Twinned/untwinned catalytic gold nanoparticles identified by applying a convolutional neural network to their Hough transformed Z-contrast images

Yuta Yamamoto; Mizuki Hattori; Junya Ohyama; Atsushi Satsuma; Nobuo Tanaka; Shunsuke Muto

doi:10.1093/jmicro/dfy036

Twinned/untwinned catalytic gold nanoparticles identified by applying a convolutional neural network to their Hough transformed Z-contrast images

Microscopy (Oxf). 2018 Dec 1;67(6):321-330. doi: 10.1093/jmicro/dfy036.

Authors

Yuta Yamamoto¹, Mizuki Hattori², Junya Ohyama^{2

3}, Atsushi Satsuma^{2

3}, Nobuo Tanaka¹, Shunsuke Muto¹

Affiliations

¹ Institute of Materials and Systems for Sustainability, Nagoya University, Furo-cho, Nagoya, Japan.
² Department of Materials Chemistry, Graduate School of Engineering, Nagoya University, Furo-cho, Nagoya, Japan.
³ Elements Strategy Initiative for Catalysts and Batteries, Kyoto University, Kyoto, Japan.

PMID: 30124934
DOI: 10.1093/jmicro/dfy036

Abstract

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.