Multivariate versus traditional quantitative phase analysis of X-ray powder diffraction and fluorescence data of mixtures showing preferred orientation and microabsorption.
Rietveld refinement
X-ray fluorescence
X-ray powder diffraction
microabsorption
multivariate analysis
preferred orientation
principal component analysis
quantitative phase analysis
Journal
Journal of applied crystallography
ISSN: 0021-8898
Titre abrégé: J Appl Crystallogr
Pays: United States
ID NLM: 9876190
Informations de publication
Date de publication:
01 Aug 2022
01 Aug 2022
Historique:
received:
15
12
2021
accepted:
03
05
2022
entrez:
17
8
2022
pubmed:
18
8
2022
medline:
18
8
2022
Statut:
epublish
Résumé
In materials and earth science, but also in chemistry, pharmaceutics and engineering, the quantification of elements and crystal phases in solid samples is often essential for a full characterization of materials. The most frequently used techniques for this purpose are X-ray fluorescence (XRF) for elemental analysis and X-ray powder diffraction (XRPD) for phase analysis. In both methods, relations between signal and quantity do exist but they are expressed in terms of complex equations including many parameters related to both sample and instruments, and the dependence on the active element or phase amounts to be determined is convoluted among those parameters. Often real-life samples hold relations not suitable for a direct quantification and, therefore, estimations based only on the values of the relative intensities are affected by large errors. Preferred orientation (PO) and microabsorption (MA) in XRPD cannot usually be avoided, and traditional corrections in Rietveld refinement, such as the Brindley MA correction, are not able, in general, to restore the correct phase quantification. In this work, a multivariate approach, where principal component analysis is exploited alone or combined with regression methods, is used on XRPD profiles collected on
Identifiants
pubmed: 35974739
doi: 10.1107/S1600576722004708
pii: S1600576722004708
pmc: PMC9348868
doi:
Types de publication
Journal Article
Langues
eng
Pagination
837-850Informations de copyright
© Mattia Lopresti et al. 2022.
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