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

IF 2 3区 工程技术 Q2 MICROSCOPY
William J. Davids, Mengwei He, Huma Bilal, Andrew J. Breen, Simon P. Ringer
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引用次数: 0

Abstract

Atom probe tomography (APT) is routinely used to investigate nano-scale solute architecture within multicomponent systems. However, there is no consensus on how to best quantify solute clustering within APT data. This contribution leverages recent developments in the field of non-parametric hypothesis testing of nearest-neighbour distributions to address this critical gap. We adapt a goodness-of-fit-type test statistic known as ‘the level of heterogeneity’ to quantitatively discern whether solute distributions exhibit clustering behaviour beyond what would be expected from a random distribution. Further, comparing APT datasets remains difficult due to the inability to directly compare their nearest-neighbour distributions. We present a method that leverages Monte-Carlo simulations, already used to calculate the non-parametric statistic, as a means of comparing APT data. The method is more powerful than comparing datasets through the Pearson coefficient, as is conventionally done.
用非参数统计检验量化原子探针重构中溶质聚类
原子探针断层扫描(APT)通常用于研究多组分系统中的纳米级溶质结构。然而,关于如何最好地量化APT数据中的溶质聚类尚无共识。这一贡献利用了最近在最近邻分布的非参数假设检验领域的发展来解决这一关键差距。我们采用了一种被称为“异质性水平”的拟合优度型检验统计量,以定量地辨别溶质分布是否表现出超出随机分布预期的聚类行为。此外,由于无法直接比较APT数据集的近邻分布,比较APT数据集仍然很困难。我们提出了一种利用蒙特卡罗模拟的方法,已经用于计算非参数统计,作为比较APT数据的一种手段。该方法比通过皮尔逊系数比较数据集更强大,这是传统的做法。
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来源期刊
Ultramicroscopy
Ultramicroscopy 工程技术-显微镜技术
CiteScore
4.60
自引率
13.60%
发文量
117
审稿时长
5.3 months
期刊介绍: Ultramicroscopy is an established journal that provides a forum for the publication of original research papers, invited reviews and rapid communications. The scope of Ultramicroscopy is to describe advances in instrumentation, methods and theory related to all modes of microscopical imaging, diffraction and spectroscopy in the life and physical sciences.
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