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图案之外的粉末衍射数据：实用综述。

Powder diffraction data beyond the pattern: a practical review.

作者信息

Casati Nicola, Boldyreva Elena

机构信息

Paul Scherrer Institute 111 Forschungstrasse 5232Villigen Switzerland.

Boreskov Institute of Catalysis RAS, Lavrentiev Ave. 5, Novosibirsk 630090, Russian Federation.

出版信息

J Appl Crystallogr. 2025 Jul 22;58(Pt 4):1085-1105. doi: 10.1107/S1600576725004728. eCollection 2025 Aug 1.

DOI:10.1107/S1600576725004728
PMID:40765977
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12321027/
Abstract

We share personal experience in the fields of materials science and high-pressure research, discussing which parameters, in addition to positions of peak maxima and intensities, may be important to control and to document in order to make deposited powder diffraction data reusable, reproducible and replicable. We discuss, in particular, which data can be considered as 'raw' and some challenges of revisiting deposited powder diffraction data. We consider procedures such as identifying ('fingerprinting') a known phase in a sample, solving a bulk crystal structure from powder data, and analyzing the size of coherently scattering domains, lattice strain, the type of defects or preferred orientation of crystallites. The specific case of characterizing a multi-phase multi-grain sample following structural changes during mechanical treatment in a mill or on hydrostatic compression is also examined. We give examples of when revisiting old data adds a new knowledge and comment on the challenges of using deposited data for machine learning.

摘要

我们分享了材料科学和高压研究领域的个人经验，讨论了除峰最大值位置和强度外，为使沉积粉末衍射数据可重复使用、可再现和可复制，哪些参数对于控制和记录可能很重要。我们特别讨论了哪些数据可被视为“原始”数据，以及重新审视沉积粉末衍射数据所面临的一些挑战。我们考虑了诸如在样品中识别（“指纹识别”）已知相、从粉末数据解析体晶体结构，以及分析相干散射域的大小、晶格应变、缺陷类型或微晶的择优取向等程序。还研究了在球磨机中进行机械处理或静水压缩过程中结构变化后表征多相多晶粒样品的具体情况。我们给出了重新审视旧数据能带来新知识的示例，并对将沉积数据用于机器学习的挑战发表了评论。

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