect to requirements specific to contamination or losses through volatilization or retention, convection-heated or microwave-assisted wet digestion, quartz-lined high-pressure wet digestion, UV digestion, and vapor-phase acid digestion seem to be the best choice. However, all of these techniques require considerable investment for apparatus. Digestion of samples in an open vessel presents a serious risk of significant analyte loss, despite the use of a reflux condenser. As far as economic aspects are concerned (low procurement, short digestion time, high sample throughput), microwave-assisted wet digestion and especially microwaveassisted pressurized on-line digestion appear to rank high. According to completion of the digestion, complete degradation of many samples is achieved only through high-pressure, high-temperature Teflon- or quartz-lined pressure vessel digestion, or by combination of a closed wet digestion system with UV irradiation. Table 6.4 summarizes the advantages and disadvantages of the wet digestion techniques discussed in Section 6.5 with respect to losses of analytes, blank levels, contamination problems, sample size, digestion time, degree of digestion, and economic aspects. 6.5.7

Digestion systems (instrumentation, equipment, automation)

Presently, the instrumentation market offers many devices to make wet digestion more efficient and easier to manage by means of possible automation, but this is achieved principally with microwave energy. Wet digestions in open vessels are undertaken with or without refluxing. Because it is very critical to adhere very closely to the optimized time and temperature digestion parameters, mechanization of the digestion not only leads to higher sample throughput with less human intervention but also to the avoidance of errors. The simplest form of mechanization can be implemented through a time (programmable timer) and temperature (via an autotransformer) controlled heating block. There are many models of heating blocks on the market. A greater degree of mechanization would also incorporate control of reagent reflux during digestion. These procedures operate batch-wise. Continuous sample handling has some advantages over discontinuous handling; the former generally better matches analytical needs. The automated wet digestion device (VAO, Anton Paar, Austria) is such a continuously operating digestion system, an ideal instrument for laboratories requiring high throughput of similar samples with which all methods of wet chemical digestions can be performed [182]. With the help of a microprocessor, all important digestion parameters are controlled. Automation controls the time – temperature/pressure program for sample digestion, so that different sample materials can be processed under optimum conditions. The loading or charging of the high-pressure asher with sample

220

TABLE 6.4 Advantages and disadvantages of wet digestion methods Digestion technique

Open systems Conventional heating

Possible way of losses

Volatilization

Microwave heating

Volatilization

UV digestion

None

Closed systems Conventional heating Microwave heating

Source of blank

Acids, vessels, air Acids, vessels, air

Sample size (g)

Maximum

Digestion time

Degree of digestion

Economical aspects

Inexpensive, needs supervision Inexpensive, needs supervision Inexpensive, needs supervision

Organic

Inorganic

Temperature (8C)

Pressure (bar)

,5

,10

,400

Several hours

Incomplete

,5

,10

,400

,1 h

Incomplete

Several hours

High

,90

Liquid

Retention

Acids (low)

,0.5

,3

,320

,150

Several hours

High

Retention

Acids (low)

,0.5

,3

,300

,200

,1 h

High

Needs no supervision Expensive, needs no supervision continued

TABLE 6.4 (continuation) Digestion technique

Possible way of losses

Source of blank

Sample size (g) Organic

Inorganic

Temperature (8C)

Pressure (bar)

,0.1 (slurry)

,0.1 (slurry)

,320

.300

Flow systems Conventional heating

Incomplete digestion

Acids (low)

UV on-line digestion


None

Microwave heating


Acids (low)

,0.1 (slurry)

None

,0.1

Vapor-phase None acid digestion

Maximum

,90

Liquid

Digestion time

Degree of digestion

Economical aspects

Several minutes

High

Several minutes

High

Expensive, needs no upervision Inexpensive, needs no supervision Expensive, needs no supervision

,0.3 (slurry)

,250

,40

Several minutes

High

,0.1

,200

,20

,1 h

High

Needs no supervision


material is achieved manually. A fully automated version of this high-pressure asher is not available. Berghof pressure digestion systems [183] serve for sample preparation of inorganic and organic matrices at high temperature (max. 200 – 2508C) and high pressure (max. 100 and 200 bar) in pure, isostatically pressed PTFE or quartz vessels. As noted already, three basic types of microwave-assisted digestion systems have evolved: atmospheric pressure, elevated pressure (closed vessel), and flow-through, working in the two most common modes: multimode cavity and focused-type (waveguide). Reviews of commercially available microwaveassisted digestion systems and vessels (summary of the vessels, ovens, and oven systems) are given in Refs. [29–32,35,37,41,184 –186] together with specifications and features for elevated-pressure, atmospheric pressure, and flow-through units. The reader should also consult Chapter 8 of this volume. The simplicity and efficacy of microwave digestion easily lends itself to automation and robotics. Systems have been developed that are capable of weighing samples, adding acids, capping and uncapping vessels, accomplishing microwave-assisted digestion, diluting digestates, transferring vessels, and even cleaning and reusing the vessels. Once such a system is operational, the only things the analyst has to do is supply and place the representative sample(s) in locations recognized by the system and then initiate the controlling program (Table 18 in Ref. [35] primarily summarizes the application and functioning of these systems). 6.5.8

Safety of acid digestions (sample acid digestion safety)

The reagents, instruments, and operations employed in the digestion of materials are potentially hazardous, even when used as directed. The operator must always be properly protected with a laboratory coat, gloves, and safety glasses or, better still, face protection. Some concentrated fuming acids (HF, HNO3, HCl) are to be handled only in a well-ventilated hood. Oxidizing acids (HNO3, HClO4) are more hazardous than non-oxidizing acids (HCl, H3PO4, HF), being more prone to explosion, especially in the presence of reducing agents, such as organic matter. Perchloric acid is oxidizing only when it is concentrated and hot; it must never be brought into contact with organic matter unless diluted with nitric acid. Acid digestion must be conducted in a fume cupboard with efficient scrubbers installed. The evaporation of perchloric acid is to be performed only in an appropriate stainless steel, stoneware, or polypropylene hood, with washing facilities to eliminate any perchlorate deposit. Great care should be taken when using “pressure digestion” methods. Pressure digestion vessels (bombs) contain the acid fumes and are useful for rapid, one-step digestions without losses. But, again, there are restrictions; in some reactions (especially spontaneous) potentially explosive gases are

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produced that exceed the safety limits of the vessels. For instance, nitric acid and especially the spontaneous HNO3 and H2O2 digestion of organic matter in a closed vessel may result in explosion due to unintended pressure build-up within the vessel. These systems produce high-pressure spikes, which can be avoided by decreasing the sample weight or by applying a gradual temperature increase. Microwave-assisted sample digestion has its own safety requirements. As a result of the direct energy absorption and rapid heating, microwave techniques introduce unique safety considerations that are not encountered in other methods. Differences in conditions between traditional laboratory practices and microwave-implemented methods should be examined before microwave energy is applied to heat reagents or samples. An excellent summary of this aspect is extensively reviewed in Refs. [30,31].

6.6

CONCLUSIONS AND FUTURE TRENDS

The chief methods used for the digestion of organic and inorganic samples have been evaluated. A brief summary of applications of these techniques to various sample matrices is presented in Table 6.5. The variety of approaches currently available for the digestion of solid and liquid samples allows the most suitable method to be selected for each application, depending on both the matrix and type of analyte, and the subsequent steps to be developed in order to complete the analytical process. In spite of that, it is fair to point out that sample digestion must not be looked at as an isolated step, but one that needs to be integrated into the entire analytical process. Attention has been focused on digestion at elevated temperature and pressure. High-pressure digestion with its large digestion temperature range is the most universal digestion system at present, and is the technique of choice for the vast majority of both inorganic and organic materials. New ways to further increase the efficiency of sample preparation should continue with development of hyphenated digestion techniques. A novel, microwaveassisted, high-temperature UV digestion for accelerated decomposition of dissolved organic compounds or slurries was developed [104]. This new technique is ideal for extreme trace analysis due to the low blank values and low acid concentration. In addition, this digestion method can be used for the determination of non-metals by ion chromatography. Alternatively, within the limits of the Teflon-lined digestion vessels, improvement in the digestion efficiency can be achieved by adding optimum concentrations of strong oxidizing agents, such as ozone or oxygen, which appear to be efficient digestion agents for the treatment of biological material. Again, this has the advantage that the agent does not contribute to the analysis blank. It should be mentioned that vapor-phase acid digestion offers an alternative solution to

224

TABLE 6.5 Summary of applications of total wet digestion procedures to the analysis of materials (determination of elements) Required acid(s)a

Digestion technique (modeb)

Reference

Water(s) Environmental samples Coal Coal fly ash Dust Catalysts Waste materials Sewage sludge Waste water Biological samples Botanicals Plants Clinical Marine Forensic Food(s) Beverages Silicates Soils Sediments Glasses Geological samples Rocks

H2O2, HNO3

UV radiation

[49]

HNO3, HCl, HF Aqua regiac þ HFd Aqua regia þ HF Aqua regia

Open or closed system Open or closed system Open or closed system Open systems

[20,31,35,38] [20,31,35,38] [20,31,35,38] [20,21]

HNO3, HCl HNO3

Open or closed or flow systems Flow systems

[18,31] [31,39]

HNO3 þ H2O2 þ HF HNO3 þ H2O2 þ HF HNO3 HNO3 HNO3 HNO3 HNO3, H2O2

Open or closed system Open or closed system Open or closed system Open or closed system Open or closed system Open or closed system Open or closed or flow systems

[15,16,18,31] [15,16,18,31] [15,16,18,31] [15,16,18,31] [41] [31,41] [41]

Aqua regia þ HF Aqua regia þ HF HF

Open and/or closed systems Open and/or closed systems Open systems

[19–22,35] [19–22,35] [19–22]

Aqua regia þ HFe

Open or closed systems

[20–22,31,33] continued


225

Material/matrix/sample

226

TABLE 6.5 6.4 (continuation) Required acid(s)a

Digestion technique (modeb)

Reference

Ores Minerals Petroleum products Fuels Oils Drugs and pharmaceuticals Metals Ferrous Non-ferrous Alloys Steels Chemicals Polymers Refractory compoundsg Ceramics Composites Nuclear materials

Aqua regia þ HF HF þ H2SO4, HCl

Open or closed systems Open systems

[20– 22,31,33] [20– 22,31,33]

HNO3 þ HCl HNO3 þ HCl HCl, HNO3

Open or closed system Open or closed system Open systems

[23,31] [23,31] [41]

HNO3 þ (HF or HNO3 or H2SO4) HCl or HNO3 or HF Aqua regia þ HF HCl þ HNO3, HClO4f HCl, HNO3, HF, H2SO4 HCl, HNO3, HF, H2SO4

Open systems Open systems Open systems Open systems Open or closed systems Open or closed systems

[41] [41] [41] [41] [20,23] [23,41]

HNO3, HCl, HF, H2SO4, H2O2 HNO3, HCl, HF, H2SO4, H2O2 HNO3 or HCl, H3PO4, HClO4

Open or closed systems Open or closed systems Open or closed systems

[20] [20] [21]

a

Concentrated acids are usually employed; H2O2 is 30%; in most cases alternative digestions are possible depending on requirements of analyst. b Conventional or microwave. c Unstable. d Use only Teflon vessels, the addition of HF is required to obtain quantitative recoveries for Cr. e Addition of H3BO3 to neutralize the HF by forming tetrafluoro-boric acid. f Danger of explosion. g Certain refractory materials are not decomposed; these must be solubilized by fusion.

H. Matusiewicz

Material/matrix/sample


these problems: reduced concentration of acid in the digestate and the possibility of using a technical grade acid without any deterioration of the analytical blank. Another example where significant improvement in digestion and dissolution was obtained is the use of a reactor that combines microwave and ultrasound energy [48]. It is expected that these two methods could open a new research field “combined digestion techniques”. It can be said with certainty that the majority of all digestions will be performed in the future by means of microwave assistance. Progress has been made over the past several years in reducing systematic errors and improving detection limits with microwave digestion, as well as its automation. A noticeable trend toward pressurized closed-vessel systems permitting hightemperature decomposition compatible with trace analysis has occurred. While some researchers advocate high-pressure (.100 bar) digestion at 250 –3008C to destroy interferences in refractory compounds, manufacturers are working to devise sample vessels that can withstand these conditions. There has been a growing trend in recent years toward development of fully automated on-line analysis techniques. Microwave-assisted high-pressure flow digestion system with PTFE or PFA tubes for digestion temperatures up to 2508C opens up new possibilities for fully automated sample preparation [119]. On the other hand, the development of new high-temperature/high-pressure flow digestion systems that incorporate resistively heated capillaries for the continuous digestion of various samples coupled with atomic spectrometric instruments has arisen [107 –109]. It is predicted that flow systems will become dominant for liquid samples and slurries and extend the analytical capabilities of instrumental methods by combining sample preparation with simultaneous analysis using only micrograms of sample and microliters of reagents. The final goal of these studies should be the adaptation of standard batch digestion methods to on-line systems combining flow-through digestion directly to analyzers. It is evident that wet digestion methods will remain a fertile area for development. New digestion techniques need to be designed that address the limitations of the instrumentation and maximize its potential. Development trends for conventional and microwave instruments will focus on sample throughput, enhanced vessel performance specifications, the use of new materials, further refinement of in situ vessel control (direct temperature and pressure, incident and reflected microwave power), and computer-controlled sample digesters with automated capability. Finally, the development of automated methods for wet digestion of solid samples without human participation can only be achieved with the use of a robotic station. Nevertheless, a number of auxiliary energies and commercially available modules can facilitate and/or accelerate one of the most timeconsuming steps of the analytical process, i.e. to obtain the analyte(s) from a solid sample in the form of a solution.

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233


Chapter 7

Dry ashing Michel Hoenig

7.1

GENERAL CONSIDERATIONS

Trace element determinations in most inorganic analytical laboratories are usually performed using atomic spectroscopic techniques. The market for this type of instrumentation was principally initially dedicated to the analysis of liquid samples. Only a few manufacturers provide equipment suitable for direct analysis of solid samples: X-ray fluorescence spectrometry (XRF) and arc/spark optical emission spectrometry (OES). These two techniques were intensively utilized between the 1950s and 1970s; presently, they cover, with difficulty, environmental applications necessitating determinations of elements at trace or ultra-trace concentrations. These techniques remain in use in several industrial domains, however, owing to several practical aspects, i.e., analyses can often be performed directly on solid samples, overcoming the problems associated with dissolution procedures. In environmental monitoring and health diagnostics, the determination of trace elements has, since the 1970s, generally been tackled using atomic absorption and atomic emission techniques. Flame atomic absorption spectrometry (F-AAS) offers detection power ranging from the mg/l to the mg/l level, depending on the element considered. Electrothermal atomic absorption spectrometry (ET-AAS) is able to address, on average, the mg/l range. However, AAS techniques are limited to the determination of metallic elements only. During the 1970s, inductively coupled plasma optical emission spectrometry (ICP-OES) appeared, exhibiting a detection power lying between F-AAS and ET-AAS. Recently, during the 1980s, instrumentation resulting from the coupling of an ICP excitation source with a mass spectrometer (ICP-MS) became commercially available. The detection power of ICP-MS is very high, on the order of ng/l or lower. Both ICP-OES and ICP-MS permit the multielement determination of metals and non-metals. Moreover, in comparison with AAS, particularly ET-AAS, the analytical throughput using plasma-based techniques is considerably enhanced. Comprehensive Analytical Chemistry XLI Mester and Sturgeon (Eds.) q 2003 Elsevier B.V. All rights reserved

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Currently, substantial improvements to the utility of such instrumentation are continuously sought. However, in such situations where a dramatic lowering of detection limits is obtained, the risk of errors suddenly appearing due to sample handling is increased. Prior to commercial introduction of this modern instrumentation, these “new” errors were practically imperceptible to the determination of relatively high analyte concentrations that were measured with less sensitive techniques. The danger of contamination is now increasingly present: the choice of sample preparation procedure, the quality of its application and the need for an adequate laboratory environment have therefore become the most critical points defining successful trace element determinations. Some basic notions that will afford a better understanding of the general philosophy regarding existing trace element analysis principles will be enumerated herein. First, it is necessary to understand that chemical analysis comprises a set of closely bound steps: for example, the choice of sample preparation procedure will depend on the measurement technique used and vice versa. It is then not sufficient to simply intuitively apply a non-validated procedure (mineralization, dissolution, measurement technique, etc.) to a sample having an unknown composition. The set of analytical criteria has to be selected following a global consideration of the final objective—reliable results in terms of accuracy and precision. The topic is quite vast and for this reason, in this chapter, trends relating to dry ashing methods, their principles, advantages and drawbacks, are discussed. It is hoped that the necessary ingredients needed to resolve usual cases will be treated here or in the references cited. It remains for the analyst to choose the appropriate preparation methodology in relation to the sample type, the available equipment and any imperatives of the analysis and, indeed, of the whole study. In most cases, preparation of solid samples involves several stages: drying (air, laboratory oven, etc.), homogenization (mixing, crushing, etc.), grinding (mills, mortars, etc.), followed by mineralization and dissolution of a subsample. The solution so obtained is ultimately diluted to volume. Ideally, the organic fraction of the sample has been decomposed and completely eliminated during these preparation steps and only dissolved inorganic compounds constitute the dissolved residue to be analysed. In the following sections, cases associated with the determination of total analyte content in the sample will be discussed. This generally means that quantitative dissolution of solid samples is required. To ensure this criterion is met for some difficult types of solids, particularly those having a matrix containing silicate compounds (soils, sediments, plant material), the known procedures are often too labour- or time-consuming to be systematically applied in routine analyses. For environmental monitoring purposes, for example, they are often replaced by simpler and more easily applicable procedures. However, these substitutive methodologies rarely lead to an accurate determination of

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total analyte content, but they are generally sufficient to satisfy the objectives of the study. Before the analysis, samples of organic or of a mixed nature are subject to two distinct steps, which often take place simultaneously: mineralization and dissolution. Samples of purely inorganic composition are simply dissolved. The composition of environmental and biological samples varies from purely inorganic (e.g. fly ash) to purely organic (e.g. fats), but generally, they are an intermediate combination of these extremes. This implies that the total dissolution of samples usually cannot be achieved in a single step using a single reagent. In practice, the necessary number of steps and reagents is dictated by the matrix composition. Purely organic or mixed samples are usually brought into solution by some type of oxidation process combined with an acid dissolution of the resulting residue, as well as of the initial inorganic part of the matrix. In 1844, Fresenius and von Babo [1] published their method for the destruction of animal tissues prior to trace element determinations. In the intervening years, many procedures have been described for this purpose. However, despite numerous possible variations, almost all of the methods fall into one of two main classes, i.e. dry ashing and wet digestion. Dry ashing methods are especially appropriate for samples having a high organic matter content. The first step of the method ensures the decomposition of organic matter by heating the sample to a relatively high temperature, with atmospheric oxygen serving as the oxidation agent. Chemical compounds (the so-called ashing aids) may sometimes be added to aid this process. The second step of a dry ashing method is the subsequent solubilization of the resultant ash using an appropriate acid or a mixture of acids. With wet digestions, the applied temperature is much lower, liquid conditions are maintained during the entire process and oxidizing agents in solution complete the oxidation. Generally, various combinations and proportions of strong acids with hydrogen peroxide ensure the decomposition of organic matter and the dissolution of the residue. Detailed aspects and the role of wet digestion procedures are discussed in Chapters 6 and 8. Depending on the sample type, the dissolution procedure generally involves several steps. Here, the terminology is precise: the term “mineralization” relates to samples having a totally or partly organic matrix only (animal and plant tissues, food samples, soils, etc.). Prior to the analysis, any organic compounds present must be decomposed and/or completely eliminated by the mineralization procedure. Using various reagents, the organic matter is decomposed into carbon dioxide, nitrogen oxides and water, thus liberating into solution all elements initially associated with it. After the mineralization procedure, the resulting sample residue should be essentially inorganic: it will be subject to a final dissolution step similar to that used for a sample having an initially total inorganic composition (rocks, metals, etc.). For more complex samples (organic plus inorganic composition: soils, sludge, plant samples, etc.),

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chemical reagents and physical means are most often used to ensure these two roles (mineralization and dissolution) are simultaneously achieved. The objective of the sample preparation stage is usually to bring all available means into play in order to determine as readily as possible the elements of interest. First, these means have to convert the sample to a form that is compatible with the measurement technique utilized (generally a dissolution). Second, they should ensure the transformation and simplification of the matrix (mineralization: wet digestion, dry ashing). Additionally, they may also perform the analyte separation or preconcentration (topics treated in subsequent sections). 7.2

WHY DRY ASHING?

Surprisingly, after the appearance of commercial advertising praising the universality and absolute necessity of wet digestion microwave heating devices for trace element analysis, several scientific papers have radically condemned dry ashing procedures, despite their long record of usefulness, e.g., “…from the general point of view, dry ashing decomposition is very problematic, especially in the determination of the trace content of heavy metals…” [2] or “…Dry ashing should be completely abandoned from the protocols of trace analysis. The experimental parameters are poorly reproducible and make dry ashing very error prone…” [3]. Moreover, “…as a result of risk of systematic errors related to dry ashing, the Measurement and Testing Programme (formerly BCR) of the European Commission has decided to withdraw mineralization procedures based on dry ashing from certification of reference materials…” [4]. In contrast, many respected institutions, such as the AOAC International or the Nordic Committee on Food Analysis, as well as numerous other laboratories, have developed, verified, validated and successfully used classical dry ashing in practical analyses of a number of materials of biological origin. The same conclusions are perceivable in the very interesting and comprehensive study on the efficiency of dry ashing procedures for electrochemical methods published by Mader et al. [5]. Additionally, our own extensive experience in the field of sample preparation shows that, when performed well, conventional dry ashing leads to complete removal of the organic matrix and to accurate analytical results for most usually determined elements. Better than the other known mineralization procedures, dry ashing methods ensure the quantitative decomposition and elimination of organic matter and an efficient liberation of elements initially associated with it. Usually, these procedures are performed by calcination at atmospheric pressure in programmable muffle furnaces. The commonly utilized temperature for this step is 4508C. In addition to conventionally heated muffle furnaces generally employed for dry ashing purposes, the market now also provides microwave furnaces especially adapted to attain elevated temperatures. The

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unique advantage of the latter is the capacity to ensure application of very fast heating ramps. However, this interesting property is not applicable directly to usual dry ashing procedures, because heating ramps needed here have to be precisely slow. Additionally, a low temperature ashing (LTA) procedure in electronically excited oxygen plasma is available [6], very useful for sample preparation when volatile elements are to be determined. The instrumentation is, unfortunately, very expensive and not readily available at present. In addition, LTA is a particularly time-consuming procedure. In the usual high temperature ashing, fresh or dried (generally 103– 1058C) samples are weighed into suitable ashing vessels (vitreous silica, porcelain, platinum) and placed in the furnace. The temperature is then progressively elevated, following a convenient heating program, to attain 4508C, and then maintained for several hours. The resulting inorganic residue (ash) is dissolved using an appropriate acid. The resulting solution is transferred to a volumetric flask, diluted to volume and analysed. Depending on the initial sample condition, results are expressed based on a fresh or dry weight basis. The application of dry ashing methods is simple and large series of samples may be treated at the same time. This is not their unique advantage—compared to wet digestions, dry ashing procedures present several other useful characteristics: †

†

†

The principal advantage of dry ashing procedures is the possibility of treating large sample amounts and dissolving the resulting ash in a small volume of acid (generally nitric or hydrochloric). This procedure permits preconcentration of trace elements in the final solution, which is useful when very low analyte concentrations are to be determined. Such an advantage is not realizable with wet digestion methods. Additionally, heterogeneity is a typical property of many biological materials. The possibility of processing larger masses of sample, which, upon mineralization, provides a homogeneous solution, helps to minimize subsampling errors. The sample matrix is substantially simplified and the resulting ash is completely free of organic matter. This is a prerequisite for ensuring accuracy with some analytical techniques (e.g. ICP-MS, ICP-OES with ultrasonic nebulization, electrochemical methods) wherein analyte response may be influenced by the presence of residual carbon or some undigested organic molecules. The resulting solutions are of very acceptable aspect (clear, colourless and odourless), rarely the case when wet digestion methods are used and residual carbon content often attains elevated values. This is systematically observed not only for both conventionally heated and open microwave digestions (our experiments), but also for closed microwaveassisted digestions [7]. With high-pressure bombs, the residual carbon content is significantly lower but it is never quantitatively eliminated as in the case of dry ashing (our experiments and Ref. [8]). Compared to wet digestion methods, reagent volumes and their handling are reduced in dry ashing procedures.

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†

The acidity of the final solutions can efficiently be controlled: the acid is added directly to the ash and only a small fraction is consummated during its dissolution. With wet digestion procedures, added acids must also ensure the destruction of the organic matter and their effective amounts utilized during these chemical reactions vary quite significantly. This results in unknown acid concentrations in the final solutions to be analysed. This fact is at variance with well-known requirements for all methods based on atomic spectroscopy concerning the need for similarity of acidities between standards and samples. In some situations, this similarity is absolutely obligatory, e.g. for the determination of nickel by ICP-MS. The commonly used nickel cones in the ICP-MS interface always produce relatively high Ni backgrounds due to their finite dissolution by the aerosol being introduced. With variable acid concentrations, the background can vary significantly from one sample to another, resulting in erratic Ni-results. Such unfavourable conditions may be avoided using dry ashing methods that ensure a practically constant acid concentration from sample to sample, allowing consistent ICP-MS determinations of nickel to be made.

Despite these several advantages, one must also accept several drawbacks of dry ashing procedures: the chemistry of the charring process is very complex, as one may have both oxidizing and reducing conditions varying throughout the sample and this may also occur as a function of time [9]. Moreover, during the combustion process, the actual temperature in the sample remains unknown; in some cases, it may be several hundred degrees above that of the furnace [10,11]. This may result in poor recoveries of some elements due to volatilization losses. A preliminary control of the methodology is then absolutely mandatory: the whole procedure must first be validated using certified reference materials having composition similar to the samples to be analysed. However, the same remark applies to all other methodologies. 7.3

OXIDATION PROCESS AND DISSOLUTION OF THE RESIDUE

Even the experimentally well-established procedures based on conventional dry ashing largely lack an exact and objective process interpretation in physicochemical terms. Hence, a need exists for a greater elucidation of what can so far be considered as mostly empirically established procedures of classical dry ashing. Mader [12] studied how classical dry ashing actually proceeds in terms of dynamics and chemistry of organic matter degradation. Results not only revealed the strong exothermic character of decomposition during charring but also a possible variability, depending on the specific type of biological material. The former findings call for sufficient moderation of the charring step (ramp heating) in order to prevent local overheating of the sample and subsequent risk of loss of a fraction of the analyte due to its mechanical removal in the form

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of solid particles of aerosol (smoke). Only under such controlled conditions, classical dry ashing can have the potential to yield accurate results. The term “dry oxidation” is generally applied to those procedures wherein organic matter is oxidized by reaction with gaseous oxygen, generally with the supply of energy in some form [13]. Included in this general term are methods in which the sample is heated to a relatively high temperature in an open vessel (conventional dry ashing), or in a stream of oxygen or air. In addition, related low-temperature techniques employing excited oxygen, bomb methods using oxygen under pressure and the classical oxygen flask technique in which the sample is ignited in a closed system must also be included. All these methods involve two processes, although the relative significance of each of them varies from one method to another, i.e., they ensure evaporation of the moisture and of volatile materials, including those produced by thermal cracking or partial oxidation, and ensure the progressive oxidation of the non-volatile residue until all organic matter is destroyed. Although these processes occur in all dry oxidations, it is not always possible to distinguish them as separate events. They are probably most easily separated in the conventional ashing procedure in which the organic material is heated in an open vessel with free access to air or oxygen. In usual analytical practice, the first steps of such a procedure are usually conducted at a temperature much lower than that used to complete the oxidation. This is largely to prevent the ignition of the volatile and inflammable material produced by the process of destructive distillation and partial oxidation, as this would lead to an uncontrolled rise in the temperature resulting in an increased danger of analyte losses. The analysis of petroleum products presents an exception, because it is practically impossible to overcome their auto-ignition in a muffle furnace, even using very low heating ramps. These samples are often purposely ignited in order to remove the bulk of the inflammable material before commencing the actual ashing procedure. Our experience has repeatedly shown that the most severe element losses are systematically observed during the heating ramp, not as a consequence of very high final ashing temperature. An inadequate heating ramp may provoke the auto-ignition of the sample and the resulting rapid temperature increase results in volatilization losses. A preliminary low temperature treatment in the conventional process can be achieved in many ways: by heating gently over a flame, on a heating plate, in a sand bath or with an overhead source of heat such as an infrared lamp. The most often utilized means of avoiding ignition problems is direct insertion of the sample into the muffle furnace at low temperature, followed by heating with an appropriately slow ramp. Before the last stage of the process—progressive oxidation of the nonvolatile residue—the material remaining after the preliminary treatment is a more or less porous mass of charred organic matter containing variable amounts of inorganic material distributed throughout it. In reality, this picture is highly variable and it will depend not only on the type and the composition of

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samples analysed, but also on the action of possible reagents added that can change the initial chemistry of the process. Consequently, the kinetics of oxidation of such material will be dependent on the nature of the material itself, the inorganic substances it contains and its particle size and porosity. Tentative findings derived from such reactions with pure carbon or graphite can only be applied with caution to the complex chars existing in dry ashing of real samples. The temperatures generally recommended for dry ashing are, at about 5008C, low compared with those reported for the oxidation of graphite [14], but the chars produced are probably far more reactive due to unknown catalytic effects of the inorganic constituents present. The large amount of work published on the oxidation of material such as coal, coke and charcoal is probably more relevant to dry ashing discussed here, but the increased complexity makes the results more difficult to interpret. In dry ashing procedures used for the analysis of environmental or biological samples (animal and plant tissues, food samples, blood, milk, etc.), the final temperature is maintained for several hours. If the oxidation is achieved under optimal conditions, it leads to white or light grey coloured ashes, easily soluble in acids. Sometimes, depending on the sample type, the oxidation of organic matter is not completely achieved; in this case, the ash exhibits darker spots (dark grey to black) attributable to insufficiently oxidized carbon. Because this phenomenon is always responsible for a difficult subsequent dissolution (often resulting in incomplete recoveries for several elements), such a residue must be re-treated using a few drops of nitric acid and briefly recalcinated at the usual ashing temperature. After this treatment, ashes generally become clear and easily soluble. During the oxidation process, the analyte(s) will behave in one or more of a number of ways. Ideally, they will quantitatively remain in the residue (ash) arising from the oxidation, and in a form in which they can be readily recovered, generally by a simple dissolution of the ash in an appropriate acid. Fortunately, for the usefulness of the method, this is the case for most analytes and samples. In some cases, a part (or the total) of the analyte may be converted to a volatile form that may escape from the vessel (i.e. volatilization losses) or may be combined with the vessel surface or with some components of the inorganic residue remaining after oxidation (i.e. retention losses). In practical trace element analysis, the most often reported volatilization losses pertain mainly to mercury, arsenic and selenium. The analysis of mercury may be considered as a particular case and it will not be treated here: its high volatility implies the application of a preparation procedure exclusively based on wet digestion methods. Alternatively, with the help of a particular atomic absorption technique preceded by the combustion of the sample in a stream of oxygen and the subsequent amalgamation of mercury followed by its thermal release, this element can also be determined directly in the solid sample, thereby avoiding any mineralization problems (AMA-254, Altec, Czech Republic or DMA-80, Milestone, USA).

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On the other hand, retention losses result in poor recoveries of one or more analytes using the normal procedure for solubilization of the ash. They are generally observed for a particular quality of ashing vessel or in the presence of silicates or other insoluble compounds in the sample matrix. 7.3.1

Particular case of plant matrices

During intercomparison studies involving analyses of plant matrices, significant discrepancies amongst results are often observed between laboratories using simple mineralization procedures and those that apply procedures that include a hydrofluoric acid attack followed by evaporation to dryness. In the former, the values obtained are systematically lower because complete digestion is not achieved. In the often utilized wet digestion procedures, mixtures of various acids with hydrogen peroxide may also lead to poor recoveries due to the presence of silicate compounds in the sample or to (co)precipitation phenomena [15]. As a consequence, Al, Fe, Cu and Mn, in particular, are not completely recovered, depending upon the specific plant matrix, probably related to the binding of analytes with the insoluble residue [16–18]. Mader et al. [19] and Quevauviller et al. [20] have noted similar problems with agricultural matrices such as composts, animal meats or brewers yeast. These statements, among others, are supported by an interesting study performed some years ago by Ledent et al. [21] wherein a variety of plants were charred at 4508C and the ash dissolved with hydrochloric acid. After filtration, washing and drying, insoluble solid residues were analysed directly by optical emission-arc spectrography. Expressed as the sample dry weight, the Si content of the plants studied ranged between 0.3 and 10.3%, the highest being exhibited by a rice sample. The insoluble residue was also the highest for this sample (11.3%). Consequently, this means that 1% of other elements is retained in this insoluble residue. For several plants studied, analyses of insoluble residues have revealed that a significant fraction of elements (major, minor, trace) is retained, varying between 0.06 and 95%, depending on the type of sample and the element studied. The most affected elements were Al (14–95% retained), Fe (7 –35%) and B (0.8– 23%). Consequently, we often use the recovery of aluminium as a marker for the procedure; if the Al recovery is incomplete, it may be concluded that the dissolution step was not performed under good conditions and that many other elements may be affected in a similar fashion. The same work [21] has shown that, in some cases, Na, an element generally considered to be easily solubilized, may also not be recovered in solution, often with more than 20% lost. Retained percentages of Ca, Mg, Cu and Mn were lower in all cases, ranging between 0.1 and 4%. For trace elements, the limited sensitivity of the technique used did not provide results

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with good precision, but it was clear that the retention of some elements in the ash, such as Pb, Ni, Cr, V and Ti, may occur. A further detailed study focussing on retention phenomena relating to dry ashing of plant samples has been performed by Hoenig and Vanderstappen [22]. Here also, losses of trace elements induced by incorporation into the insoluble residue were by far the most important: after a simple dissolution of ash by nitric acid losses could reach 20% for Pb, 15% for Cd, 10% for Cu, 8% for Zn and 5% for Mn. Such problems highlight the paramount need for a more elaborate dissolution procedure for plant ashes. For this reason, we have repeatedly claimed the absolute necessity of utilizing an HF-assisted dissolution of the ash if the objective is the determination of total element content. This problem, typically associated with plant samples, is similar when applying a wet digestion procedure: if an insoluble residue remains, an additional HF step, followed by evaporation to dryness, must also be performed. Another example highlighting this problem was reported by Dams et al. [23], who demonstrated that ICP-MS interferences from residual silicon resulted in up to 30% positive bias in intensities from 63Cu, 65Cu and 55Mn derived from soil and sewage sludge digests, due to spectral interferences from 28 27 Si Alþ, 28Si35Clþ and 28Si37Clþ. Addition of an HF step to the procedure followed by evaporation to dryness removed the interfering Si and permitted accurate analysis to be obtained. Such a procedure is not always easy to achieve with most commercially available microwave heating (closed) devices and is, in any case, much more difficult to apply than with a dry ashing procedure. Finally, plants are often considered as purely organic samples with some trace elements present. It is clear that the aforementioned problem is comparable to that encountered in soil, sediment, sludge and rock samples where silicon is typically the primary matrix element. In this case, however, all analysts are aware of the absolute necessity of dissolving the entire sample if the total analyte content has to be determined. 7.4 7.4.1

METHODOLOGY Heating devices

Dry ashing methods can be applied to mineralization of organic materials, biological tissues, plant and food samples, sludge, etc. Well mastered, they ensure total destruction of the organic matter; the associated elements are generally transformed to carbonate or oxide forms. At present, they are generally performed using fully programmable (ramp and holding times/temperatures) muffle furnaces equipped with an efficient temperature control and reproducible thermal programs. Required intermediate evaporations to dryness are usually achieved on sand baths or on hot plates.

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7.4.2

Ashing vessels

It is mandatory to select an ashing temperature that ensures quantitative decomposition of the organic matter without partial or total loss of analytes by volatilization or by their incorporation into a residue that is insoluble in usual reagents. The latter may result from formation of refractory oxides, from combinations with other sample constituents present, as well as from reactions with the walls of the crucible. As noted earlier, one of the causes of losses during dry ashing procedures is the reaction of the analyte with some of the solid matter present in the system. In order for a reaction of this nature to constitute a problem, it is first necessary that it occurs to a significant extent and, second, that the product of the reaction be insoluble in the reagents generally used for dissolving the resultant ash. The solid matter available for such a reaction is generally the material of the ashing vessel and the residue from the sample itself. It is obvious that their nature will have a considerable effect on the extent of the losses. The most commonly used ashing vessels are made of silica or porcelain, with platinum as a third alternative. Vitreous silica is a glass consisting almost entirely of SiO2 (^99.8%), with some Na, Al, Fe, Mg and Ti oxide impurities, whereas the glaze on porcelain ware is a more complex material containing Al, K, Ca and Na oxides (up to 30%) in addition to silica. For both vitreous silica and porcelain, the obvious reaction is between the oxide of the analyte and the ashing vessel to produce a complex silicate, resulting in a loss. Studies with radiotracers performed by Gorsuch [24] have shown that retention of metals by reaction vessels made of vitreous silica may be very significant during dry ashing. The most affected element is silver (47%), followed by gold (17%), copper (4%), lead and cobalt (0%). This type of reaction clearly occurs, but it is dependent on many factors. Some oxides react much more readily than others and, even if silicates are formed, some will be stable to subsequent acid attack while others will readily be decomposed and so cannot be considered to cause losses. These reactions will, of course, be exacerbated if the ashing vessel is made of silica or porcelain, exhibits a marked weakening of the silicate structure, or a worn surface because of extensive use. Because the extent of such reactions remains unknown, the alternative practice of using essentially inert platinum crucibles is much more reliable. This metal is virtually unaffected by any of the usual acids, including hydrofluoric. Platinum is attacked only by concentrated phosphoric acid at very high temperatures and it dissolves readily in mixtures of hydrochloric and nitric acids (or of nitric acid with other chlorides). Consequently, platinum vessels cannot be used for aqua regia digestion procedures. Of course, the initial cost for platinum is significantly higher than for other types of ashing vessel, but its lifetime is practically unlimited. Moreover, the total cost of a modern programmable muffle furnace equipped with a set of

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platinum crucibles is, in any case, much less than the cost of commercial microwave-assisted wet digestion systems. 7.4.3

Influence of the sample composition

An additional retention problem encountered with dry ashing procedures is that posed by the sequestering action of some materials produced during ignition. The binding of iron by condensed phosphates produced by the action of heat on simple phosphates [13] or retention of several elements on silicate compounds present in the sample [25] is the best-known example. Munter et al. [26] reported Al, Fe and Cr losses as a result of their retention on the ash residue, and Cu through its retention on the silica crucible, after ashing at 450– 5008C. Similarly, Dahlquist and Knoll [27] found losses of Fe, Ni, V, Al and Cr due to their retention on insoluble silica residues, although these were not observed when nitric acid was used as an ashing aid. Koirtyohann and Hopkins [28] demonstrated that the losses of Cd, Cr, Fe and Zn from animal tissue samples ashed at temperatures below 6008C were also attributable to retention on the insoluble residue. The often contradictory observations described in all these, and other studies, illustrate the complexity of the retention problems and the need for an adequate dissolution step. On the other hand, with the exception of arsenic and selenium, these considerations indicate that several losses reported as being due to volatilization are, in reality, due to retention problems. Dry ashing temperatures commonly used for trace element analysis range between 450 and 5008C. They are generally high enough to ensure a complete oxidation of the organic matrix while avoiding the dangers of volatilization losses for most analytes (except Hg, As and Se). Losses are further minimized if the ashing temperature is attained using a slow ramp (8 h from ambient temperature) that prevents any local hot spots or self-ignition of the sample from developing. Volatilization losses can also result from overshoot of the maximum temperature set, hence the importance of efficient temperature control on the heating device. 7.4.4

Operating modes for environmental samples

The objectives of dry ashing methodology are very simple and may be summarized as follows: an efficient decomposition of organic matter without losses of elements of interest, and a subsequent easy dissolution of the residue. For most samples and analytes studied, the application of a conventional dry ashing method satisfies these requirements and numerous alternatives are then not necessary. From the literature cited, and from our own experience, it may be stated that a unique procedure also permits good recoveries for most major (Na, K, Ca, Mg, P), minor (Al, Fe, Mn) and trace (B, Be, Ba, Cd, Co, Cr,

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Cu, Ni, Mo, Pb, Sb, Tl, V, Zn) elements in environmental, biological and food samples. Of course, the above-mentioned problem associated with arsenic and selenium remains. Dry ashing procedures are apparently slow, sometimes requiring up to 24 h per sample, but they have the advantage of being readily adapted to a large batch processing and do not require constant operator attention. The operating modes for two of the most often utilized dry ashing procedures are presented below: Materials: platinum crucibles, programmable muffle furnace, sand bath or hot plate, demineralized water, concentrated nitric and hydrofluoric acids of adequate purity, appropriate micropipettes, calibrated flasks, storage bottles. Procedure 1: dry ashing method for mineralization of animal tissues, milk, fruit juices, blood, faeces, urine, etc. (and all samples of organic nature without silicates in the matrix). If well applied, this procedure is safe and gives very accurate and reproducible results for major, minor and trace elements (As and Se excepted) [29]. If the % moisture is required, its determination may be performed using a separate vessel: after weighing of the raw sample, it is dried for 16 h at 1038C, cooled in a desiccator and weighed again. † † †

† †

Following the measurement technique used, weigh in a platinum crucible 0.2 –2.0 g dried sample (1058C) or up to 10 g fresh sample. Place the crucible into a cold muffle furnace and progressively elevate the temperature to attain 4508C in 8 h. Maintain this temperature for 5 h, cool. If the resulting ash is not of white or light grey colour, add 0.5 ml demineralized water and 0.1 –0.2 ml nitric acid to the residue. Evaporate to dryness on a sand bath or a hot plate, place the crucible again into the muffle furnace, elevate the temperature to attain 4508C in 2 h and maintain it for 1 h. After this treatment, ashes generally become clear and easily solubilized. Dissolve the cool residue with 1 ml nitric acid and 20 ml demineralized water and heat to a gentle boil. After cooling, quantitatively transfer the solution to a 50 or 100 ml calibrated flask and dilute to volume with demineralized water.

Procedure 2: modified dry ashing method of the Comite´ Inter-Instituts d’Etude des Techniques Analytiques (CII) for mineralization of plant samples with removal of silica (may possibly be used for soils, sludges and sediments having high organic content). This reference dry ashing method dedicated to plant tissues was tested and largely validated during the long existence of the CII group [30–37]. If well performed, this dry ashing method is safe and gives very accurate and reproducible results for major, minor and trace elements in all plant and similar samples (As and Se excepted). If the % moisture is required, perform its determination using a separate crucible, as noted earlier.

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† † †

† † †

Following the measurement technique used, weigh in a platinum crucible 0.2 –2 g dried sample (1038C). Place the crucible into a cold muffle furnace and progressively elevate the temperature to attain 4508C in 8 h. Maintain this temperature for 5 h, cool. Following the initial sample weighing, moisten the residue with 0.5 –2.0 ml demineralized water, add 1.0 – 3.0 ml concentrated nitric acid and 0.5 –2.0 ml concentrated hydrofluoric acid. Evaporate slowly to dryness on a sand bath or on a hot plate. Repeat the last step two times. Dissolve the cool residue with 1 ml nitric acid and 20 ml demineralized water and heat to a gentle boil. After cooling, quantitatively transfer the solution to a 50 or 100 ml calibrated flask and dilute to volume with demineralized water.

As reported in a previous study [37], amongst all the procedures known for the mineralization of plant samples, only a method able to volatilize silicon allows one to avoid problems due to retention losses of trace elements by insoluble silica residues. Only methods that include hydrofluoric acid treatment will ensure the removal of silicon and the mobilization of analytes into solution. Solutions obtained using this dry ashing method permit all major, minor and trace elements usually studied (As and Se excepted) to be easily determined by all atomic spectrometric techniques (F-AAS, ET-AAS, ICP-OES, ICP-MS). With the objective being multielement routine analysis, it would be highly desirable to also determine arsenic and selenium in these same solutions. In addition, because of the very low As and Se contents in most plant samples, the effective preconcentration that can be achieved during dry ashing is also welcome. Unfortunately, this alternative initially seems to be unrealizable because of possible As and Se volatilization losses. Nevertheless, recent studies indicate that volatilization losses of arsenic and selenium during the ashing of plant material can be avoided [38]. 7.5

PARTICULAR CASES OF ARSENIC AND SELENIUM

It is likely that the major analytical concern has been with the determination of these two volatile elements occurring in low concentrations in environmental materials. As might be expected, virtually all the usual oxidation procedures have been applied to ensure their recovery, with a pronounced preference for wet oxidation methods. The number of references to methods for decomposition of organic samples in the literature is immense, practically impossible to summarize. Many comparisons of various methods have been conducted and, despite inevitable contradictions, some conclusions can be drawn. Sample preparation for the determination of arsenic and selenium is generally performed using wet digestions that are, from the outset, less likely

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to suffer from volatilization losses. Nevertheless, more than for other analytes, and independent of the preparation procedure adopted, there are other severe problems that can arise during the analysis of these elements. These are due to a number of unfavourable analytical factors, ranging from insufficient instrumental sensitivity (ICP-OES with low nebulization efficiency, ET-AAS with energetically poor primary sources) and/or low analyte levels (milk, plant and food samples), to more pronounced spectral interferences (ET-AAS with a structured background at the As and Se analytical wavelengths, ICP-MS with the unique As isotope and possible 75As/40Ar35Cl interference) and unusually severe matrix effects (ET-AAS with strongly decreasing slopes of working curves and HG-AAS with changing hydride generation kinetics in the presence of some matrices). Such conditions sometimes necessitate such extreme dilution factors that they are incompatible with the initial analyte content needed to permit final determinations (ET-AAS analysis of urine and milk). Precisely uncontrolled matrix effects generally lead to signal suppressions resulting in low recovered values. However, volatilization losses (preparation steps), preatomization losses (ET-AAS) or As losses in the condensation stage of ultrasonic nebulizers (USN-ICP-OES) also result in low recovered values. In such situations, the weak link in the analytical chain remains unknown, particularly for inexperienced analysts. This clearly indicates that not only preparation steps, but measurement steps also have to be carefully controlled. In other words, it can be said that the entire methodology used for arsenic and selenium remains one of the most arguable points encountered in environmental trace element analysis. Turning first to a consideration of methods used for dry ashing, a number of recovery experiments have been reported, and quite significant controversy amongst the results is to be seen. Despite the great volume of work devoted to investigating the recovery of arsenic during dry ashing, it is apparent that much remains to be done. Studies such as that of Hamilton et al. [39], in which a radioactive tracer is fully incorporated into the sample, need to be extended, with both animals and plants being used, and with the effect of the many possible ashing aids being investigated. One point appears clear, arsenic recoveries from plant samples are significantly higher than those from samples of animal origin, such as blood, bone or kidney [24,39,40]. Similar recovery studies have also been performed with selenium; e.g., Reamer and Veillon [41] have proposed both wet and dry oxidation methods, which lead to complete selenium recoveries. Unfortunately, these claims were based on experiments with biological materials containing added selenium only and, consequently, were not representative of real forms of selenium likely to be present in the samples. The suitability of dry oxidation methods for recovery of arsenic is, at present, somewhat uncertain. Charring with magnesium nitrate as an ashing aid appears to be the most satisfactory of the many dry procedures described, but more rigorous testing is required, using a wide variety of different samples.

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In the discussion concerning the recovery of arsenic from biological material, the nearly 60-year-old work of Satterlee and Blodgett [42] is of considerable significance. They found a thermolabile arsenic-containing fraction in blood and tissues that was lost on drying at temperatures as low as about 608C. If such substances are really of common occurrence, the use of practically all available dry and wet oxidation methods of sample decomposition is essentially forbidden for preparation of such materials. 7.5.1

Ashing aids

In its initial form, a dry ashing procedure cannot be considered appropriate for preparation of samples to be used for the determination of As and Se. However, many of the reported ashing methods describe the addition of some extra inorganic compounds to the sample to improve the efficiency of the procedure. These added materials are generally called ashing aids, and they serve one or both of two purposes, i.e., to facilitate the decomposition of the organic matter, or to improve the recovery of the element to be determined. The most common aid, used to purely hasten the oxidation of organic material, is nitric acid. It is generally added towards the end of the ashing process to decompose small amounts of remaining carbonaceous material (see Section 2.2.4.4). Because the ash from most biological materials contains up to several tens of per cent carbonates, nitrates are formed in situ after the addition of nitric acid. Additional ashing is then, in fact, melting with nitrates and should help to remove the most resistant degradation products present in the organic matrix. This step, leading to the production of a clean ash, has to be performed with care because when appreciable amounts of organic material are still present, it can cause the ignition of the residue when it is returned to the furnace, resulting in a possible loss of material. Some substances serve as auxiliary oxidants as well as serving other purposes. These are commonly the nitrates of light metals such as magnesium, calcium or aluminium, which decompose on heating to yield oxides of nitrogen. Of all the ashing aids noted, magnesium nitrate is probably the most widely used. According to Gorsuch [13], these auxiliary oxidants also fulfil the important function of being an inactive diluent in the process. As the organic matter in a sample is progressively decomposed, the analytes are brought into closer contact with the material of the vessel and other constituents of the residue. If a reaction with them is feasible, then the increased proximity will increase the chance of its occurrence. Under these circumstances, dilution of the ash with an inert material, such as magnesium oxide, should greatly reduce the possibility of undesirable solid state reactions, resulting in improvement of recoveries. The well-known utilization of relatively unstable magnesium nitrate as an ashing aid likely offers both the advantage of more rapid oxidation and of decreased retention losses.

250

Dry ashing

These oxidative-dilution agents improve recoveries without entering into any reaction with the sample itself. Another group of ashing aids achieves the same end by altering the chemical nature of some of the constituents. The best example of this is the use of sulphuric acid to convert volatile chlorides to involatile sulphates; this may prevent losses of Cd, Pb or Cu at ash temperatures up to 7508C [43]. Arsenic and Se determinations can, in some cases and under particular conditions, also benefit from the advantages offered by a dry ashing procedure. The addition of ashing aids—generally MgO and/or MgNO3—can give rise to less volatile As or Se compounds during the ashing procedure. The successful use of ashing aids is, of course, strongly dependent on the initial form of the analyte. In any case, utilization of ashing aids is a particularly delicate step because some successful examples cannot lead to generalizations: for routine use, the procedure necessitates a serious and time-consuming validation for each type of sample analysed. In addition, the utilization of ashing aids significantly increases the total dissolved solids content of solutions and enhances the dangers of contamination, limiting the use of this approach for ICP-MS analysis. General recommendations for the use of ashing aids may be found in the appropriate Analytical Methods Committee report [44]. 7.5.2

What to do?

Using radiotracers, Gorsuch [39] reported recoveries of As of about 90% for a cocoa sample following a dry ashing at 5508C (99% with Mg nitrate). Similarly, Hoenig et al. [38] have shown that a dry ashing procedure (4508C) applied without ashing aids to plants of terrestrial origin (leaves, grasses, etc.) provided consistent recoveries for As and Se but this was not the case for plants of aquatic origin (algae). Arsenic recoveries from blood samples treated without ashing aids at various temperatures are widely divergent and may vary between 0% (500 and 7008C ashing temperature) and 23% (4008C) [40] or between 57% (8508C) and 72% (4508C) [39]. This clearly shows that the form of the analyte initially present in the sample (organic, inorganic, oxidized, etc.), as well as the particularly experimental conditions used, plays, more than for other analytes, roles of paramount importance during the analysis of arsenic. It appears that As in terrestrial plants is present in an inorganic form (e.g. oxides, probably coming from dust emission) that is not volatilized during dry ashing. In plants of aquatic origin, it is perhaps represented by more volatile organic species formed after its assimilation directly from water. Despite these suppositions, the complete recovery of arsenic from terrestrial plants cannot be explained because arsenic oxides possess melting/boiling points that are lower than the applied ashing temperature of 4508C. Amongst other possibly more refractory compounds, only arsenic sulphide, having a boiling point higher than 7008C, might be considered, but

251

Michel Hoenig

in this case a reaction is difficult to formulate. Because a plant matrix may contain up to 5% K and Ca and up to 0.5% Mg, Na and P, more probable is the possible participation of these elements or their compounds, which might act as “natural” ashing aids during the process. But, here again, an unwelcome question arises: why is arsenic lost during the dry ashing of aquatic plants and animal tissues, where the matrix is of similar composition to the matrix of terrestrial plants? For animal tissues, the only real difference is the absence of silicates (that might ensure possible retention of As and then prevent its volatilization) but in aquatic plants silicates are also present at significant concentration levels (e.g. IAEA-0390 Algae). Currently, there is no consistent explanation for the good arsenic recoveries obtained during dry ashing of terrestrial plants. An explanation of the characteristics of selenium is equally difficult. This element closely resembles sulphur in many ways, and forms an extensive number of organic compounds. For trace element analysis, as in the case of arsenic, wet oxidation procedures have found much wider acceptance than dry ashing methods, due to the readiness with which the analyte may be lost during ignition. Oelschla¨ger [45] described the use of a dry ashing procedure in the presence of magnesium nitrate after a preliminary treatment with nitric acid; it is possible that the use of this ashing aid is adequate to prevent undesired reducing conditions or to permit other chemical reactions. For example, the presence of undigested carbon in the sample may lead to losses of As and Se as volatile carbides. The role of magnesium nitrate is to further oxidize any incompletely digested components of the organic matrix during dry ashing treatment by elimination of reactive carbon and to favour a possible mechanism that prevents the production of such carbides [46,47]. It must be acknowledged that numerous laboratories currently successfully apply various alternatives of this methodology. In an unpublished study, we have tested an additional approach for possible application of dry ashing for the determination of As. In order to modify initial forms of As, or the form of the sample matrix, or both, various plant and animal tissue reference samples were pretreated using a wet digestion (nitric acid plus hydrogen peroxide under reflux), followed by evaporation to dryness before the application of a classical dry ashing procedure. This approach should result in an original procedure that might combine the advantages of both wet and dry methods utilized in laboratory practice. The preliminary wet oxidation treatment modifies the matrix and removes a large fraction of the organic matter initially present. Such pretreatment avoids the often reported dry ashing losses due to self-ignition of the sample during the ashing ramp, but probably also provides a matrix modification that results in a more efficient retention of As in the residue. With this relatively time-consuming preparation methodology, As recoveries increased up to about 60% (in comparison with the

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