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Structural Studies of Macromolecules in Solution using Small Angle X-Ray Scattering

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Published on: November 5, 2018

Using non-parametric statistical testing to quantify solute clustering in atom probe reconstructions.

William J Davids¹, Mengwei He¹, Huma Bilal¹

¹Australian Centre for Microscopy & Microanalysis, and School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, Sydney, NSW 2006, Australia.

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|March 4, 2025

Summary

This summary is machine-generated.

This study introduces a new statistical method to quantify solute clustering in atom probe tomography (APT) data. It enables more robust comparisons between datasets, improving nano-scale material analysis.

Keywords:

Atom probe tomography Cluster analysis Solute clustering

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Area of Science:

Materials Science
Nanotechnology
Statistical Analysis

Background:

Atom probe tomography (APT) is crucial for analyzing nano-scale solute distributions in complex materials.
Quantifying solute clustering in APT data lacks standardized methodologies.
Comparing different APT datasets is challenging due to limitations in analyzing nearest-neighbor distributions.

Purpose of the Study:

To develop a quantitative method for assessing solute clustering in APT data.
To introduce a statistically robust approach for comparing APT datasets.
To overcome limitations of conventional comparison methods like the Pearson coefficient.

Main Methods:

Adaptation of a non-parametric hypothesis testing statistic, 'the level of heterogeneity', for nearest-neighbor distributions.
Utilizing Monte-Carlo simulations to calculate the heterogeneity statistic.
Developing a novel method for comparing APT datasets based on Monte-Carlo simulations.

Main Results:

The 'level of heterogeneity' statistic effectively quantifies solute clustering beyond random distributions.
The proposed Monte-Carlo simulation method provides a more powerful approach for comparing APT datasets.
The new method surpasses the efficacy of the conventional Pearson coefficient for dataset comparison.

Conclusions:

The developed statistical framework offers a standardized and powerful approach to solute clustering analysis in APT.
This method enhances the reliability and comparability of nano-scale solute architecture investigations.
The findings advance the quantitative analysis capabilities within materials science using APT.