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Alpha Shape Analysis (ASA) Framework for Post- Clustering Property Determination in Atom Probe Tomographic Data.

Evan K Still1, Daniel K Schreiber2, Jing Wang2

  • 1Department of Nuclear Engineering, University of California, Berkeley, CA94720, USA.

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Summary
This summary is machine-generated.

This study introduces a new stochastic method for analyzing atom probe tomography data. It accurately determines solute cluster volume, surface area, and composition using alpha shapes and mesh-based approaches.

Keywords:
alpha shapesatom probe tomographycluster-analysissurface reconstruction

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Area of Science:

  • Materials Science
  • Computational Materials Science
  • Nanotechnology

Background:

  • Atom probe tomography (APT) is crucial for quantifying solute clusters.
  • Current methods struggle to accurately determine cluster volume, surface area, and composition.
  • Existing techniques like maximum separation have limitations in accurately defining cluster interfaces.

Purpose of the Study:

  • To develop a novel stochastic method for enhanced analysis of atom probe tomography data.
  • To accurately quantify solute cluster properties, including volume, surface area, and composition.
  • To overcome limitations of conventional clustering algorithms in composition determination.

Main Methods:

  • A novel stochastic method combining clustering algorithms with mesh-based approaches.
  • Generation of alpha shapes for each identified solute cluster.
  • Utilizing mesh boundaries to update and determine the total composition within clusters.

Main Results:

  • The method accurately quantifies cluster volume and surface area, accounting for concave components.
  • Improved determination of solute cluster composition by utilizing internal ions within the mesh boundary.
  • Enhanced analytical flexibility compared to traditional methods.

Conclusions:

  • The proposed stochastic method offers a more comprehensive analysis of solute clusters in APT data.
  • This approach overcomes previous limitations in determining cluster composition and geometric properties.
  • The technique provides a powerful tool for materials characterization and understanding solute behavior.