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Atom Probe Tomography Analysis of Exsolved Mineral Phases
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Point-by-point compositional analysis for atom probe tomography.

Leigh T Stephenson1, Anna V Ceguerra1, Tong Li1

  • 1Australian Centre for Microscopy & Microanalysis, and School of Aerospace, Mechatronic and Mechanical Engineering, The University of Sydney, NSW 2006, Australia.

Methodsx
|July 8, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a new coordinate-independent method for atom probe tomography (APT) data analysis, replacing traditional grid-based counting. This approach enhances measurement accuracy and enables analysis of smaller, non-dilute datasets.

Keywords:
Atom probe compositional analysisAtom probe microscopyBinomial analysisCompositional analysisNearest neighbours

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Area of Science:

  • Materials Science
  • Data Analysis
  • Nanotechnology

Background:

  • Traditional grid-based counting methods in atom probe tomography (APT) can introduce errors and limit analysis.
  • Existing methods struggle with smaller or non-dilute solid solution datasets.

Purpose of the Study:

  • To develop a coordinate-independent data processing approach for APT.
  • To improve measurement accuracy and statistical reliability in APT analyses.
  • To enable quantitative analysis of challenging datasets and enhance data visualization.

Main Methods:

  • Modified the binomial analysis for APT data by replacing grid-based counting with nearest neighbor identification.
  • Utilized spherical k-atom blocks based on nearest neighbors for analysis.
  • Implemented 3D data visualization of block composition and nearest neighbor anisotropy.
  • Applied z-statistics for direct comparison of experimental and expected composition curves.

Main Results:

  • Achieved improved measurements and statistics compared to grid-based methods.
  • Enabled quantitative analysis of smaller and non-dilute solid solution datasets.
  • Provided enhanced visualization of compositional fluctuations within the data.

Conclusions:

  • The coordinate-independent nearest neighbor approach offers a significant improvement over traditional grid-based methods for APT data analysis.
  • This novel method enhances analytical capabilities, statistical rigor, and data visualization.
  • Potential for application to other grid-based counting analyses and development of new visualization techniques.