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Atom Probe Tomography Analysis of Exsolved Mineral Phases
08:14

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Published on: October 25, 2019

Atomic-scale phase composition through multivariate statistical analysis of atom probe tomography data.

Michael R Keenan1, Vincent S Smentkowski, Robert M Ulfig

  • 1mrkeena@rochester.rr.com

Microscopy and Microanalysis : the Official Journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada
|May 24, 2011
PubMed
Summary
This summary is machine-generated.

Multivariate statistical analysis can now estimate chemical composition from atom probe tomography data at full 3D spatial resolution. This method overcomes data sparsity and noise for precise material analysis.

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Area of Science:

  • Materials Science
  • Analytical Chemistry
  • Statistical Analysis

Background:

  • Atom probe tomography (APT) provides high-resolution 3D chemical information.
  • Raw APT data is sparse and dominated by counting noise, limiting compositional analysis.
  • Current methods struggle to fully leverage the spatial resolution of APT for compositional mapping.

Purpose of the Study:

  • To demonstrate the application of multivariate statistical analysis (MSA) to APT data.
  • To enable chemical composition estimation at the full 3D spatial resolution of APT.
  • To address the challenges posed by sparse and noisy APT datasets.

Main Methods:

  • Application of multivariate statistical analysis techniques to APT spectral-image datasets.
  • Development of efficient numerical approaches for principal component analysis (PCA) on large-scale APT data.
  • Addressing data sparsity and counting noise inherent in APT.

Main Results:

  • Successful estimation of chemical composition at the full 3D spatial resolution of APT.
  • Multivariate results provide probabilities of atom presence within specific chemical phases.
  • Principal component analysis (PCA) is computed efficiently on massive datasets, even on standard hardware.

Conclusions:

  • Multivariate statistical analysis is a powerful tool for extracting detailed chemical information from APT data.
  • This approach significantly enhances the compositional analysis capabilities of atom probe tomography.
  • The developed methods are computationally efficient and applicable to large-scale APT datasets.