Machine learning-assisted benign transformation of three zinc states in zinc ion batteries
Energy Environ. Sci., 2025, 18,4872-4882DOI: 10.1039/D5EE00650C, Paper Open Access   This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.Jianbo Dong, Guolang Zhou, Wenhao Ding, Jiayi Ji, Qing Wang, Tianshi Wang, Lili Zhang, Xiuyang Zou, Jingzhou Yin, Edison Huixiang AngA machine-learning-designed cerium-iron MOF layer enhances Zn anode stability, achieving over 4300 hours at 1 mA cm−2 and 99.8% coulombic efficiency over 1400 cycles at 2 mA cm−2, providing a cost-effective protective strategy.The content of this RSS Feed (c) The Royal Society of Chemistry
Energy Environ. Sci., 2025, 18,4872-4882
DOI: 10.1039/D5EE00650C, Paper
DOI: 10.1039/D5EE00650C, Paper
Open Access
This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.Jianbo Dong, Guolang Zhou, Wenhao Ding, Jiayi Ji, Qing Wang, Tianshi Wang, Lili Zhang, Xiuyang Zou, Jingzhou Yin, Edison Huixiang Ang
A machine-learning-designed cerium-iron MOF layer enhances Zn anode stability, achieving over 4300 hours at 1 mA cm−2 and 99.8% coulombic efficiency over 1400 cycles at 2 mA cm−2, providing a cost-effective protective strategy.
The content of this RSS Feed (c) The Royal Society of Chemistry
A machine-learning-designed cerium-iron MOF layer enhances Zn anode stability, achieving over 4300 hours at 1 mA cm−2 and 99.8% coulombic efficiency over 1400 cycles at 2 mA cm−2, providing a cost-effective protective strategy.
The content of this RSS Feed (c) The Royal Society of Chemistry
