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Development of a Monte Carlo—Library Least-Squares code package for the EDXRF inverse problem

Published online by Cambridge University Press:  01 March 2012

Robin P. Gardner*
Affiliation:
Center for Engineering Applications of Radioisotopes (CEAR), Nuclear Engineering Department, PO Box 7909, North Carolina State University, Raleigh, North Carolina 27695
Weijun Guo
Affiliation:
Center for Engineering Applications of Radioisotopes (CEAR), Nuclear Engineering Department, PO Box 7909, North Carolina State University, Raleigh, North Carolina 27695
*
a)Electronic mail: gardner@ncsu.edu

Abstract

The Monte Carlo—Library Least-Squares (MCLLS) approach has now been developed, implemented, and tested for solving the inverse problem of EDXRF sample analysis. It consists of a linear library least-squares code and a comprehensive Monte Carlo code named CEARXRF that is capable of calculating the unknown sample spectrum, all the elemental library spectra in the sample, and the differential operators for each library spectrum with respect to each element. Two codes with graphical user interface have been designed to implement the MCLLS approach and benchmark results are presented for the two stainless steel samples; SS304 and SS316. The results are accurate, the system is easy to use, and all indications are that this approach will be very useful for the EDXRF practitioner.

Type
XRD Instrumentation and Techniques
Copyright
Copyright © Cambridge University Press 2005

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References

Ao, Q., Lee, S. H., and Gardner, R. P. (1997). “Development of the specific purpose Monte Carlo code CEARXRF for the design and use of in vivo X-ray fluorescence analysis systems for lead in bone,” Appl. Radiat. Isot. ARISEF 48, 14031412.CrossRefGoogle ScholarPubMed
Arinc, F., Wielopolski, L., and Gardner, R. P. (1977). “The linear least-squares 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, edited by Dzubay, T. G. (Ann Arbor Science, Raleigh), pp. 227240.Google Scholar
Bearden, J. A. and Burr, A. F. (1967). “Reevaluation of X-ray atomic energy levels,” Rev. Mod. Phys. RMPHAT 10.1103/RevModPhys.39.125 39, 125142.CrossRefGoogle Scholar
Biggs, F., Mendelsohn, L. B., and Mann, J. B. (1975). “Hartree-Fock Compton profiles for the elements,” At. Data Nucl. Data Tables ADNDAT 10.1016/0092-640X(75)90030-3 16, 201310.CrossRefGoogle Scholar
Gardner, R. P. et al. (1986). “An investigation of the possible interaction mechanisms for Si(Li) and Ge detector response functions by Monte Carlo simulation,” Nucl. Instrum. Methods Phys. Res. A NIMAER 242, 399405.CrossRefGoogle Scholar
Gardner, R. P., Guo, W., and Li, F. (2004). “A Monte Carlo code for simulation of pulse pile-up spectral distortion in pulse-height measurement,” 53rd Denver X-ray Conference.CrossRefGoogle Scholar
Guo, W. (2003). “Improving the MCLLS method applied to the in vivo XRF measurement of lead in bone by using the differential operator approach (MCDOLLS) and X-ray coincidence spectroscopy,” in Nuclear Engineering Department. North Carolina State University, Raleigh, p. 100.Google Scholar
Guo, W., Gardner, R. P., and Metwally, W. A. (2004). “Preliminary studies on K and L coincidence spectroscopy for optimizing the in vivo XRF measurement of lead in bone,” Nucl. Instrum. Methods Phys. Res. B NIMBEU 213, 574578.CrossRefGoogle Scholar
Hall, M. C. G. (1982). “Cross-section adjustment with Monte Carlo sensitivities: Application to the Winfrith iron benchmark,” Nucl. Sci. Eng. NSENAO 81, 423431.CrossRefGoogle Scholar
He, T., Gardner, R. P., and Verghese, K. (1991). “NCSXRF: A general geometry Monte Carlo simulation for EDXRF analysis,” Adv. X-Ray Anal. AXRAAA 35B, 727736.Google Scholar
He, T., Gardner, R. P., and Verghese, K. (1990). “An improved Si(Li) detector response function,” Nucl. Instrum. Methods Phys. Res. A NIMAER 299, 354366.CrossRefGoogle Scholar
Hubbell, J. H. (1975). “Atomic form factors, incoherent scattering functions, and photon scattering cross sections,” J. Phys. Chem. Ref. Data JPCRBU 10.1063/1.555523 4, 471538.CrossRefGoogle Scholar
Jin, Y., Gardner, R. P., and Verghese, K. (1986). “A semi-empirical model for the gamma-ray response function of germanium detectors based on fundamental interaction mechanisms,” Nucl. Instrum. Methods Phys. Res. A NIMAER 242, 416426.CrossRefGoogle Scholar
Krause, M. O. (1979). “Atomic radiative and radiationless yields for K and L shells,” J. Phys. Chem. Ref. Data JPCRBU 10.1063/1.555594 8, 307327.CrossRefGoogle Scholar
Lee, M. C., Verghese, K., and Gardner, R. P. (1987). “Extension of the semiempirical germanium detector response function to low energy gamma rays,” Nucl. Instrum. Methods Phys. Res. A NIMAER 262, 430438.CrossRefGoogle Scholar
Lee, S. H. (1999). “Use of differential operators in the Monte Carlo—Library Least-Squares Method for X-ray Fluroescence Applications,” NCSU, Raleigh.Google Scholar
Lee, S. H., Gardner, R. P., and Todd, A. C. (2001). “Preliminary studies on combining the K and L XRF methods for in vivo bone lead measurement,” Appl. Radiat. Isot. ARISEF 54, 893904.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,” Nucl. Instrum. Methods Phys. Res. A NIMAER 299, 516523.CrossRefGoogle Scholar
Rief, H. (1984). “Generalized Monte Carlo perturbation algorithms for correlated sampling and a second-order Taylor series approach,” Ann. Nucl. Energy ANENDJ 11, 455476.CrossRefGoogle Scholar
Rief, H. (1994). “A synopsis of Monte Carlo perturbation algorithms,” J. Comput. Phys. JCTPAH 10.1006/jcph.1994.1041 111, 3348.CrossRefGoogle Scholar
Rose, P. F. (1991). ENDF/B-VI Summary Documentation (National Nuclear Data Center, Upton, NY).Google Scholar
Salmon, L. (1961). “Analysis of gamma-ray scintillation spectra by the method of least-squares,” Nucl. Instrum. Methods NUIMAL 14, 193.CrossRefGoogle Scholar
Scofield, J. H. (1974a). “Relativistic Hartree-Slater values for K and L X-ray emission rates,” At. Data Nucl. Data Tables ADNDAT 14, 121137.CrossRefGoogle Scholar
Scofield, J. H. (1974b). “Hartree-Fock values of L X-ray emission rates,” Phys. Rev. A PLRAAN 10.1103/PhysRevA.10.1507 10, 15071510.CrossRefGoogle Scholar
Storm, E. and Israel, I. H. (1967). “Photon cross sections from 0.001 to 100 MeV for elements 1 through 100,” Los Alamos National Laboratory.Google Scholar