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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 ,
Auriane Etienne ,
Alex Frolov ,
Svetlana Fedotova ,
Katsuhiko Fujii ,
Koji Fukuya ,
Constantinos Hatzoglou ,
Evgenia Kuleshova ,
Kristina Lindgren  and
Andrew London Open the ORCID record for Andrew London [Opens in a new window]
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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
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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.

Keywords

atom probe tomographycluster analysismaximum separation
Type
Data Analysis
Information
Microscopy and Microanalysis , Volume 25 , Special Issue 2: Atom Probe Tomography and Microscopy APT&M 2018 , April 2019 , pp. 356 - 366
Copyright
Copyright © Microscopy Society of America 2019 

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References

Auger, P, Pareige, P, Welzel, S and Van Duysen, JC (2000). Synthesis of atom probe experiments on irradiation-induced solute segregation in French ferritic pressure vessel steels. J Nucl Mater 280(3), 331–344.10.1016/S0022-3115(00)00056-8Google Scholar
Blavette, D and Chambreland, S (1986). A statistical model for deriving microstructure parameters of finely dispersed systems from atom-probe analyses. J Phys Colloq 47(C7), C7–503.10.1051/jphyscol:1986784Google Scholar
Blum, TB, Darling, JR, Kelly, TF, Larson, DJ, Moser, DE, Perez-Huerta, A, Prosa, TJ, Reddy, SM, Reinhard, DA, Saxey, DW, Ulfig, RM and Valley, JW (2017). Best practices for reporting atom probe analysis of geological materials. In Microstructural Geochronology: Planetary Records Down to Atom Scale. Moser, DE, Corfu, F, Darling, JR, Reddy, SM and Tait, K. Wiley, pp. 369–373.10.1002/9781119227250.ch18Google Scholar
Ceguerra, AV, Moody, MP, Stephenson, LT, Marceau, RKW and Ringer, SP (2010). A three-dimensional Markov field approach for the analysis of atomic clustering in atom probe data. Philos Mag 90(12), 1657–1683.10.1080/14786430903441475Google Scholar
Cerezo, A and Davin, L (2007). Aspects of the observation of clusters in the 3-dimensional atom probe. Surf Interface Anal 39, 184–188.10.1002/sia.2486Google Scholar
Chen, Y, Chou, PH and Marquis, EA (2014). Quantitative atom probe tomography characterization of microstructures in a proton irradiated 304 stainless steel. J Nucl Mater 451(1–3), 130–136.10.1016/j.jnucmat.2014.03.034Google Scholar
Couturier, L, De Geuser, F and Deschamps, A (2016). Direct comparison of Fe–Cr unmixing characterization by atom probe tomography and small angle scattering. Mater Charact 121, 61–67.10.1016/j.matchar.2016.09.028Google Scholar
CVL (2016). CVL: Characterization Virtual Laboratory. Monash University. Available at https://www.massive.org.au/cvl/cvl-workbenches/atom-probe-workbench.Google Scholar
De Geuser, F, Lefebvre, W and Blavette, D (2006). 3D atom probe study of solute atoms clustering during natural ageing and pre-ageing of an Al–Mg–Si alloy. Philos Mag Lett 86(4), 227–234.10.1080/09500830600643270Google Scholar
Edmondson, PD, Miller, MK, Powers, KA and Nanstad, RK (2016). Atom probe tomography characterization of neutron irradiated surveillance samples from the R. E. Ginna reactor pressure vessel. J Nucl Mater 470, 147–154.10.1016/j.jnucmat.2015.12.038Google Scholar
Gault, B, Danoix, F, Hoummada, K, Mangelinck, D and Leitner, H (2012 a). Impact of directional walk on atom probe microanalysis. Ultramicroscopy 113, 182–191.10.1016/j.ultramic.2011.06.005Google Scholar
Gault, B, Moody, MP, Cairney, JM and Ringer, SP (2012 b). Atom Probe Microscopy. New York: Springer-Verlag.10.1007/978-1-4614-3436-8Google Scholar
Gurovich, B, Kuleshova, E, Shtrombakh, Y, Fedotova, S, Maltsev, D, Frolov, A, Zabusov, O, Erak, D and Zhurko, D (2015). Evolution of structure and properties of VVER-1000 RPV steels under accelerated irradiation up to beyond design fluences. J Nucl Mater 456, 23–32.10.1016/j.jnucmat.2014.09.019Google Scholar
Haley, D (2010). 3Depict—Visualisation and Analysis for Atom Probe. Available at http://threedepict.sourceforge.net/.Google Scholar
Heinrich, A, Al-Kassab, TA and Kirchheim, R (2003). Investigation of the early stages of decomposition of Cu–0.7 at% Fe with the tomographic atom probe. Mater Sci Eng A 353(1–2), 92–98.10.1016/S0921-5093(02)00673-1Google Scholar
Hellman, OC, Vandenbroucke, JA, Rüsing, J, Isheim, D and Seidman, DN (2000). Analysis of three-dimensional atom probe data by the proximity histogram. Microsc Microanal 6, 437–444.10.1007/S100050010051Google Scholar
Hyde, JM, Cerezo, A and Williams, TJ (2009). Statistical analysis of atom probe data: Detecting the early stages of solute clustering and/or co-segregation. Ultramicroscopy 109(5), 502–509.10.1016/j.ultramic.2008.10.007Google Scholar
Hyde, JM, DaCosta, G, Hatzoglou, C, Weekes, H, Radiguet, B, Styman, PD, Vurpillot, F, Pareige, C, Etienne, A, Bonny, G, Castin, N, Malerba, L and Pareige, P (2017). Analysis of radiation damage in light water reactors: Comparison of cluster analysis methods for the analysis of atom probe data. Microsc Microanal 23(2), 366–375.10.1017/S1431927616012678Google Scholar
Hyde, JM, Marquis, EA, Wilford, KB and Williams, TJ (2011). A sensitivity analysis of the maximum separation method for the characterisation of solute clusters. Ultramicroscopy 111(6), 440–447.10.1016/j.ultramic.2010.12.015Google Scholar
Jaegle, EA, Choi, PP and Raabe, D (2014). The maximum separation cluster analysis algorithm for atom-probe tomography: Parameter determination and accuracy. Microsc Microanal 20, 1662–1671.10.1017/S1431927614013294Google Scholar
Jiao, Z and Was, GS (2011). Impact of localized deformation on IASCC in austenitic stainless steels. J Nucl Mater 408(3), 246–256.10.1016/j.jnucmat.2010.10.087Google Scholar
Kolli, RP and Seidman, DN (2007). Comparison of compositional and morphological atom-probe tomography analyses for a multicomponent Fe–Cu steel. Microsc Microanal 13, 272–284.10.1017/S1431927607070675Google Scholar
Kuramoto, A, Toyama, T, Nagai, Y, Inoue, K, Nozawa, Y, Hasegawa, M and Valo, M (2013). Microstructural changes in a Russian-type reactor weld material after neutron irradiation, post-irradiation annealing and re-irradiation studied by atom probe tomography and positron annihilation spectroscopy. Acta Mater 61(14), 5236–5246.Google Scholar
Lefebvre, W, Philippe, T and Vurpillot, F (2011). Application of Delaunay tessellation for the characterization of solute-rich clusters in atom probe tomography. Ultramicroscopy 111(3), 200–206.10.1016/j.ultramic.2010.11.034Google Scholar
Lefebvre, W, Vurpillot, F and Sauvage, X (2016). Atom Probe Tomography, Put Theory Into Practice, Elsevier.Google Scholar
London, A (2016). AtomProbeLab: Matlab-based analysis of Atom Probe Data. Available at https://sourceforge.net/projects/atomprobelab/.Google Scholar
Marceau, RK, Stephenson, LT, Hutchinson, CR and Ringer, SP (2011). Quantitative atom probe analysis of nanostructure containing clusters and precipitates with multiple length scales. Ultramicroscopy 111(6), 738–742.Google Scholar
Marquis, EA, Araullo-Peters, V, Dong, Y, Etienne, A, Fedotova, S, Fujii, K, Fukuya, K, Kuleshova, E, Lopez, A, London, A, Lozano-Perez, S, Nagai, Y, Nishida, K, Radiguet, B, Schreiber, D, Soneda, N, Thuvander, M, Toyama, T, Sefta, F and Chou, P (2017). On the use of density-based algorithms for the analysis of solute clustering in atom probe tomography data. Proceedings of the 18th International Conference on Environmental Degradation of Materials in Nuclear Power Systems—Water Reactors. The Minerals, Metals and Materials Series. Springer, Cham.Google Scholar
Meslin, E, Lambrecht, M, Hernández-Mayoral, M, Bergner, F, Malerba, L, Pareige, P, Radiguet, B, Barbu, A, Gómez-Briceño, D, Ulbricht, A and Almazouzi, A (2010). Characterization of neutron-irradiated ferritic model alloys and a RPV steel from combined APT, SANS, TEM and PAS analyses. J Nucl Mater 406(1), 73–83.10.1016/j.jnucmat.2009.12.021Google Scholar
Meslin, E, Radiguet, B and Loyer-Prost, M (2013). Radiation-induced precipitation in a ferritic model alloy: An experimental and theoretical study. Acta Mater 61(16), 6246–6254.10.1016/j.actamat.2013.07.008Google Scholar
Miller, MK and Hetherington, MG (1991). Local magnification effects in the atom probe. Surf Sci 246, 442–449.Google Scholar
Miller, MK, Powers, KA, Nanstad, RK and Efsing, P (2013). Atom probe tomography characterizations of high nickel, low copper surveillance RPV welds irradiated to high fluences. J Nucl Mater 437(1–3), 107–115.10.1016/j.jnucmat.2013.01.312Google Scholar
Miller, MK and Russell, KF (2007). Embrittlement of RPV steels: An atom probe tomography perspective. J Nucl Mater 371(1), 145–160.10.1016/j.jnucmat.2007.05.003Google Scholar
Miller, MK, Russell, KF, Kocik, J and Keilova, E (2000). Embrittlement of low copper VVER 440 surveillance samples neutron-irradiated to high fluences. J Nucl Mater 282(1), 83–88.10.1016/S0022-3115(00)00240-3Google Scholar
Moody, MP, Stephenson, LT, Ceguerra, AV and Ringer, SP (2008). Quantitative binomial distribution analyses of nanoscale like-solute atom clustering and segregation in atom probe tomography data. Microsc Res Technol 71(7), 542–550.10.1002/jemt.20582Google Scholar
Pareige, P, Stoller, RE, Russell, KF and Miller, MK (1997). Atom probe characterization of the microstructure of nuclear pressure vessel surveillance materials after neutron irradiation and after annealing treatments. J Nucl Mater 249(2–3), 165–174.10.1016/S0022-3115(97)00215-8Google Scholar
Radiguet, B, Barbu, A and Pareige, P (2007). Understanding of copper precipitation under electron or ion irradiations in FeCu 0.1 wt% ferritic alloy by combination of experiments and modelling. J Nucl Mater 360, 104–117.10.1016/j.jnucmat.2006.09.007Google Scholar
Rose, DJ (1956). On the magnification and resolution of the field emission electron microscope. J Appl Phys 27(3), 215–220.Google Scholar
Saxey, DW (2011). Correlated ion analysis and the interpretation of atom probe mass spectra. Ultramicroscopy 111(6), 473–479.10.1016/j.ultramic.2010.11.021Google Scholar
Shu, S, Wirth, BD, Wells, PB, Morgan, DD and Odette, GR (2018). Multi-technique characterization of the precipitates in thermally aged and neutron irradiated Fe–Cu and Fe–Cu–Mn model alloys: Atom probe tomography reconstruction implications. Acta Mater 146, 237–252.Google Scholar
Stephenson, LT, Moody, MP, Liddicoat, PV and Ringer, SP (2007). New techniques for the analysis of fine-scaled clustering phenomena within atom probe tomography (APT) data. Microsc Microanal 13(6), 448–463.10.1017/S1431927607070900Google Scholar
Styman, PD, Hyde, JM, Parfitt, D, Wilford, K, Burke, MG, English, CA and Efsing, P (2015). Post-irradiation annealing of Ni–Mn–Si-enriched clusters in a neutron-irradiated RPV steel weld using atom probe tomography. J Nucl Mater 459, 127–134.10.1016/j.jnucmat.2015.01.027Google Scholar
Styman, PD, Hyde, JM, Wilford, K and Smith, GDW (2013). Quantitative methods for the APT analysis of thermally aged RPV steels. Ultramicroscopy 132(0), 258–264.10.1016/j.ultramic.2012.12.003Google Scholar
Takeuchi, T, Kuramoto, A, Kameda, J, Toyama, T, Nagai, Y, Hasegawa, M, Ohkubo, T, Yoshiie, T, Nishiyama, Y and Onizawa, K (2010). Effects of chemical composition and dose on microstructure evolution and hardening of neutron-irradiated reactor pressure vessel steels. J Nucl Mater 402(2), 93–101.10.1016/j.jnucmat.2010.04.008Google Scholar
Thompson, K, Lawrence, D, Larson, DJ, Olson, JD, Kelly, TF and Gorman, B (2007). In situ site-specific specimen preparation for atom probe tomography. Ultramicroscopy 107(2–3), 131–139.Google Scholar
Toyama, T, Kuramoto, A, Nagai, Y, Inoue, K, Nozawa, Y, Shimizu, Y, Matsukawa, Y, Hasegawa, M and Valo, M (2014). Effects of post-irradiation annealing and re-irradiation on microstructure in surveillance test specimens of the Loviisa-1 reactor studied by atom probe tomography and positron annihilation. J Nucl Mater 449, 207–212.Google Scholar
Toyama, T, Nagai, Y, Tang, Z, Hasegawa, M, Almazouzi, A, van Walle, E and Gerard, R (2007). Nanostructural evolution in surveillance test specimens of a commercial nuclear reactor pressure vessel studied by three-dimensional atom probe and positron annihilation. Acta Mater 55(20), 6852–6860.Google Scholar
Vaumousse, D, Cerezo, A and Warren, PJ (2003). A procedure for quantification of precipitates microstructures from three-dimensional atom probe data. Ultramicroscopy 95, 215–221.10.1016/S0304-3991(02)00319-4Google Scholar
Vurpillot, F, Bostel, A and Blavette, D (2000 a). Trajectory overlaps and local magnification in three-dimensional atom probe. Appl Phys Lett 76(21), 3127–3129.10.1063/1.126545Google Scholar
Vurpillot, F, Bostel, A, Cadel, E and Blavette, D (2000 b). The spatial resolution of 3D atom probe in the investigation of single-phase materials. Ultramicroscopy 84(3–4), 213–224.10.1016/S0304-3991(00)00035-8Google Scholar
Vurpillot, F, De Geuser, F, Da Costa, G and Blavette, D (2004). Application of Fourier transform and autocorrelation to cluster identification in the three-dimensional atom probe. J Microsc 216, 234–240.10.1111/j.0022-2720.2004.01413.xGoogle Scholar
Waugh, AR, Boyes, ED and MJ, S (1876). Investigations of field evaporation with a field-desorption microscope. Surf Sci 69(1), 109–142.Google Scholar
Wells, PB, Yamamoto, T, Miller, B, Milot, T, Cole, J, Wu, Y and Odette, GR (2014). Evolution of manganese–nickel–silicon-dominated phases in highly irradiated reactor pressure vessel steels. Acta Mater 80, 205–219.10.1016/j.actamat.2014.07.040Google Scholar
Williams, CA, Haley, D, Marquis, EA, Smith, GD and Moody, MP (2013). Defining clusters in APT reconstructions of ODS steels. Ultramicroscopy 132, 271–278.10.1016/j.ultramic.2012.12.011Google Scholar
Zelenty, J, Dahl, A, Hyde, J, Smith, GD and Moody, MP (2017). Detecting clusters in atom probe data with Gaussian mixture models. Microsc Microanal 23(2), 269–278.10.1017/S1431927617000320Google Scholar
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