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A New Analysis Principle for EDXRF: The Monte Carlo - Library Least-Squares Analysis Principle

Published online by Cambridge University Press:  06 March 2019

K. Verghese ,
M. Mickael ,
T. He  and
R.P. Gardner
K. Verghese
Affiliation:
Center for Engineering Applications of Radioisotopes Box 7909, North Carolina State University Raleigh, North Carolina 27695-7909
M. Mickael
Affiliation:
Center for Engineering Applications of Radioisotopes Box 7909, North Carolina State University Raleigh, North Carolina 27695-7909
R.P. Gardner
Affiliation:
Center for Engineering Applications of Radioisotopes Box 7909, North Carolina State University Raleigh, North Carolina 27695-7909
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Abstract

A new analysis principle for energy-dispersive X–ray fluorescence has been identified and investigated as to feasibility. It consists of: (1) generating the complete spectral response for a sample of known (assumed) composition by Monte Carlo simulation,(2) keeping track of the individual elemental responses within the Monte Carlo simulation for use as library spectra, (3) use of the library least–squares (linear) analysis method to obtain the elemental amounts for any unknown sample spectrum, and (4) iterating these steps if the unknown amounts are too fax from the assumed composition originally used.

This principle has been investigated for a radioisotope source excited EDXRF system consisting of a 109Cd source and a Si(Li) detector for a Cu-Ni alloy sample (CDA Alloy 715) and a stainless steel sample (304 Stainless Steel) and found to give excellent results. This analysis principle makes unique use of the Monte Carlo “forward” simulation method to provide the elemental library spectra for use in the library least-squares method of analysis.

Type
VII. XRF Techniques, Instrumentation and Mathematical Models
Information
Advances in X-Ray Analysis , Volume 31: Thirty-Sixth Annual Conference on Applications of X-ray Analysis, August 3-7, 1987 , 1987 , pp. 461 - 469
Copyright
Copyright © International Centre for Diffraction Data 1987

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References

• Arinc, F., Gardner, R.P., Wielopolski, L., and Stiles, A.R., 1975, Application of the least-squares method to the analysis of X R F spectral intensities from atmospheric particulates collected on filters, Advances in X-Ray Analysis, 19. 367–379.Google Scholar
• Arinc, F., Wielopolski, L., and Gardner, R.P., 1977, Chapter 20, Linear leastsquares analysis of X-ray fluorescence spectra of aerosol samples using pure element library standards and photon excitation, in: “X-Ray Fluorescence Analysis of Environmental Samples”, Dzubay, T.G., ed., Ann Arbor Science Publishers, Inc., Ann Arbor, Michigan.Google Scholar
• and, Doster, J.M. Gardner, R.P., 1982a, The complete spectral response for EDXRF systems - calculation by Monte Carlo and analysis applications. 1 Homogeneous samples, X-Ray Spectromtry, Al 1(4), 173–180.Google Scholar
• Ibid., 1982b, 2 Heterogeneous samples, 181-186,Google Scholar
• Gardner, R.P. and Hawthorne, A.R., 1975, Monte Carlo simulation of the Xray fluorescence excited by discrete energy photons in homogeneous samples including tertiary inter-element effects, X-Ray Spectromtry, 4, 138–148.Google Scholar
• Gardner, R.P., Choi, H.K., Mickael, M., Yacout, A.M., Jin, Y, and Verghese, K., 1987, Algorithms for forcing scattered radiation to spherical, planar circular, and right circular cylindrical detectors for Monte Carlo simulation, Nuclear Science and Engineering. 95. 245–256.Google Scholar
• Gardner, R.P., Mickael, M., and Verghese, K., 1988, A new directional biasing approach for Monte Carlo simulation, accepted for publication by Nuclear Science and Engineering.Google Scholar
• Hawthorne, A.R. and Gardner, R.P., 1975, Monte Carlo simulation of X-ray fluorescence from homogeneous multielement samples excited by continuous and discrete energy photons from X-ray tubes, Analytical Chemistry, 47(13). 2220–2225.Google Scholar
• Heckel, J. and Scholz, W., 1987, Description of low-energy peak distortion in X-ray spectrometry with Si(Li) detectors, X-Ray Spectromtry, 16. 181–185.Google Scholar
• Mickael, M., Gardner, R.P., and Verghese, K., 1988, An improved method for calculating the expected value of particle scattering to finite detectors in Monte Carlo simulation, submitted to Nuclear Science and Engineering.Google Scholar
• Schamber, F.H., 1977, Chapter 21. A modification of the linear least-squares fitting method which provides continuum suppression, in: “X-Ray Fluorescence Analysis of Environmental Samples” , Dzubay, T.G., ed., Ann Arbor Science Publishers, Inc., Ann Arbor, Michigan.Google Scholar
• Yacout, A.M., Gardner, R.P., and Verghese, K., 1984, Cubic spline techniques for fitting X-ray cross sections, Nuclear Instruments and Methods in Physics Research, 220, 461–472.Google Scholar
• Yacout, A.M., Verghese, K., and Gardner, R.P., 1986, Cubic spline representation of the two-variable cumulative distribution functions for coherent and incoherent X-ray scattering, X-Ray Spectrom.try, 15, 259–265,Google Scholar
• Yacout, A.M., Gardner, R.P., and Verghese, K., 1987, Monte Carlo simulation of the X-ray fluorescence spectra from multielement homogeneous and heterogeneous samples, accepted for publication in Advances in X-Ray Analysis.Google Scholar