Abstract

The analysis of solute clustering in atom probe tomography (APT) has almost exclusively relied on a simple algorithm based on the simple friend-of-friend analysis where a threshold distance or maximum separation defines whether atoms are part of a cluster or part of the matrix. This method is however limited to very specific microstructures and is very sensitive to parameter selection. To expand the range and applicability of current APT analysis tools, we introduce new quantitative data analysis methods based on density-based hierarchical clustering algorithms and relevant to solute clustering and segregation. We demonstrate the methods’ performance on the complex microstructure developing in a proton-irradiated Alloy 625, specifically focusing on the analyses of nanoscale Al clusters, Si clusters, and Si-decorated dislocation loops.

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