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Atom Probe Tomography Interlaboratory Study on Clustering Analysis in Experimental Data Using the Maximum Separation Distance Approach

Published online by Cambridge University Press:  04 February 2019

Yan Dong
Affiliation:
Department of Materials Science and Engineering, University of Michigan, Ann Arbor, MI 48109, USA
Auriane Etienne
Affiliation:
Normandie Univ, UNIROUEN, INSA Rouen, CNRS, Groupe de Physique des Matériaux, F-76000 Rouen, France
Alex Frolov
Affiliation:
National Research Center ‘Kurchatov Institute’, Pl. Kurtachova, 123 182 Moscow, Russian Federation
Svetlana Fedotova
Affiliation:
National Research Center ‘Kurchatov Institute’, Pl. Kurtachova, 123 182 Moscow, Russian Federation
Katsuhiko Fujii
Affiliation:
Institute of Nuclear Safety System Inc., 64 Sata, Mihama 919-1205, Japan
Koji Fukuya
Affiliation:
Institute of Nuclear Safety System Inc., 64 Sata, Mihama 919-1205, Japan
Constantinos Hatzoglou
Affiliation:
Normandie Univ, UNIROUEN, INSA Rouen, CNRS, Groupe de Physique des Matériaux, F-76000 Rouen, France
Evgenia Kuleshova
Affiliation:
National Research Center ‘Kurchatov Institute’, Pl. Kurtachova, 123 182 Moscow, Russian Federation
Kristina Lindgren
Affiliation:
Department of Physics, Chalmers University of Technology, SE-412 96, Göteborg, Sweden
Andrew London
Affiliation:
United Kingdom Atomic Energy Authority, Culham Science Centre, Abingdon, Oxon, OX14 3DB, UK
Anabelle Lopez
Affiliation:
DEN-Service d'Etudes des Matériaux Irradiés, CEA, Université Paris-Saclay, F-91191, Gif-sur-Yvette, France
Sergio Lozano-Perez
Affiliation:
Department of Materials, University of Oxford, Parks Road, Oxford OX1 3PH, UK
Yuichi Miyahara
Affiliation:
Materials Science Research Laboratory, Central Research Institute of Electric Power Industry, Yokosuka, Japan
Yasuyoshi Nagai
Affiliation:
The Oarai Center, Institute for Materials Research, Tohoku University, Oarai, Ibaraki 311-1313, Japan
Kenji Nishida
Affiliation:
Materials Science Research Laboratory, Central Research Institute of Electric Power Industry, Yokosuka, Japan
Bertrand Radiguet
Affiliation:
Normandie Univ, UNIROUEN, INSA Rouen, CNRS, Groupe de Physique des Matériaux, F-76000 Rouen, France
Daniel K. Schreiber
Affiliation:
Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA
Naoki Soneda
Affiliation:
Materials Science Research Laboratory, Central Research Institute of Electric Power Industry, Yokosuka, Japan
Mattias Thuvander
Affiliation:
Department of Physics, Chalmers University of Technology, SE-412 96, Göteborg, Sweden
Takeshi Toyama
Affiliation:
The Oarai Center, Institute for Materials Research, Tohoku University, Oarai, Ibaraki 311-1313, Japan
Jing Wang
Affiliation:
Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA
Faiza Sefta
Affiliation:
Departement Métallurgie, EDF—R&D, Avenue des Renardières—Ecuelles, 77818 Moret-sur-Loing, France
Peter Chou
Affiliation:
Electric Power Research Institute, Palo Alto, CA, 94304, USA
Emmanuelle A. Marquis*
Affiliation:
Department of Materials Science and Engineering, University of Michigan, Ann Arbor, MI 48109, USA
*
*Author for correspondence: Emmanuelle A. Marquis, E-mail: emarq@umich.edu

Abstract

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.

Type
Data Analysis
Copyright
Copyright © Microscopy Society of America 2019 

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