Dr. Laisuo Su, Postdoc Research Associate from Department of Mechanical Engineering, University of Texas at Austin, will give a seminar titled "Materials Compatibility Engineering for Advanced Renewable Energy Devices" to the interested students and faculty at Discovery Park.

Abstract

Energy crises, climate change, and air pollution are three key challenges that we are facing in building a sustainable society. Replacing fossil fuels with renewable energy systems, such as solar cells and batteries, offers a highly scalable pathway toward sustainability. Because of this, tremendous effort has been put into developing batteries with high energy and power density, long cycle life, low cost, and good safety in the past decades. Here, materials innovation plays a major role. But the main challenge lies in not only developing high-performance materials but also achieving chemical, electrochemical, and mechanical compatibility among these materials when putting them together in a functional device. In this talk, I will introduce a few key strategies to improve battery materials compatibility, including surface and interface engineering of battery electrodes and developing advanced electrolytes. These strategies allow us to utilize high-performance materials that are unstable by themselves for developing next-generation batteries, such as Li metal and Si anodes and high-Ni cathodes. I will also introduce the application of the design of experiments techniques and machine learning algorithms to accelerate materials innovation for developing next-generation batteries.

Biography

Laisuo Su received his M.S. degree from Tsinghua University (China) in 2016 and his Ph.D. degree from Carnegie Mellon University in 2021. Then, he joined Professor Arumugam Manthiram's group at the University of Texas at Austin as a postdoctoral fellow, and currently, he is a research associate. His research focuses on surface and interface engineering of battery electrodes, advanced electrolyte development for next-generation batteries, and incorporating data-driven methods and machine learning to predict battery performance.