Obtaining parallax-free X-ray powder diffraction computed tomography data with a self-supervised neural network
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Author(s)
Type
Journal Article
Abstract
In this study, we introduce a method designed to eliminate parallax artefacts present in X-ray
17 powder diffraction computed tomography data acquired from large samples. These parallax
18 artefacts manifest as artificial peak shifting, broadening and splitting, leading to inaccurate
19 physicochemical information, such as lattice parameters and crystallite sizes. Our approach
20 integrates a 3D artificial neural network architecture with a forward projector that accounts for
21 the experimental geometry and sample thickness. It is a self-supervised tomographic volume
22 reconstruction approach designed to be chemistry-agnostic, eliminating the need for prior
23 knowledge of the sample's chemical composition. We showcase the efficacy of this method
24 through its application on both simulated and experimental X-ray powder diffraction tomography
25 data, acquired from a phantom sample and an NMC532 cylindrical Lithium-ion battery.
17 powder diffraction computed tomography data acquired from large samples. These parallax
18 artefacts manifest as artificial peak shifting, broadening and splitting, leading to inaccurate
19 physicochemical information, such as lattice parameters and crystallite sizes. Our approach
20 integrates a 3D artificial neural network architecture with a forward projector that accounts for
21 the experimental geometry and sample thickness. It is a self-supervised tomographic volume
22 reconstruction approach designed to be chemistry-agnostic, eliminating the need for prior
23 knowledge of the sample's chemical composition. We showcase the efficacy of this method
24 through its application on both simulated and experimental X-ray powder diffraction tomography
25 data, acquired from a phantom sample and an NMC532 cylindrical Lithium-ion battery.
Date Issued
2024-09-02
Date Acceptance
2024-08-20
Citation
npj Computational Materials, 2024, 10
ISSN
2057-3960
Publisher
Nature Portfolio
Journal / Book Title
npj Computational Materials
Volume
10
Copyright Statement
© The Author(s) 2024
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
License URL
Identifier
https://www.nature.com/articles/s41524-024-01389-1
Publication Status
Published
Article Number
201
Date Publish Online
2024-09-02