Omori Naomi E, Bobitan Antonia D, Vamvakeros Antonis, Beale Andrew M, Jacques Simon D M
Finden Limited, Merchant House, 5 East St Helens Street,Abingdon OX14 5EG, UK.
Department of Chemistry, University College London, 20 Gordon Street, London WC1H 0AJ, UK.
Philos Trans A Math Phys Eng Sci. 2023 Oct 30;381(2259):20220350. doi: 10.1098/rsta.2022.0350. Epub 2023 Sep 11.
X-ray diffraction/scattering computed tomography (XDS-CT) methods are a non-destructive class of chemical imaging techniques that have the capacity to provide reconstructions of sample cross-sections with spatially resolved chemical information. While X-ray diffraction CT (XRD-CT) is the most well-established method, recent advances in instrumentation and data reconstruction have seen greater use of related techniques like small angle X-ray scattering CT and pair distribution function CT. Additionally, the adoption of machine learning techniques for tomographic reconstruction and data analysis are fundamentally disrupting how XDS-CT data is processed. The following narrative review highlights recent developments and applications of XDS-CT with a focus on studies in the last five years. This article is part of the theme issue 'Exploring the length scales, timescales and chemistry of challenging materials (Part 2)'.
X射线衍射/散射计算机断层扫描(XDS-CT)方法是一类非破坏性化学成像技术,能够提供具有空间分辨化学信息的样品横截面重建图像。虽然X射线衍射计算机断层扫描(XRD-CT)是最成熟的方法,但仪器和数据重建方面的最新进展使得小角X射线散射计算机断层扫描和对分布函数计算机断层扫描等相关技术得到了更广泛的应用。此外,将机器学习技术用于断层扫描重建和数据分析正在从根本上改变XDS-CT数据的处理方式。以下叙述性综述重点介绍了XDS-CT的最新发展和应用,尤其关注过去五年的研究。本文是“探索具有挑战性材料的长度尺度、时间尺度和化学性质(第2部分)”主题特刊的一部分。