| 主讲人简介： |
Dr. Leiting Zhang is an assistant professor at the Department of Chemistry - Ångström Laboratory, Uppsala University, Sweden. He received his PhD degree in 2018, under the joint supervision of Prof. Guohua Chen (The Hong Kong University of Science and Technology) and Prof. Jean-Marie Tarascon (Collège de France). Dr. Zhang’s main research interests include mechanistic understanding of aqueous batteries, development and application of operando techniques, and machine learning-assisted lab automation. He has been the first/corresponding author of leading journals such as Advanced Materials, Advanced Energy Materials, and ACS Energy Letters. He has received a starting grant from the Swedish Research Council. He is the recipient of the Bergstedt Prize from the Royal Academy of Sciences in Uppsala, Sweden, and is a finalist for the 2024 Energy Lectureship (early career) from the American Chemical Society. |
| 报告内容简介： |
With the growing global energy demand and the urgent need for sustainable energy solutions, the development of high-performance, environmentally friendly, and economically viable battery technologies has become particularly important. Aqueous batteries have attracted much attention for their safety and environmental friendliness, but their energy density and cycle life still need to be improved. Compared with manual electrolyte optimizations, the integration of automation and data-driven methodologies offers a promising approach. This talk presents a self-driving laboratory framework to accelerate electrolyte discovery by integrating automated coin-cell assembly, galvanostatic cycling of Li4Ti5O12-based organic-aqueous full-cells, Bayesian optimization, and subsequent operando gas analysis. The results highlight the potential of combining Bayesian optimization with autonomous full-cell experimentation, while contributing new electrolyte design insights for next-generation aqueous batteries. |
