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Abstract
Diagnostic statistics and information theory techniques have been developed to investigate the accuracy to which solute clusters characterised in atom probe tomography (APT) data can reflect the true nature of the physical clusters in the original specimen. Simulated atom-probe datasets representing a range of atomic solute clustering within a pseudo-binary alloy upon an fcc aluminium lattice were generated for the study. The effectiveness of partitioning the APT-like simulated data based upon a binary classification defined by a distance threshold d max upon the kth nearest neighbour distance distribution was investigated. Information theory was also used to optimise the selection of the threshold d max. Analysis of variation was performed upon a factorial design of data simulations with low and high levels of: solute concentration; short-range order; and background to the mass-to-charge-state-ratio spectrum. This meta-analysis showed that the background levels have a significant compromising effect upon the binary classification in low solute systems with relatively low or random levels of clustering. Although the random clustering of higher solute concentrations is better analysed, significantly non-random clustering in both low and high solute concentrations is analysed well despite the presence of high levels of background. A meta-analysis of the binary classification upon a simulated dispersion of coherent precipitates within a similar matrix was also undertaken. Optimal k and d max parameters are likely a dependent upon the physical dimensions of precipitate size as well as the precipitate/matrix solute concentrations.
