Abstract: Lithium-ion battery is featured by structural and chemical complexities across a broad range of length scales and, ultimately, it is the hierarchy of the battery structure that determines its functionality. Investigating battery function, degradation, and failure mechanisms requires a comprehensive exploration encompassing structural, chemical, mechanical, and dynamic perspectives. Thus, battery research poses significant experimental and computational challenges, stemming from the intricate chemomechanical processes taking place within an operational battery. In this talk, I will present a macro-to-nano zoom through the structural hierarchy of real-world battery cells using advanced X-ray techniques. We visualize damage, deformation, compositional and chemical heterogeneity at different length scales, associating them to diverse degradation phenomena and mechanisms. These findings underscore the significance of the mechanical properties of the battery electrode material, which undergo changes during battery cycling, significantly impacting both immediate and long-term cell behaviors. The integration of in-situ experimentation, statistical analysis, numerical modeling, and machine learning approaches forms key components of our research efforts and will be discussed in this presentation. I aim for this talk to inspire enthusiasm and ideas for future collaborations in this dynamic field.
Bio: Dr. Yijin Liu received his B.S. (2004) and Ph.D. (2009) degrees from the Physics Department at the University of Science & Technology of China. He joined Stanford University as a postdoctoral scholar in 2009 and became an Associate Staff Scientist at the SLAC National Accelerator Laboratory in 2012, a Staff Scientist in 2015, and a Lead Scientist in 2020. In August 2023, Dr. Liu joined the Walker Department of Mechanical Engineering at UT Austin as an Associate Professor. In his previous role as a National Lab Scientist, Dr. Liu led the technical developments and scientific applications for the Transmission X-ray Microscopy program at SLAC/Stanford. With over 15 years of experience in this field, Dr. Liu has developed and applied X-ray characterization methods for a broad range of research fields. In more recent years, Dr. Liu’s research focused on studying energy storage materials using high-throughput experimental methods as well as machine learning-assisted statistical analysis. Specific areas of focus include battery manufacturing, safety, degradation, and failure analysis.