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Atom Probe Tomography Interlaboratory Study on Clustering analysis in experimental data using the maximum separation distance approach

Abstract : We summarize the findings from a interlaboratory study conducted between ten international research groups and investigate the use of the commonly used maximum separation distance and local concentration thresholding methods for solute clustering quantification. The study objectives are to bring clarity on the range of applicability of the methods, existing and/or needed modifications, and interpretation of past published data. Participants collected experimental data from a proton,irradiated 304 stainless steel and analyzed Cu,rich and Ni,Si rich clusters. The datasets were also analyzed by one researcher to clarify variability originating from different operators. The Cu distribution fulfils the ideal requirements of the maximum separation method, namely a dilute matrix Cu concentration and concentrated Cu clusters. This enabled a relatively tight distribution of the cluster number density among the participants. By contrast the group analysis of the Ni,Si rich clusters by the maximum separation method was complicated by a high Ni matrix concentration and by the presence of Si,decorated dislocations, leading to larger variability amongst researchers. While local concentration filtering could in principle tighten the results, the cluster identification step inevitably maintained a high scatter. Recommendations regarding reporting, selection of analysis method, and expected variability when interpreting published data are discussed.
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https://hal-cea.archives-ouvertes.fr/cea-02339926
Contributor : Bibliothèque Cadarache <>
Submitted on : Wednesday, October 30, 2019 - 3:44:39 PM
Last modification on : Tuesday, April 28, 2020 - 11:28:13 AM

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Ea. Marquis, A. Lopez, Y. Dong, A. Etienne, A. Frolov, et al.. Atom Probe Tomography Interlaboratory Study on Clustering analysis in experimental data using the maximum separation distance approach. Microscopy and Microanalysis, Cambridge University Press (CUP), 2018, ⟨10.1017/S1431927618015581⟩. ⟨cea-02339926⟩

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