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The Influence of Isoconcentration Surface Selection in Quantitative Outputs from Proximity Histograms

Published online by Cambridge University Press:  04 March 2019

Dallin J Barton ,
B Chad Hornbuckle ,
Kristopher A Darling  and
Gregory B Thompson
Dallin J Barton*
Affiliation:
Department of Metallurgical & Materials Engineering, The University of Alabama, Box 870202 Tuscaloosa, AL 35487-0200, USA
B Chad Hornbuckle
Affiliation:
United States Army Research Laboratory, Weapons and Materials Research Directorate, RDRL-WMM-B Aberdeen Proving Grounds, Aberdeen Proving Grounds, MD, 21005-5069, USA
Kristopher A Darling
Affiliation:
United States Army Research Laboratory, Weapons and Materials Research Directorate, RDRL-WMM-B Aberdeen Proving Grounds, Aberdeen Proving Grounds, MD, 21005-5069, USA
Gregory B Thompson
Affiliation:
Department of Metallurgical & Materials Engineering, The University of Alabama, Box 870202 Tuscaloosa, AL 35487-0200, USA
*
*Author for correspondence: Dallin J. Barton, E-mail: dbarton@crimson.ua.edu
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Abstract

Isoconcentration surfaces are commonly used to delineate phases in atom probe datasets. These surfaces then provide the spatial and compositional reference for proximity histograms, the number density of particles, and the volume fraction of particles within a multiphase system. This paper discusses the influence of the isoconcentration surface selection value on these quantitative outputs, using a simple oxide dispersive strengthened alloy, Fe91Ni8Zr1, as the case system. Zirconium reacted with intrinsic oxygen impurities in a consolidated ball-milled powder to precipitate nanoscale zirconia particles. The zirconia particles were identified by varying the Zr-isoconcentration values as well as by the maximum separation data mining method. The associated outputs mentioned above are elaborated upon in reference to the variation in this Zr isosurface value. Considering the dataset as a whole, a 10.5 at.% Zr isosurface provided a compositional inflection point for Zr between the particles and matrix on the proximity histogram; however, this value was unable to delineate all of the secondary oxide particles identified using the maximum separation method. Consequently, variations in the number density and volume fraction were observed as the Zr isovalue was changed to capture these particles resulting in a loss of the compositional accuracy. This highlighted the need for particle-by-particle analysis.

Keywords

cluster analysishigh-chemical-partitioningisoconcentration surfacelow-soluteproximity histogram
Type
Data Analysis
Information
Microscopy and Microanalysis , Volume 25 , Special Issue 2: Atom Probe Tomography and Microscopy APT&M 2018 , April 2019 , pp. 401 - 409
Copyright
Copyright © Microscopy Society of America 2019 

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References

Baik, SIl, Rawlings, MJS & Dunand, DC (2018). Atom probe tomography study of Fe-Ni-Al-Cr-Ti ferritic steels with hierarchically-structured precipitates. Acta Mater 144, 707–715.Google Scholar
Barton, DJ, Kale, C, Hornbuckle, BC, Darling, KA, Solanki, KN & Thompson, GB (2018). Microstructure and dynamic strain aging behavior in oxide dispersion strengthened 91Fe-8Ni-1Zr (at.%) alloy. Mater Sci Eng A 725, 503–509.Google Scholar
Chen, YM, Ohkubo, T, Kodzuka, M, Morita, K & Hono, K (2009). Laser-assisted atom probe analysis of zirconia/spinel nanocomposite ceramics. Scr Mater 61, 693–696.Google Scholar
Coppa, AC, Kapoor, M, Hornbuckle, BC, Weaver, ML, Noebe, RD & Thompson, GB (2015). Influence of dilute Hf additions on precipitation and martensitic transformation in Ni-Ti-Pd alloys. 67, 2244–2250.Google Scholar
Darling, KA., Kapoor, M, Kotan, H, Hornbuckle, BC, Walck, SD, Thompson, GB, Tschopp, MA & Kecskes, LJ (2015). Structure and mechanical properties of Fe-Ni-Zr oxide-dispersion-strengthened (ODS) alloys. J Nucl Mater 467, 205–213.Google Scholar
Dhara, S, Marceau, RKW, Wood, K, Dorin, T, Timokhina, IB & Hodgson, PD (2018). Atom probe tomography data analysis procedure for precipitate and cluster identification in a Ti-Mo steel. Data Brief 18, 968–982.Google Scholar
Gama, BA, Lopatnikov, SL & Gillespie, J (2004). Hopkinson bar experimental technique: A critical review. Applied Mechanics Reviews 57, 223–250.Google Scholar
Gault, B, Moody, MP, Cairney, JM & Ringer, SP (2012). In Atom Probe Microscopy, 1st ed, Hull, R, Jagadish, C, Osgood, RM, Parisi, J & Wang, ZM (Eds.), Analysis Techniques for Atom Probe Tomography, pp. 260–276. New York: Springer.Google Scholar
Hellman, OC, Du Rivage, JB & Seidman, DN (2003). Efficient sampling for three-dimensional atom probe microscopy data. Ultramicroscopy 95, 199–205.Google Scholar
Hellman, OC, Vandenbroucke, JA, Rüsing, J, Isheim, D & Seidman, DN (2000). Analysis of three-dimensional atom-probe data by the proximity histogram. Microsc Microanal 6, 437–444.Google Scholar
Hornbuckle, BC, Kapoor, M & Thompson, GB (2015). A procedure to create isoconcentration surfaces in low-chemical-partitioning, high-solute alloys. Ultramicroscopy 159, 346–353.Google Scholar
Isheim, D, Gagliano, MS, Fine, ME & Seidman, DN (2006). Interfacial segregation at Cu-rich precipitates in a high-strength low-carbon steel studied on a sub-nanometer scale. Acta Mater 54, 841–849.Google Scholar
Kapoor, M, Kaub, T, Darling, KA, Boyce, BL & Thompson, GB (2017). An atom probe study on Nb solute partitioning and nanocrystalline grain stabilization in mechanically alloyed Cu-Nb. Acta Mater 126, 564–575.Google Scholar
Kontis, P, Collins, DM, Wilkinson, AJ, Reed, RC, Raabe, D & Gault, B (2018). Microstructural degradation of polycrystalline superalloys from oxidized carbides and implications on crack initiation. Scr Mater 147, 59–63.Google Scholar
Marquis, EA, Araullo-Peters, V, Dong, Y, Etienne, A, Fedotov, S, Fujii, K, Fukuya, K, Kuleshova, E, Nahai, Y, Nishida, K, Radiguet, B, Schreiber, D, Soneda, N, Thuvander, M, Toyama, T, Sefta, F & Chou, P (2017). On the Use of Density-Based Algorithms for the Analysis of Solute Clustering in Atom Probe Tomography Data. In 18th International Conference on Environmental Degradation of Materials in Nuclear Power Systems Water Reactors, Jackson, J. H., Paraventi, D. & Wright, M. (Eds.), pp. 881–897. Portland: Springer.Google Scholar
Marquis, EA & Vurpillot, F (2008). Chromatic aberrations in the field evaporation behavior of small precipitates. Microsc Microanal 14, 561–570.Google Scholar
Miller, MK, Longstreth-Spoor, L & Kelton, KF (2011). Detecting density variations and nanovoids. Ultramicroscopy 111, 469–472.Google Scholar
Miller, MK & Kenik, EA (2002). Atom probe tomography: A technique for nanoscale characterization. Microsc Microanal 8, 336–341.Google Scholar
Prakash Kolli, R & Seidman, DN (2007). Comparison of compositional and morphological atom-probe tomography analyses for a multicomponent Fe-Cu steel. Microsc Microanal 13, 272–284.Google Scholar
Stephenson, LT, Moody, MP, Liddicoat, PV & Ringer, SP (2007). New techniques for the analysis of fine-scaled clustering phenomena within Atom Probe Tomography (APT) data. Microsc Microanal 13, 448–463.10.1017/S1431927607070900Google Scholar
Thompson, GB, Wan, L, Yu, X Xiang & Vogel, F (2017). Influence of phase stability on the in situ growth stresses in Cu/Nb multilayered films. Acta Mater 132, 149–161.Google Scholar
Torres, KL, Daniil, M, Willard, MA & Thompson, GB (2011). The influence of voxel size on atom probe tomography data. Ultramicroscopy 111, 464–468.Google Scholar
Williams, CA, Haley, D, Marquis, EA, Smith, GDW & Moody, MP (2013). Defining clusters in APT reconstructions of ODS steels. Ultramicroscopy 132, 271–278.Google Scholar
Yoon, KE, Noebe, RD, Hellman, OC & Seidman, DN (2004). Dependence of interfacial excess on the threshold value of the isoconcentration surface. Surf Interface Anal 36, 594–597.Google Scholar
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