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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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A Precise Method for Analysis of Elemental Distribution Inside Solute Clusters.

Takumi Kitayama1, Masaya Kozuka2, Yasuhiro Aruga3

  • 1Applied Physics Research Laboratory,Kobe Steel Ltd,1-5-5, Takatsukadai,Nishi-ku,Kobe, Hyogo 651-2271,Japan.

Microscopy and Microanalysis : the Official Journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada
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Summary

A new method analyzes elemental distribution within solute clusters using atom probe tomography data. This technique visualizes average concentration profiles, crucial for understanding material composition and guiding future research.

Keywords:
atom probe tomographyclusterdata analysiselemental concentration distribution

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Area of Science:

  • Materials Science
  • Analytical Chemistry
  • Computational Methods

Background:

  • Atom probe tomography (APT) is a powerful technique for nanoscale chemical analysis.
  • Analyzing elemental concentration within solute clusters requires specialized data processing methods.
  • Existing methods may struggle with clusters of varying sizes and shapes.

Purpose of the Study:

  • To develop and validate a computational procedure for analyzing elemental concentration distribution within solute clusters identified by APT.
  • To create a code capable of illustrating average concentration profiles with respect to absolute distance, accounting for cluster size and shape variations.
  • To establish criteria for reliable cluster analysis using APT data.

Main Methods:

  • Development of a custom code to process APT datasets and extract concentration profiles from detected solute clusters.
  • Implementation of algorithms to handle ellipsoidal clusters of various sizes and orientations.
  • Verification of the code's reliability using an artificial cluster model with known inhomogeneous elemental distribution.

Main Results:

  • The developed code can directly illustrate average concentration profiles within clusters, including error estimations for each data point.
  • Precise estimation of cluster centroids was identified as critical for accurate analysis.
  • Optimal conditions for cluster targeting include >20% detection efficiency, >30 atoms per cluster on average, and >100 atoms for concentration data points.

Conclusions:

  • The proposed procedure and developed code provide a reliable method for analyzing elemental concentration distribution in solute clusters from APT data.
  • The findings highlight the importance of accurate centroid estimation and provide practical guidelines for data acquisition and analysis.
  • This work enhances the quantitative analysis capabilities of APT for materials characterization.