Recent developments in X-ray diffraction/scattering computed tomography for materials science

被引:17
|
作者
Omori, Naomi E. [1 ]
Bobitan, Antonia D. [1 ,2 ,3 ]
Vamvakeros, Antonis [1 ,4 ]
Beale, Andrew M. [1 ,2 ,3 ]
Jacques, Simon D. M. [1 ]
机构
[1] Finden Ltd, Merchant House,5 East St Helens St, Abingdon OX14 5EG, England
[2] UCL, Dept Chem, 20 Gordon St, London WC1H 0AJ, England
[3] Rutherford Appleton Lab, Res Complex Harwell, Harwell Sci & Innovat Campus, Didcot OX11 0FA, England
[4] Imperial Coll London, Dyson Sch Design Engn, London SW7 2DB, England
来源
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES | 2023年 / 381卷 / 2259期
基金
英国工程与自然科学研究理事会;
关键词
XRD-CT; tomography; X-ray; scattering; diffraction; chemical imaging; TECHNOLOGY CURRENT STATUS; SYNCHROTRON-RADIATION; CONTRAST TOMOGRAPHY; SMALL SAMPLES; IN-SITU; SCATTERING; CATALYSTS; REAL; RECONSTRUCTION; POLYCRYSTALS;
D O I
10.1098/rsta.2022.0350
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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)'.
引用
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页数:29
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