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NCSXRF: A General Geometry Monte Carlo Simulation Code for EDXRF Analysis

Published online by Cambridge University Press:  06 March 2019

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

EDXRF analysis is conveniently split into two parts: (1) the determination of X-ray intensities and (2) the determination of elemental amounts from X-ray intensities. For the first, most EDXRF analysis has been done by some method of integrating the essentially Gaussian distribution of observed full energy pulse heights. This might be done, for example, by least-square fitting of Gaussian distributions superimposed on a straight line or a quadratic background. Recently more elaborate shapes of the energy peaks also have been considered (Kennedy, 1990). After the X-ray intensities have been determined, interelement effects between the analyte element and other elements must be corrected for in order to obtain the elemental amounts from X-ray intensities. This correction can be done either by an empirical correction procedure as in the influence coefficient method which requires measurements on a number of standard samples to determine the required coefficients, or by theoretical calculation as in the fundamental parameters method which does not require standard samples.

Type
X. Mathematical Methods in X-Ray Spectrometry (XRS)
Information
Advances in X-Ray Analysis , Volume 35 , Issue B: First Pacific-International Congress on X-ray Analytical Methods (PICXAM). Fortieth Annual Conference on Applications of X-ray Analysis, August 7-16, 1991 , 1991 , pp. 727 - 736
Copyright
Copyright © International Centre for Diffraction Data 1991

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References

• Biggs, F., Mendelsohn, L. B., and Mann, J. B., 1975, “Hartree-Fock Compton Profiles for the Elements”, Atomic Data and Nuclear Data Tables, 16, 201.Google Scholar
• Doster, J. M. and Gardner, R. P., 1982 “The Complete Spectral Response for EDXRF Systems - Calculation by Monte Carlo and Analysis Applications. 1, Homogeneous Samples”, X-Ray Spectrometry, A11(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 X-Ray Fluorescence Excited by Discrete Energy Photons in Homogeneous Samples Including Tertiary Inter-Element Effects”, X-Ray Spectrometry, 4, 138–148.Google Scholar
• Gardner, R. P. and Wielopolski, L., 1977a, “A Generalized Method for Correcting Pulse-Height Spectra for the Peak Pile-Up Effect Due to Double Sum Pulses. Fart I. Predicting Spectral Distortion for Arbitrary Pulse Shapes”, Nuclear Instruments and Methods, 140, 289–296.Google Scholar
•Ibid., 1977b, “Part II. The Inverse Calculation for Obtaining True from Observed Spectra”, 297-303. Google Scholar
• Gardner, R. P., Mickael, M. W., and Verghese, K., 1989, “Complete Composition and Density Correlated Sampling in the Specific Purpose Monte Carlo Codes McPNL and McDNL for Simulating Pulsed Neutron and Neutron Porosity Logging Tools”, Nuclear Geophysics, Vol. 3, No. 3, pp. 157–165.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
• He, T., Gardner, R. P., and Verghese, K., 1990, “An Improved Si(Li) Detector Response Function”, Nuclear Instruments and Methods in Physics Research A299, 354–366.Google Scholar
• He, T., Dobbs, C. L., Verghese, K. and Gardner, R. P., 1991, “Investigation of Energy- Dispersive X-Ray Fluorescence Analysis for On-Line Aluminum Thickness/Composition Measurement”, Transactions of the American Nuclear Society, Vol. 63, 147–148.Google Scholar
• Hubbell, J. H., Veigele, W. J., Briggs, E. A., Brown, R. T., Cromer, D. T., and Howerton, R. J., 1975, “Atomic Form Factors, Incoherent Scattering Functions, and Photon Scattering Cross Sections”, J. Phys. Chem. Ref. Data, 4, 471.Google Scholar
• Kennedy, G., 1990, “Comparison of Photopeak Integration Methods”, Nuclear Instruments and Methods in Physics Research A299, 342–349.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”, Nuclear Science and Engineering, 99, 251–266.Google Scholar
• Prettyman, T. H., Gardner, R. P., and Verghese, K., 1990, “MCPT: A Monte Carlo Code for Simulation of Photon Transport in Tomographic Scanners”, Nuclear Instruments and Methods in Physics Research A299, 516–523.Google Scholar
• Scofield, J. H., 1975, Atomic Inner-Shell Processes, Academic Press Inc., 265–288.Google Scholar
• Veigele, W. J., 1973, Atomic Data Tables, 5, 51–111.Google Scholar
• Verghese, K., Mickael, M., He, T., and Gardner, R. P., “A New Analysis Principle for EDXRF: the Monte Carlo - Library Least-Squares Principle”, Advances in X-Ray Analysis, Vol. 31, pp. 461–469.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., Gardner, R. P., and Verghese, K., 1986, “Cubic Spline Representation of the Two-Variable Cumulative Distribution Functions for Coherent and Incoherent X-Ray Scattering”, X-Ray Spectrometry, 15, 259–265.Google Scholar
• Yacout, A. M., Gardner, R. P., and Verghese, K., 1987, “Monte Carlo Simulation of the XRay Fluorescence Spectra from Multielement Homogeneous and Heterogeneous Samples”, Advances in X-Ray Analysis, 30, 121–132.Google Scholar
• Williams, B., 1977, Compton Scattering, McGraw-Hill, New York Google Scholar