Advanced Characterization Techniques for Understanding Failure Mechanisms and Guiding Materials Design in Garnet‐Type Solid‐State Batteries
Yan‐Feng Wang, Song‐Ze Li, Xiao‐Man Chen, Ji‐Jun Lu, Feng‐Shuo Xi, Kui‐Xian Wei, Wen‐Hui Ma, Rong Deng, Shao‐Yuan LiABSTRACT
Garnet‐type solid‐state electrolytes (SSEs) are widely regarded as key materials for high‐energy lithium‐ion solid‐state batteries (LSSBs), owing to their compatibility with lithium metal anodes and high‐voltage cathodes. However, their practical application is limited by multiscale degradation processes, including interfacial instability, space‐charge‐layer formation, surface contamination, structural defects, and electro‐chemo‐mechanical lithium penetration. Revealing the dynamic evolution of these failure mechanisms and the associated interfacial reaction and ion transport processes is essential for developing high‐performance LSSBs. Advanced characterization techniques serve as powerful tools to unravel the complex physicochemical processes occurring in SSEs and at electrode/electrolyte interfaces. Herein, we review recent research advances in spectroscopic, microscopic, scattering, and operando methods for characterizing dynamic processes in SSEs, aiming to provide mechanistic insights into interfacial evolution and failure mechanisms related to lithium penetration. Furthermore, it is demonstrated how such characterization‐enabled understanding informs rational design strategies to enhance interfacial stability and suppress dendrite formation, including surface chemical regulation and microstructural engineering, and how the effectiveness of these approaches is validated by electrochemical characterization. Deeper insights into the microstructure and mechanisms of SSEs are still limited by spatial and temporal resolution, making it difficult to elucidate the evolution of buried interfaces. Finally, future perspectives are outlined, highlighting multimodal operando characterization, quantitative structure–property correlations, machine learning‐assisted analysis, and artificial intelligence‐driven materials discovery for next‐generation garnet‐type LSSBs.