期刊
MICROSCOPY
卷 67, 期 6, 页码 321-330出版社
OXFORD UNIV PRESS
DOI: 10.1093/jmicro/dfy036
关键词
convolutional neural network (CNN); gold nanoparticle; Z-contrast image; crystal structure; Hough transform
类别
资金
- Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan [16H06131, 16K14476]
In this article, we demonstrate that a convolutional neural network (CNN) can be effectively used to determine the presence of twins in the atomic resolution scanning transmission electron microscopy (STEM) images of catalytic Au nanoparticles. In particular, the CNN screening of Hough transformed images resulted in significantly higher accuracy rates as compared to those obtained by applying this technique to the raw STEM images. The proposed method can be utilized for evaluating the statistical twining fraction of Au nanoparticles that strongly affects their catalytic activity.
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