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Contingency table techniques for three dimensional atom probe tomography.

Michael P Moody1, Leigh T Stephenson, Peter V Liddicoat

  • 1Australian Key Centre for Microscopy and Microanalysis, University of Sydney, New South Wales, Australia. michael.moody@emu.usyd.edu.au

Microscopy Research and Technique
|February 7, 2007
PubMed
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This study introduces a new method for analyzing solute clustering in multicomponent alloys using contingency tables. The approach refines data interpretation for more accurate material characterization.

Area of Science:

  • Materials Science
  • Metallurgy
  • Data Analysis

Background:

  • Investigating fine-scale solute co-/anti-segregation effects in multicomponent alloys is crucial for understanding material properties.
  • Conventional statistical methods like P-value testing with chi-squared can be ambiguous for atom probe data.
  • Existing methods struggle with large datasets or high solute concentrations, necessitating improved analytical techniques.

Purpose of the Study:

  • To develop and validate a robust contingency table analysis procedure for three-dimensional atom probe data.
  • To address limitations of conventional statistical tests in analyzing solute segregation.
  • To introduce a novel method for quantifying solute clustering in materials.

Main Methods:

  • Developed a contingency table analysis procedure for three-dimensional atom probe datasets.

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  • Identified and eliminated sources of error and inaccuracy in the technique.
  • Introduced random labeling techniques and proposed comparing experimental results (μ(exp)) to randomized values (μ(rand)).

    • Main Results:

    • The coefficient of contingency is a useful basis for comparison but is dependent on block size and solute composition.
    • Conventional P-value testing is unsatisfactory for large block numbers or high solute concentrations.
    • The proposed μ(exp) vs. μ(rand) method effectively quantifies solute clustering and helps optimize block size for analysis.

    Conclusions:

    • Contingency table analysis is valuable for studying solute segregation but requires careful parameter selection.
    • The new random labeling comparison method provides a more reliable assessment of solute clustering.
    • This refined approach enhances the interpretation of atom probe data for multicomponent alloys.