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.

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基于MCNP6仿真代码的反向蒙特卡洛迭代算法定量X射线荧光分析

反向蒙特卡洛迭代算法已经开发出来，用于定量能量色散X射线荧光分析，以便计算固体物质中基本成分的浓度。模拟代码的核心是MCNP6，它是在核研究和实践中用于核系统模拟或伽马射线或X射线光谱法全过程的成熟且广泛应用的软件包。通过对参考合金样品进行定量XRF分析，测试了反向蒙特卡洛算法和完整的分析程序。参考样品的原子组成通过反向蒙特卡洛技术以及基本参数方法和火花发射原子光谱法确定。在样品主要元素的标准偏差内发现了这三种分析方法的结果之间的一致性。使用MCNP6中提供的方差减少程序，将反向蒙特卡洛数值计算的总持续时间最小化到几分钟。