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

Yan Dong1, Auriane Etienne2, Alex Frolov3

  • 1Department of Materials Science and Engineering,University of Michigan,Ann Arbor, MI 48109,USA.

Microscopy and Microanalysis : the Official Journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada
|February 5, 2019
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Summary
This summary is machine-generated.

This study evaluated solute clustering quantification methods on proton-irradiated stainless steel. Maximum separation distance (MSM) worked well for Cu clusters but showed variability for Ni-Si clusters due to matrix effects.

Keywords:
atom probe tomographycluster analysismaximum separation

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Area of Science:

  • Materials Science
  • Metallurgy
  • Nuclear Engineering

Background:

  • Solute clustering quantification is crucial for understanding material behavior under irradiation.
  • Maximum separation distance (MSM) and local concentration thresholding are common analysis methods.
  • Interlaboratory studies are vital for assessing method reproducibility and applicability.

Purpose of the Study:

  • To evaluate the applicability and limitations of MSM and local concentration thresholding for solute clustering.
  • To identify necessary modifications and interpret existing data for cluster analysis.
  • To clarify variability in results arising from different operators and analysis methods.

Main Methods:

  • An interlaboratory study involving ten international research groups.
  • Analysis of experimental data from proton-irradiated 304 stainless steel.
  • Comparison of results from MSM and local concentration thresholding for Cu-rich and Ni-Si rich clusters.

Main Results:

  • MSM provided tight cluster number density for Cu clusters due to ideal matrix conditions.
  • MSM analysis of Ni-Si clusters exhibited significant variability due to high Ni matrix concentration and Si-decorated dislocations.
  • Local concentration filtering showed potential but cluster identification maintained high data scatter.

Conclusions:

  • The effectiveness of clustering quantification methods depends on solute distribution and matrix characteristics.
  • Clear guidelines are needed for selecting appropriate analysis methods and reporting results.
  • Understanding interoperator variability is essential for reliable interpretation of published data.