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Spatial Separation of Molecular Conformers and Clusters
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The maximum separation cluster analysis algorithm for atom-probe tomography: parameter determination and accuracy.

Eric Aimé Jägle1, Pyuck-Pa Choi1, Dierk Raabe1

  • 1Metal Physics and Alloy Design,Max-Planck-Institut für Eisenforschung GmbH,Max-Planck-Strasse 1,40237 Düsseldorf,Germany.

Microscopy and Microanalysis : the Official Journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada
|October 21, 2014
PubMed
Summary
This summary is machine-generated.

A new method optimizes atom-probe tomography analysis for accurate cluster detection. This improves the precision of cluster number density, concentration, and size measurements in materials science.

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Area of Science:

  • Materials Science
  • Nanotechnology
  • Data Analysis

Background:

  • Atom-probe tomography (APT) is crucial for studying material clustering and precipitation.
  • Accurate cluster analysis in APT relies heavily on the maximum separation algorithm's parameters.
  • Incorrect parameter selection leads to significant errors in cluster characterization.

Purpose of the Study:

  • To introduce a novel method for determining the optimal dmax parameter in APT cluster analysis.
  • To enhance the accuracy of cluster analysis by minimizing parameter-dependent errors.
  • To validate the proposed method using atom-probe simulations.

Main Methods:

  • Development of a new parameter optimization technique for the maximum separation algorithm.
  • Utilizing atom-probe simulations to verify the method and assess parameter sensitivity.
  • Investigating the impact of trajectory aberrations on cluster analysis accuracy.

Main Results:

  • The proposed method provides an objective way to determine the optimal dmax parameter.
  • Simulations confirm the method's effectiveness in improving cluster analysis accuracy.
  • Errors in concentration and radius for Cu-rich precipitates were significantly reduced (<10 at% and <0.15 nm, respectively).

Conclusions:

  • The novel dmax determination method enhances the reliability of APT-based cluster analysis.
  • Accurate parameter selection is critical for precise characterization of nanoscale precipitates.
  • This approach improves the quantitative analysis of clustering and precipitation phenomena in materials.