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Estimating the physical cluster-size distribution within materials using atom-probe.

L T Stephenson1, M P Moody, B Gault

  • 1Australian Center for Microscopy and Microanalysis, The University of Sydney, New South Wales 2006, Australia. leigh.stephenson@sydney.edu.au

Microscopy Research and Technique
|August 14, 2013
PubMed
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Atom-probe techniques suffer from missing ion data, impacting cluster analysis. A new deconvolution method using the expectation-maximization algorithm accurately reconstructs cluster distributions, improving atom probe results.

Area of Science:

  • Materials Science
  • Analytical Chemistry
  • Nanotechnology

Background:

  • Atom-probe tomography (APT) is a powerful technique for analyzing atomic-scale structures.
  • A significant limitation of APT is the nondetection of ions, leading to 'missing information'.
  • This missing information complicates the accurate investigation of atomic clustering phenomena.

Purpose of the Study:

  • To address the challenge of 'missing information' in atom-probe experiments.
  • To develop a method for accurately determining cluster number densities from APT data.
  • To evaluate the impact of detector efficiency on cluster analysis.

Main Methods:

  • Modeling the measurable cluster-size distribution using a mixed binomial distribution.
  • Implementing a deconvolution method based on the expectation-maximization (EM) algorithm.
Keywords:
atom probe tomographyclustering analysisdisordered solidssolute clustering

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  • Restoring the original physical distribution from an efficiency-degraded distribution.

    • Main Results:

    • The expectation-maximization (EM) algorithm effectively deconvolutes efficiency-degraded distributions.
    • Accurate cluster number densities can be calculated from atom probe results using this method.
    • Restoration accuracy is highly dependent on detector efficiency, proving effective at 57% efficiency.

    Conclusions:

    • Detector efficiency limitations are more critical for cluster-finding analyses in APT than spatial resolution.
    • The developed deconvolution method provides accurate cluster number densities, crucial for precipitate analysis.
    • Improvements in atom-probe detector technology are essential for advancing cluster analysis.