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Dong, Y.; Etienne, A.; Frolov, A.; Fedotova, S.; Fujii, K.; Fukuya, K.; Hatzoglou, C.; Kuleshova, E.; Lindgren, K.; London, A.; Lopez, A.; Lozano-Perez, S.; Miyahara, Y.; Nagai, Y.; Nishida, K.; Radiguet, B.; Schreiber, D.K.; Soneda, N.; Thuvander, M.; Toyama, T.; Wang, J.; Sefta, F.; Chou, P.; Marquis, E.A. |
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Publication |
Atom Probe Tomography Interlaboratory Study on Clustering Analysis in Experimental Data Using the Maximum Separation Distance Approach |
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Journal Article |
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2019 |
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Microscopy and Microanalysis |
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25 |
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2 |
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Summary Language |
356-366 |
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atom probe tomography; cluster analysis; maximum separation |
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Abbreviated Series Title |
We summarize the findings from an 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 to the range of applicability of the methods; identify 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 fulfills the ideal requirements of the maximum separation method (MSM), 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 MSM was complicated by a high Ni matrix concentration and by the presence of Si-decorated dislocations, leading to larger variability among 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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Cambridge University Press |
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2019/02/04 |
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1431-9276 |
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NU @ karnesky @ |
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11537 |
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