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Automated voxelization of 3D atom probe data through kernel density estimation.

Srikant Srinivasan1, Kaustubh Kaluskar1, Santoshrupa Dumpala1

  • 1Institute of Combinatorial Discovery, Department of Materials Science and Engineering, Iowa State University, Ames, IA 50011-2300, USA.

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Summary
This summary is machine-generated.

This study introduces an unsupervised method using Kernel density estimators to optimize voxel size for atom probe tomography (APT) data. This approach enhances the accurate identification of nanoscale chemical features, especially at interfaces.

Keywords:
APTInformaticsInterfacesKernel density estimationNi Alloy

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Area of Science:

  • Materials Science
  • Nanotechnology
  • Data Analysis

Background:

  • Atom probe tomography (APT) is crucial for nanoscale chemical analysis.
  • Current APT data analysis relies on user-defined voxel sizes, introducing subjectivity and potential errors.
  • Resolving fine interfacial features and chemistries in materials is challenging with conventional methods.

Purpose of the Study:

  • To develop an unsupervised method for optimal voxel size selection in APT data analysis.
  • To improve the accuracy and reproducibility of nanoscale feature identification.
  • To specifically enhance the resolution of interfacial features and chemistries.

Main Methods:

  • Utilized Kernel density estimators (KDE) for unsupervised voxel size optimization.
  • Applied the method to atom probe tomography (APT) data.
  • Focused on analyzing the gamma/gamma' interface in a Ni-Al-Cr superalloy.

Main Results:

  • Successfully identified an optimal voxel size without user intervention or heuristic knowledge.
  • Demonstrated improved resolution of interfacial features and chemistries.
  • Validated the approach on a complex superalloy system.

Conclusions:

  • The Kernel density estimator-based method offers an objective and robust approach to APT data analysis.
  • This unsupervised technique enhances the reliability of nanoscale chemical feature identification, particularly at material interfaces.
  • The findings have significant implications for materials characterization and development.