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Powder diffraction data beyond the pattern: a practical review
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AI
DOI：10.1107/S1600576725004728.png)
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Abstract
En 中文
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.
Keywords:
powder diffraction
FAIR data
raw data
2D to 1D conversion
metadata
particle statistics
images
materials
minerals
high-pressure data
in situ mechanochemical studies
Journal
IF:
2.8
Papers:
274
Citations:
3.1W