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Multivariate statistical analysis of micro-X-ray fluorescence spectral images

Published online by Cambridge University Press:  15 June 2012

Mark A. Rodriguez*
Affiliation:
Sandia National Laboratories, Albuquerque, New Mexico 87185-1411
Paul G. Kotula
Affiliation:
Sandia National Laboratories, Albuquerque, New Mexico 87185-1411
James J. M. Griego
Affiliation:
Sandia National Laboratories, Albuquerque, New Mexico 87185-1411
Jason E. Heath
Affiliation:
Sandia National Laboratories, Albuquerque, New Mexico 87185-1411
Stephen J. Bauer
Affiliation:
Sandia National Laboratories, Albuquerque, New Mexico 87185-1411
Daniel E. Wesolowski
Affiliation:
Sandia National Laboratories, Albuquerque, New Mexico 87185-1411
*
a)Author to whom correspondence should be addressed. Electronic mail: marodri@sandia.gov

Abstract

Multivariate statistical analysis (MSA) is applied to the extraction of chemically relevant signals acquired with a micro-X-ray fluorescence (μ-XRF) mapping (full-spectral imaging) system. The separation of components into individual histograms enables separation of overlapping peaks, which is useful in qualitatively determining the presence of chemical species that have overlapping emission lines, and holds potential for quantitative analysis of constituent phases via these same histograms. The usefulness of MSA for μ-XRF analysis is demonstrated by application to a geological rock core obtained from a subsurface compressed air energy storage (CAES) site. Coupling of the μ-XRF results to those of quantitative powder X-ray diffraction analysis enables improved detection of trace phases present in the geological specimen. The MSA indicates that the spatial distribution of pyrite, a potentially reactive phase by oxidation, has low concentration and thus minimal impact on CAES operations.

Type
Technical Articles
Copyright
Copyright © International Centre for Diffraction Data 2012

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