Deep Learning Enhanced in Situ Atomic Imaging of Ion Migration at Crystalline–Amorphous Interfaces

作者：Weikang Dong, Yi‐Chi Wang, Chen Yang, Chunhao Sun, Hesong Li, Ze Hua, Ziqi Wu, Xiaoxue Chang, Lixia Bao, Shuangquan Qu, Xintao Zuo, Wen Yang, Jing Lü, Ying Fu, Jiafang Li, Lixin Dong, Ruiwen Shao · 发表于：Nano Letters · 年份：2024 · DOI：10.1021/acs.nanolett.4c04472 · 被引用次数：11 · 研究领域：Electronic and Structural Properties of Oxides、Ion-surface interactions and analysis、Nuclear Physics and Applications

Improving the performance of energy storage, neuromorphic computing, and more applications requires an in-depth understanding of ion transport at interfaces, which are often hindered by facile atomic reconfiguration at working conditions and limited characterization capability. Here, we construct an in situ double-tilt electric manipulator inside an aberration-corrected scanning transmission electron microscope. Coupled with deep learning-based image enhancement, atomic images are enhanced 3-fold compared to traditional methods to observe the potassium ion migration and microstructure evolution at the crystalline–amorphous interface in antimony selenide. Potassium ions form stable anisotropic insertion sites outside the (Sb 4 Se 6 ) chain, with a few potassium ions present within the moieties. Combined experiments and density functional theory calculations reveal a reaction pathway of forming a novel metastable state during potassium ion insertion, followed by recovery and unexpected chirality changes at the interface upon potassium ion extraction. Our unique methodology paves the way for facilitating the improvement and rational design of nanostructured materials.