Self-absorption correction on 2D X-ray fluorescence maps.


Journal

Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288

Informations de publication

Date de publication:
04 May 2023
Historique:
received: 29 01 2023
accepted: 12 04 2023
medline: 5 5 2023
pubmed: 5 5 2023
entrez: 4 5 2023
Statut: epublish

Résumé

X-ray fluorescence mapping (XRF) is a highly efficient and non-invasive technique for quantifying material composition with micro and nanoscale spatial resolutions. Quantitative XRF analysis, however, confronts challenges from the long-lasting problem called self-absorption. Moreover, correcting two-dimensional XRF mapping datasets is particularly difficult because it is an ill-posed inverse problem. Here we report a semi-empirical method that can effectively correct 2D XRF mapping data. The correction error is generally less than 10% from a comprehensive evaluation of the accuracy in various configurations. The proposed method was applied to quantify the composition distribution around the grain boundaries in an electrochemically corroded stainless steel sample. Highly localized Cr enrichment was found around the crack sites, which was invisible before the absorption correction.

Identifiants

pubmed: 37142634
doi: 10.1038/s41598-023-33383-w
pii: 10.1038/s41598-023-33383-w
pmc: PMC10160062
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

7271

Subventions

Organisme : U.S. Department of Energy
ID : DE-SC0012704
Organisme : U.S. Department of Energy,United States
ID : LDRD-24255

Informations de copyright

© 2023. The Author(s).

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Auteurs

Mingyuan Ge (M)

National Synchrotron Light Source II, Brookhaven National Laboratory, Upton, NY, USA.

Hanfei Yan (H)

National Synchrotron Light Source II, Brookhaven National Laboratory, Upton, NY, USA.

Xiaojing Huang (X)

National Synchrotron Light Source II, Brookhaven National Laboratory, Upton, NY, USA.

Yong S Chu (YS)

National Synchrotron Light Source II, Brookhaven National Laboratory, Upton, NY, USA. ychu@bnl.gov.

Classifications MeSH