Exploring the accuracy of isotopic analyses in atom probe mass spectrometry.

Atom probe Isotopic analysis Mass spectrometry Multi-hit detection events Peak fitting

Journal

Ultramicroscopy
ISSN: 1879-2723
Titre abrégé: Ultramicroscopy
Pays: Netherlands
ID NLM: 7513702

Informations de publication

Date de publication:
Sep 2020
Historique:
received: 09 01 2020
revised: 30 04 2020
accepted: 02 05 2020
pubmed: 12 6 2020
medline: 12 6 2020
entrez: 12 6 2020
Statut: ppublish

Résumé

Atom probe tomography (APT) can theoretically deliver accurate chemical and isotopic analyses at a high level of sensitivity, precision, and spatial resolution. However, empirical APT data often contain significant biases that lead to erroneous chemical concentration and isotopic abundance measurements. The present study explores the accuracy of quantitative isotopic analyses performed via atom probe mass spectrometry. A machine learning-based adaptive peak fitting algorithm was developed to provide a reproducible and mathematically defensible means to determine peak shapes and intensities in the mass spectrum for specific ion species. The isotopic abundance measurements made with the atom probe are compared directly with the known isotopic abundance values for each of the materials. Even in the presence of exceedingly high numbers of multi-hit detection events (up to 80%), and in the absence of any deadtime corrections, our approach produced isotopic abundance measurements having an accuracy consistent with values limited predominantly by counting statistics.

Identifiants

pubmed: 32526558
pii: S0304-3991(20)30005-X
doi: 10.1016/j.ultramic.2020.113018
pmc: PMC7717065
mid: NIHMS1642328
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

113018

Subventions

Organisme : Intramural NIST DOC
ID : 9999-NIST
Pays : United States

Informations de copyright

Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Auteurs

Frederick Meisenkothen (F)

Materials Measurement Science Division, National Institute of Standards and Technology, Gaithersburg, MD 20899 United States. Electronic address: frederick.meisenkothen@nist.gov.

Daniel V Samarov (DV)

Statistical Engineering Division, National Institute of Standards and Technology, Gaithersburg, MD 20899 United States.

Irina Kalish (I)

Materials Measurement Science Division, National Institute of Standards and Technology, Gaithersburg, MD 20899 United States; MATSYS, Inc., Sterling, VA 20164 United States.

Eric B Steel (EB)

Materials Measurement Science Division, National Institute of Standards and Technology, Gaithersburg, MD 20899 United States.

Classifications MeSH