Inverse modeling for quantitative X-ray microanalysis applied to 2D heterogeneous materials

Yu Yuan; Hendrix Demers; Nicolas Brodusch; Xianglong Wang; Raynald Gauvin

doi:10.1016/j.ultramic.2020.113117

Inverse modeling for quantitative X-ray microanalysis applied to 2D heterogeneous materials

Ultramicroscopy. 2020 Dec:219:113117. doi: 10.1016/j.ultramic.2020.113117. Epub 2020 Sep 20.

Authors

Yu Yuan¹, Hendrix Demers², Nicolas Brodusch³, Xianglong Wang⁴, Raynald Gauvin⁵

Affiliations

¹ Department of Mining and Materials Engineering, McGill University, 3610 Rue University, Montreal, Québec, Canada, H3A 0C5. Electronic address: yu.yuan@mail.mcgill.ca.
² Centre d'excellence en électrification des transports et stockage d'énergie, Hydro- Québec, 1806 Boulevard Lionel-Boulet, Varennes, Québec, Canada, J3X 1S1. Electronic address: hendrix.demers@mail.mcgill.ca.
³ Department of Mining and Materials Engineering, McGill University, 3610 Rue University, Montreal, Québec, Canada, H3A 0C5. Electronic address: nicolas.brodusch@mcgill.ca.
⁴ Department of Mining and Materials Engineering, McGill University, 3610 Rue University, Montreal, Québec, Canada, H3A 0C5. Electronic address: xianglong.wang@mail.mcgill.ca.
⁵ Department of Mining and Materials Engineering, McGill University, 3610 Rue University, Montreal, Québec, Canada, H3A 0C5. Electronic address: raynald.gauvin@mcgill.ca.

PMID: 32987247
DOI: 10.1016/j.ultramic.2020.113117

Abstract

Current quantitative X-ray microanalysis methods are only available for homogeneous materials. This paper presents a newly developed inverse modeling algorithm to determine both the structure and composition of two-dimensional (2D) heterogeneous materials from a series of X-ray intensity measurements under different beam energies and beam positions. It utilizes an iterative process of forward modeling to determine the optimal specimen to minimize the relative differences between the simulated and experimental characteristic X-ray intensities. The Monte Carlo method is used for the forward modeling to predict the X-ray radiation for a given specimen and experimental setup. Several examples of applications are presented for different types of samples with one-dimensional (1D) and 2D structures, in which the simulated X-ray intensities from phantom samples are used as input. Most of the results obtained from our algorithm agree well with the phantom samples. Some discrepancies are found for the voxels located at deeper depths of the 2D samples. And the discrepancies may be attributed to errors from the Monte Carlo simulations and from the variation of the X-ray range with beam energy. As a proof-of-concept work, this paper confirms the feasibility of our inverse modeling algorithm applied to 2D heterogeneous materials.

Keywords: 2D heterogeneous materials; Inverse modeling; Monte Carlo simulation; Structure and composition determination; Voxel-based specimen; X-ray microanalysis.