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Using DTSA-II to Simulate and Interpret Energy Dispersive Spectra from Particles

Published online by Cambridge University Press:  20 April 2010

Nicholas W.M. Ritchie
Nicholas W.M. Ritchie
Affiliation:
Surface and Microanalysis Science Division, Chemical Science and Technology Laboratory, National Institute of Standards and Technology, 100 Bureau Drive, MS: 8371, Gaithersburg, MD 20899-8371, USA
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Abstract

A high quality X-ray spectrum image of a 3.3 μm diameter sphere of K411 glass resting on a copper substrate was collected at 25 keV. The same sample configuration was modeled using the NISTMonte Monte Carlo simulation of electron and X-ray transport as is integrated into the quantitative X-ray microanalysis software package DTSA-II. The distribution of measured and simulated X-ray intensity compare favorably for all the major lines present in the spectra. The simulation is further examined to investigate the influence of angle-of-incidence, sample thickness, and sample diameter on the generated and measured X-ray intensity. The distribution of generated X-rays is seen to deviate significantly from a naive model which assumes that the distribution of generated X-rays is similar to bulk within the volume they share in common. It is demonstrated that the angle at which the electron beam strikes the sample has nonnegligible consequences. It is also demonstrated that within the volume that the bulk and particle share in common that electrons, which have exited and later reentered the particle volume, generate a significant fraction of the X-rays. Any general model of X-ray generation in particles must take into account the lateral spread of the scattered electron beam.

Keywords

microanalysisparticleMonte Carlosimulationspectrum imagingX-ray
Type
Instrumentation and Software: Development and Applications
Information
Microscopy and Microanalysis , Volume 16 , Issue 3 , June 2010 , pp. 248 - 258
Copyright
Copyright © Microscopy Society of America 2010

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References

REFERENCES

Armstrong, J.T. (1991). Quantitative elemental analysis of individual microparticles with electron beam instruments. In Electron Probe Quantification, Heinrich, K.J.F. & Newbury, D.E. (Eds.), pp. 261–315. New York: Plenum Press.Google Scholar
Chantler, C.T., Olsen, K., Dragoset, R.A., Chang, J., Kishore, A.R., Kotochigova, S.A. & Zucker, D.S. (2005). X-ray form factor, attenuation and scattering tables (version 2.1). Available at http://physics.nist.gov/ffast (March 29, 2010). Gaithersburg, MD: National Institute of Standards and Technology. Originally published as Chantler, C.T. (1995). J Phys Chem Ref Data 29(4), 597–1048; and Chantler, C.T. (1995). J Phys Chem Ref Data 24, 71–643.Google Scholar
Kanaya, K. & Okayama, S. (1972). Penetration and energy-loss theory of electrons in solid targets. J Phys D 5, 43–58.Google Scholar
Ritchie, N.W.M. (2005). A new Monte Carlo application for complex sample geometries. Surf Interface Anal 37, 1006–1011.Google Scholar
Ritchie, N.W.M. (2009). Spectrum simulation in DTSA-II. Microc Microanal 15, 454–468.CrossRefGoogle ScholarPubMed
Ro, C.-U., Osán, J., Szalóki, I., de Hoog, J., Worobiec, A. & Van Grieken, R. (2003). An expert system for chemical speciation of individual particles using low-Z particle electron probe X-ray microanalysis data. Anal Chem 75, 851–859.Google Scholar