Reverse Monte Carlo iterative algorithm for quantification of X-ray fluorescence analysis based on MCNP6 simulation code

Abstract

Reverse Monte Carlo iterative algorithm has been developed for quantification of energy-dispersive X-ray fluorescence analysis in order to calculate the concentrations of the elementary composition in solid substances. The core of the simulation code was the MCNP6 that is a well-established and widely applied software package in the nuclear research and practice for simulation of nuclear systems or the full process of gamma- or X-ray spectrometry. The reverse Monte Carlo algorithm and the full analytical procedure was tested by quantitative XRF analysis of reference alloy samples. The atomic compositions of the reference samples were determined by reverse Monte Carlo technique and also fundamental parameter method and by spark emission atomic spectroscopy. The agreement between the results of these three analytical methods was found within the standard deviations of the major elements of the samples. The total duration of the reverse Monte Carlo numerical computation was minimized to a few minutes using the variance reduction procedures available in the MCNP6.