Machine Learning Driven Optimization of Electrolyte for Highly Reversible Zn-Air Batteries with Superior Long-Term Cycling Performance.

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Tác giả: Lichao Fu, Zerui Fu, Huaiyun Ge, Xiangrui Gong, Ying Jiang, Dapeng Liu, Mingming Song, Tingting You, Yu Zhang

Ngôn ngữ: eng

Ký hiệu phân loại: 296.83322 Denominations and movements

Thông tin xuất bản: Germany : Advanced materials (Deerfield Beach, Fla.) , 2025

Mô tả vật lý:

Bộ sưu tập: NCBI

ID: 189133

Aqueous alkaline Zn-air batteries (ZABs) have garnered widespread attention due to their high energy density and safety, however, the poor electrochemical reversibility of Zn and low battery round-trip efficiency strongly limit their further development. The manipulation of an intricate microscopic balance among anode/electrolyte/cathode, to enhance the performance of ZABs, critically relies on the formula of electrolytes. Herein, the Bayesian optimization approach is employed to achieve the effective design of optimal compositions of multicomponent electrolytes, resulting in the remarkable enhancement of ZAB performance. Notably, ethylene glycol has been successfully employed as both electrolyte additive and fuel, playing key roles in changing the reaction pathways of ZABs, especially the storage form of discharge products from ZnO deposition on the anode to Zn
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