摘要
In this study,we introduce a method designed to eliminate parallax artefacts present in X-ray powder diffraction computed tomography data acquired from large samples.These parallax artefactsmanifest as artificial peak shifting,broadening and splitting,leading to inaccurate physicochemical information,such as lattice parameters and crystallite sizes.Our approach integrates a 3D artificial neural network architecture with a forward projector that accounts for the experimental geometry and sample thickness.It is a self-supervised tomographic volume reconstruction approach designed to be chemistry-agnostic,eliminating the need for prior knowledge of the sample’s chemical composition.We showcase the efficacy of this method through its application on both simulated and experimental X-ray powder diffraction tomography data,acquired from a phantom sample and an NMC532 cylindrical lithium-ion battery.
基金
funding through the Innovate UK Analysis for Innovators(A4i)programme(Project No.106003)
Parts of this research were carried out at PETRA III.A.V.acknowledges financial support from the Royal Society as a Royal Society Industry Fellow(IF\R2\222059).
