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Powder diffraction data beyond the pattern: a practical review

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Názov: Powder diffraction data beyond the pattern: a practical review
Autori: Nicola Casati, Elena Boldyreva
Zdroj: Journal of Applied Crystallography. 58:1085-1105
Informácie o vydavateľovi: International Union of Crystallography (IUCr), 2025.
Rok vydania: 2025
Popis: 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 in situ 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.
Druh dokumentu: Article
ISSN: 1600-5767
DOI: 10.1107/s1600576725004728
Rights: CC BY
Prístupové číslo: edsair.doi...........2b5d3edfc384384ed0b565c2d6ce3238
Databáza: OpenAIRE
Popis
Abstrakt: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 in situ 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.
ISSN:16005767
DOI:10.1107/s1600576725004728