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Area of Science:

  • Materials Science
  • Astrophysics
  • Data Analysis

Background:

  • Accurate characterization of nanoscale microstructures is crucial for material properties.
  • Existing methods for analyzing atom probe data can be subjective and require user-defined parameters.
  • Novel approaches are needed for objective and quantitative microstructure analysis.

Purpose of the Study:

  • To apply astrophysical methods for quantitative morphological analysis of nanoscale alloy microstructures.
  • To develop a user-independent method for characterizing microstructural features.
  • To achieve sub-voxel resolution in microstructure analysis from atomistic datasets.

Main Methods:

  • Utilized Minkowski functionals (volume, surface area, mean curvature, Euler characteristic) from astrophysics to describe second-phase regions.
  • Developed a maximum likelihood-based denoising filter to improve analysis accuracy and reduce user decision-making.
  • Employed natural cubic splines for data interpolation to refine voxel sizes and surface representation.

Main Results:

  • Demonstrated that alloy microstructures exhibit diverse morphologies (sponge-like, filament-like, plate-like, sphere-like) at different concentrations.
  • Quantitatively measured these microstructural features with high precision.
  • Showcased the superior performance of the maximum likelihood denoising filter over Gaussian smoothing.
  • Achieved sub-voxel resolution analysis without user-tuneable parameters.

Conclusions:

  • A mathematically well-defined, quantitative description of microstructure from atomistic datasets is achievable.
  • The adopted astrophysical methods provide an objective framework for nanoscale microstructure analysis.
  • The developed denoising and interpolation techniques enhance the accuracy and reliability of microstructure characterization.