Dhara S, Marceau R K W, Wood K, Dorin T, Timokhina I B, Hodgson P D
Deakin University, Institute for Frontier Materials, Geelong, VIC 3216, Australia.
Australian Nuclear Science and Technology Organisation (ANSTO), Kirrawee, New South Wales, Australia.
Data Brief. 2018 Mar 27;18:968-982. doi: 10.1016/j.dib.2018.03.094. eCollection 2018 Jun.
An atom probe tomography data analysis procedure for identification of particles in a Ti-Mo steel is presented. This procedure has been used to characterise both carbide precipitates (larger particles) and solute clusters (smaller particles), as reported in an accompanying Mater. Sci. Eng. A paper [1]. Particles were identified using the maximum separation method (cluster-finding algorithm) after resolving peak overlaps at several locations in the mass spectrum. The cluster-finding algorithm was applied to the data in a two-stage process to properly identify particles having a bimodal size distribution. Furthermore, possible misidentification of matrix atoms (mainly Fe) due to the local magnification effect (from the difference in field evaporation potential between the matrix and precipitates) has been resolved using an atomic density approach, comparing that measured experimentally using APT to the theoretical density of both the matrix and particles.
本文介绍了一种用于识别Ti-Mo钢中颗粒的原子探针层析成像数据分析程序。如一篇随附的《材料科学与工程A》论文[1]中所报道，该程序已用于表征碳化物析出物（较大颗粒）和溶质团簇（较小颗粒）。在解决质谱中几个位置的峰重叠问题后，使用最大分离法（聚类算法）识别颗粒。聚类算法分两个阶段应用于数据，以正确识别具有双峰尺寸分布的颗粒。此外，利用原子密度方法解决了由于局部放大效应（基体和析出物之间场蒸发势的差异）导致的基体原子（主要是Fe）可能的误识别问题，即将原子探针层析成像实验测量的密度与基体和颗粒的理论密度进行比较。
