Hostname: page-component-cd9895bd7-p9bg8 Total loading time: 0 Render date: 2024-12-23T12:43:07.450Z Has data issue: false hasContentIssue false

Outcomes of 12 Years of the Reynolds Cup Quantitative Mineral Analysis Round Robin

Published online by Cambridge University Press:  01 January 2024

Mark D. Raven*
Affiliation:
Commonwealth Scientific and Industrial Research Organisation, Land and Water and Mineral Resources, Waite Road, Urrbrae, South Australia, Australia
Peter G. Self
Affiliation:
Commonwealth Scientific and Industrial Research Organisation, Land and Water and Mineral Resources, Waite Road, Urrbrae, South Australia, Australia
*
*E-mail address of corresponding author: [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

In 2000, The Clay Minerals Society established a biennial quantitative mineralogy round robin. The so-called Reynolds Cup competition is named after Bob Reynolds for his pioneering work in quantitative clay mineralogy and exceptional contributions to clay science. The first contest was run in 2002 with 40 sets of three samples, which were prepared from mixtures of purified, natural, and synthetic minerals that are commonly found in clay-bearing rocks and soils and represent realistic mineral assemblages. The rules of the competition allow any method or combination of methods to be used in the quantitative analysis of the mineral assemblages. Throughout the competition, X-ray diffraction has been the method of choice for quantifying the mineralogy of the sample mixtures with a multitude of other techniques used to assist with phase identification and quantification. In the first twelve years of the Reynolds Cup competition (2002 to 2014), around 14,000 analyses from 448 participants have been carried out on a total of 21 samples. The data provided by these analyses constitute an extensive database on the accuracy of quantitative mineral analyses and also has given enough time for the progression of improvements in such analyses. In the Reynolds Cup competition, the accuracy of a particular quantification is judged by calculating a “bias” for each phase in an assemblage. Determining exactly the true amount of a phase in the assemblage would give a bias of zero. Generally, the higher placed participants correctly identified all or most of the mineral phases present. Conversely, the worst performers failed to identify or misidentified phases. Several contestants reported a long list of minor exotic phases, which were likely reported by automated search/match programs and were mineralogically implausible. Not surprisingly, clay minerals were among the greatest sources of error reported. This article reports on the first 12 years of the Reynolds Cup competition results and analyzes the competition data to determine the overall accuracy of the mineral assemblage quantities reported by the participants. The data from the competition were also used to ascertain trends in quantification accuracy over a 12 year period and to highlight sources of error in quantitative analyses.

Type
Article
Copyright
Copyright © Clay Minerals Society 2018

References

Altomare, A. Burla, M. C. Giacovazzo, C. Guagliardi, A. Moliterni, A G G Polidori, G. and Rizzi, R., 2001 Quanto: a Rietveld program for quantitative phase analysis of polycrystalline mixtures Journal of Applied Crystallography 34 392397.CrossRefGoogle Scholar
Aplin, A.C. Matenaar, I.F. and van der McCarty, DK P ^BA, 2006 Influence of mechanical compaction and clay mineral diagenesis on the microfabric and porescale properties of deep-water Gulf of Mexico mudstones Clays and Clay Minerals 54 500514.CrossRefGoogle Scholar
Bergmann, J. and Kleeberg, R., 1998 Rietveld analysis of disordered layer silicates Materials Science Forum 278-281 300305.CrossRefGoogle Scholar
Blanc, P. Legendre, O. and Gaucher, E.C., 2007 Estimate of clay minerals amounts from XRD pattern modelling: The Arquant model Physics and Chemistry of the Earth 32 135144.CrossRefGoogle Scholar
Calvert, C.S. Palkowsky, D.A. Pevear, D.R., Pevear, D.R. and Mumpton, F.A., 1989 A combined X-ray powder diffraction and chemical method for the quantitative mineral analysis of geological samples CMS Workshop Lectures, Volume 1, Quantitative Mineral Analysis of Clays Evergreen, Colorado The Clay Minerals Society 154166.Google Scholar
Chipera, S.J. and Bish, D.L., 2002 FULLPAT: A full-pattern quantitative analysis program for X-ray powder diffraction using measured and calculated patterns Journal of Applied Crystallography 35 744749.CrossRefGoogle Scholar
Chung, F.H., 1974 Quantitative interpretation of X-ray diffraction patterns of mixtures I. Matrix-flushing method for quantitative multicomponent analysis. Journal of Applied Crystallography 7 519525.Google Scholar
Eberl, D.D., 2003.User’s guide to Rockjock a program for determining quantitative mineralogy from powder X-ray diffraction data US Geological SurveyCrossRefGoogle Scholar
Harvey, C.C. and Lagaly, G., 2013 Industrial applications Handbook of Clay Science 5 451490.CrossRefGoogle Scholar
Hillier, S., 2003 Quantitative analysis of clay and other minerals in sandstones by X-ray powder diffraction (XRPD) Clay Mineral Cements in Sandstones 34 213251.Google Scholar
Hubbard, C.R. and Snyder, R.L., 1988 RIR — Measurement and use in quantitative XRD Powder Diffraction 3 2 7477.CrossRefGoogle Scholar
Hughes, H. and Hurley, P.W., 1987 Precision and accuracy of test methods and the concept of K-factors in chemical analysis Analyst 112 14451449.CrossRefGoogle Scholar
ISO, 1994 Accuracy (trueness and precision) of measurement methods and results. Part 2: Basic method for the determination of repeatability and reproducibility of a standard measurement method ISO Standard 5725-2:1994(E) .Google Scholar
Kleeberg, R., 2005 Results of the second Reynolds Cup contest in quantitative mineral analysis IUCr. CPD Newsletter 30 2226.Google Scholar
Larson, A.C. and Von Dreele, R.B., 2000.General Structure Analysis System (GSAS) Los Alamos National LaboratoryGoogle Scholar
Lutterotti, L. Matthies, S. and Wenk, H.-R., 1999 MAUD: a friendly Java program for material analysis using diffraction IUCr CPD Newsletter 21 1415.Google Scholar
Madsen, I.C. Scarlett, N.V.Y. Cranswick, L.M.D. and Lwin, T., 2001 Outcomes of the International Union of Crystallography Commission on Powder Diffraction round robin on quantitative phase analysis: samples 1a to 1h Journal of Applied Crystallography 34 409426.CrossRefGoogle Scholar
McCarty, D.K., 2002 Quantitative mineral analysis of claybearing mixtures: The “Reynolds Cup” contest IUCr CPD Newsletter 27 1216.Google Scholar
Moore, D.M. and Reynolds, R.C., 1997 X-ray Diffraction and the Identification and Analysis of Clay Minerals Oxford Oxford University Press.Google Scholar
Mystkowski, K. Środoń, J. and McCarty, D.K., 2002 Application of evolutionary programming to automatic XRD quantitative analysis of clay-bearing rocks The Clay Minerals Society 39th Annual Meeting Colorado Boulder.Google Scholar
Omotoso, O. McCarty, D.K. Hillier, S. and Kleeberg, R., 2006 Some successful approaches to quantitative mineral analysis as revealed by the 3rd Reynolds Cup contest Clays and Clay Minerals 54 751763.CrossRefGoogle Scholar
Ottner, F. Gier, S. Kuderna, M. and Schwaighofer, B., 2000 Results of inter-laboratory comparison of methods for quantitative clay analysis Applied Clay Science 17 223243.CrossRefGoogle Scholar
Rancort, D.G. and Dang, M., 2005 Absolute quantification by powder X-ray diffraction of complex mixtures of crystalline and amorphous phases for applications in the Earth sciences American Mineralogist 90 15711586.CrossRefGoogle Scholar
Raven, M.D. and Self, P.G., 2012 Outcomes of the 2012 Reynolds Cup quantitative mineralogy competition The Clay Minerals Society 49th Annual Meeting Colorado Golden.Google Scholar
Reynolds, R.C., 1983 Calculation of absolute diffraction intensities for mixed-layered clays Clays and Clay Minerals 31 233234.CrossRefGoogle Scholar
Rodriguez-Carvajal, J. and Roisnel, T., 1998.FullProf.98 and WinPLOTR: new windows 95/NT applications for diffraction IUCr CPD Newsletter 20Google Scholar
Scarlett, N.V.Y. Madsen, I.C. Cranswick, L.M.D. Lwin, T. Groleau, E. Stephenson, G. Alymore, M. and Agron-Olshina, N., 2002 Outcomes of the International Union of Crystallography Commission on Powder Diffraction round robin on quantitative phase analysis: samples 2, 3, 4, synthetic bauxite, natural granodiorite and pharmaceuticals Journal of Applied Crystallography 35 383400.CrossRefGoogle Scholar
Schwertmann, U. and Cornell, R.M., 2000 Iron Oxides in the Laboratory: Preparation and Characterization.CrossRefGoogle Scholar
Środoń, J. Drits, V.A. McCarty, D.K. Hsieh, J.C.C. and Eberl, D.D., 2001 Quantitative XRD analysis of clay-rich rocks from random preparations Clays and Clay Minerals 49 514528.CrossRefGoogle Scholar
Zevin, L.S. and Kimmel, G. (1995) Quantitative X-ray Diffractometry (Mureinik, I., editor). Springer-Verlag, New York.CrossRefGoogle Scholar