Obtaining parallax-free X-ray powder diffraction computed tomography data with a self-supervised neural network

Finden’s latest work with UCL Chemistry, Deutsches Elektronen-Synchrotron DESY, Dyson School of Design Engineering at Imperial College London and Science and Technology Facilities Council (STFC) has been published in a new paper by Hongyang Dong “Obtaining parallax-free X-ray powder diffraction computed tomography data with a self-supervised neural network” in npj Computational  Materials.  This study introduces a method designed to eliminate parallax artefacts present in X-ray powder diffraction computed tomography data acquired from large samples. These parallax artefacts manifest 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.