4.1 Article Data Paper

Data on Cu- and Ni-Si-Mn-rich solute clustering in a neutron irradiated austenitic stainless steel

Journal

DATA IN BRIEF
Volume 42, Issue -, Pages -

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ELSEVIER
DOI: 10.1016/j.dib.2022.108263

Keywords

Atom-probe tomography; Solute clustering; Python; Austenitic stainless steels; Precipitation

Funding

  1. University of Michigan College of Engineering

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The data presented in this article is supplementary to the research article, providing valuable information on phase instabilities in neutron-irradiated austenitic stainless steels. The article includes detailed experimental methods and analysis using the OSCAR program, which can be reused for processing other APT datasets. Overall, these findings contribute to the understanding of material behavior under particle bombardment.
The data presented in this article is supplementary to the re-search article Phase instabilities in austenitic steels during particle bombardment at high and low dose rates (Levine et al.) [5]. Needle-shaped samples were prepared with fo-cused ion beam milling from a 304L stainless steel that was irradiated with fast neutrons (E > 0.1 MeV) in the BOR-60 reactor at 318 degrees C to 47.5 dpa. Atom probe tomography (APT) experiments in voltage mode were then conducted on a Cameca LEAP 5000X HR. Atom position, range, and mass spectrum files after reconstruction with Cameca's IVAS soft-ware are included. Cu-and Ni-Si-Mn-rich solute nanoclusters were identified and analyzed using the Open Source Charac-terization of APT Reconstructions (OSCAR) program. Python code for OSCAR [4], information on the program's underlying algorithm, and sample output files are provided. A prox-imity histogram of a Ni-Si-Mn-rich cluster and a 1D den-sity/solute concentration profile of a Cu-rich cluster are given to demonstrate OSCAR's analytical functionalities. The provided APT dataset is valuable for benchmarking phase instabilities in neutron-irradiated austenitic stainless steels that occur at high doses. The OSCAR program can be reused to process other APT data sets where solute nanoclustering is of interest. (C) 2022 The Authors. Published by Elsevier Inc.

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