期刊
ULTRAMICROSCOPY
卷 109, 期 10, 页码 1304-1309出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.ultramic.2009.06.007
关键词
Atom-probe tomography; Clustering; Nearest neighbour distances; Statistics
类别
资金
- French National Agency (ANR) [ANR-08-JCJC-0129-01]
- Agence Nationale de la Recherche (ANR) [ANR-08-JCJC-0129] Funding Source: Agence Nationale de la Recherche (ANR)
The measurement of chemical composition of tiny clusters is a tricky problem in both atom-probe tomography experiments and atomic simulations. A new approach relying on the distribution of the first nearest neighbour (1NN) distances between solute atoms in the 3D space composed of A and B atoms was developed. This new approach, the 1NN method, is shown to be an elegant way to get the composition of tiny B-enriched clusters embedded in a random AB solid solution. The theoretical statistical distributions of first neighbour distances P(r) for both random solid solution and solute-enriched clusters finely dispersed in a depleted matrix are established. It is shown that the most probable distance of P(r) gives directly the phase composition. Applications of this model to both one-phase SiGe alloy and boron-doped silicon containing small clusters indicate that this new approach is quite reliable. (C) 2009 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据