Hou Pengfei, Tian Yumiao, Xie Yu, Du Fei, Chen Gang, Vojvodic Aleksandra, Wu Jianzhong, Meng Xing
Key Laboratory of Physics and Technology for Advanced Batteries (Ministry of Education), College of Physics, Jilin University, 130012, Changchun, P. R. China.
Department of Chemical and Biomolecular Engineering, University of Pennsylvania, PA 19104, Philadelphia, USA.
Angew Chem Int Ed Engl. 2023 Aug 7;62(32):e202304205. doi: 10.1002/anie.202304205. Epub 2023 Jun 30.
MXenes are 2D materials with great potential in various applications. However, the degradation of MXenes in humid environments has become a main obstacle in their practical use. Here we combine deep neural networks and an active learning scheme to develop a neural network potential (NNP) for aqueous MXene systems with ab initio precision but low cost. The oxidation behaviors of super large aqueous MXene systems are investigated systematically at nanosecond timescales for the first time. The oxidation process of MXenes is clearly displayed at the atomic level. Free protons and oxides greatly inhibit subsequent oxidation reactions, leading to the degree of oxidation of MXenes to exponentially decay with time, which is consistent with the oxidation rate of MXenes measured experimentally. Importantly, this computational study represents the first exploration of the kinetic process of oxidation of super-sized aqueous MXene systems. It opens a promising avenue for the future development of effective protection strategies aimed at controlling the stability of MXenes.
MXenes是二维材料，在各种应用中具有巨大潜力。然而，MXenes在潮湿环境中的降解已成为其实际应用中的主要障碍。在此，我们结合深度神经网络和主动学习方案，开发了一种用于水性MXene体系的神经网络势（NNP），其具有从头算精度但成本较低。首次在纳秒时间尺度上系统地研究了超大水性MXene体系的氧化行为。MXenes的氧化过程在原子水平上清晰展现。游离质子和氧化物极大地抑制了后续氧化反应，导致MXenes的氧化程度随时间呈指数衰减，这与实验测得的MXenes氧化速率一致。重要的是，这项计算研究代表了对超大尺寸水性MXene体系氧化动力学过程的首次探索。它为未来开发旨在控制MXenes稳定性的有效保护策略开辟了一条充满希望的途径。
