著者

木村 勇気 勝野 弘康 平川 靜 山﨑 智也

出版者

公益社団法人 日本表面真空学会

雑誌

表面と真空 (ISSN:24335835)

巻号頁・発行日

vol.66, no.12, pp.700-705, 2023-12-10 (Released:2023-12-10)

参考文献数

27

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Non-equilibrium processes such as nucleation are difficult to observe in situ using transmission electron microscopy (TEM) because of spatiotemporal stochastic process. Therefore, we have been developing a method to predict/detect nucleation events and observe under low electron doses conditions in real time with the support of machine learning. Low electron dose observation is important to avoid radiolysis of water in the observation of aqueous solutions using liquid-cell TEM. Our data-driven TEM that can suggest observation points to the operator by processing in situ observation data in real time. In other words, it is data-driven TEM in which the TEM helps the operator, rather than the TEM being driven by the data. By incorporating the two codes into the TEM's software, nucleation can now be observed efficiently.