ScholarWorks@경상국립대학교

초록

The pursuit of high-energy-density anode-free batteries (AFBs) has shifted the research focus toward the fundamental understanding of structural stability and interfacial chemistry. Layered cathode materials are central to this transition, yet their practical application in AFBs is hindered by complex failure mechanisms such as lattice strain and parasitic side reactions. This review systematically traces the evolution of advanced transmission electron microscopy (TEM) and its pivotal role in elucidating the structure-property relationships of these electrode materials. By integrating artificial intelligence (AI) and machine learning (ML) for highthroughput data processing, we highlight how modern characterization overcomes traditional limitations in image denoising and automated defect recognition. The discussion encompasses both lithium and sodium-ion systems, focusing on in situ and cryogenic TEM techniques that reveal real-time ion migration, phase transformations, and the evolution of fragile solid-electrolyte interphases. Furthermore, we emphasize the interdisciplinary synergy between electron microscopy and AI as a necessity for the "mechanism-driven" design of nextgeneration and high-stability batteries. This review provides critical insights into addressing the bottlenecks of layered cathode configurations. Furthermore, this review discusses the emerging role of AI in integrating fragmented research data into structured knowledge graphs. By organizing highly heterogeneous experimental findings into relational networks, AI can uncover latent structure-performance correlations, offering a datadriven roadmap for mechanism-guided material design and advanced characterization.

키워드