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Quantifying X-Ray Fluorescence Data Using MAPS.

Authors
  • Nietzold, Tara1
  • West, Bradley M2
  • Stuckelberger, Michael2
  • Lai, Barry3
  • Vogt, Stefan3
  • Bertoni, Mariana I4
  • 1 School for Engineering of Matter, Transport, and Energy, Arizona State University.
  • 2 School of Electrical, Computer, and Energy Engineering, Arizona State University.
  • 3 Advanced Photon Source, Argonne National Laboratory.
  • 4 School of Electrical, Computer, and Energy Engineering, Arizona State University; [email protected]
Type
Published Article
Journal
Journal of Visualized Experiments
Publisher
MyJoVE Corporation
Publication Date
Feb 17, 2018
Issue
132
Identifiers
DOI: 10.3791/56042
PMID: 29553551
Source
Medline
Language
English
License
Unknown

Abstract

The quantification of X-ray fluorescence (XRF) microscopy maps by fitting the raw spectra to a known standard is crucial for evaluating chemical composition and elemental distribution within a material. Synchrotron-based XRF has become an integral characterization technique for a variety of research topics, particularly due to its non-destructive nature and its high sensitivity. Today, synchrotrons can acquire fluorescence data at spatial resolutions well below a micron, allowing for the evaluation of compositional variations at the nanoscale. Through proper quantification, it is then possible to obtain an in-depth, high-resolution understanding of elemental segregation, stoichiometric relationships, and clustering behavior. This article explains how to use the MAPS fitting software developed by Argonne National Laboratory for the quantification of full 2-D XRF maps. We use as an example results from a Cu(In,Ga)Se2 solar cell, taken at the Advanced Photon Source beamline 2-ID-D at Argonne National Laboratory. We show the standard procedure for fitting raw data, demonstrate how to evaluate the quality of a fit and present the typical outputs generated by the program. In addition, we discuss in this manuscript certain software limitations and offer suggestions for how to further correct the data to be numerically accurate and representative of spatially resolved, elemental concentrations.

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