Huang Renzhi, Guo Xin, Chen Binbin, Ma Mengying, Chen Qinlong, Zhang Canfu, Liu Yingchun, Kong Xueqian, Fan Xiulin, Wang Linjun, Ling Min, Pan Huilin
Department of Chemistry, Zhejiang University, Hangzhou 310012, China.
Zhejiang Provincial Key Laboratory of Advanced Chemical Engineering Manufacture Technology, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, China.
JACS Au. 2024 May 3;4(5):1986-1996. doi: 10.1021/jacsau.4c00196. eCollection 2024 May 27.
Developing advanced electrolytes has been regarded as a pivotal strategy for enhancing the electrochemical performance of batteries; however, the criteria for electrolyte design remain elusive. In this study, we present an electrolyte design chart reframed through intermolecular interactions. By combining systematic nuclear magnetic resonance, Fourier transform infrared measurements, molecular dynamics (MD) simulations, and machine-learning-assisted classifications, we establish semiquantitative correlations between electrolyte components and the electrochemical reversibility of electrolytes. We propose the equivalent increment of Li salt resulting from functional cosolvent and solvent-solvent interactions for effective electrolyte design and prediction. The controllable regulation of the electrolyte design chart by the properties of solvent-solvent interactions presents varying equivalent effects of increasing Li salt concentrations in different electrolyte systems. Based on this mechanism, we demonstrate highly reversible and nonflammable phosphate-based electrolytes for graphite||NCM811 full cells. The proposed electrolyte design chart, semiquantitatively determined by intermolecular interactions, provides the necessary experimental foundation and basis for the future rapid screening and prediction of electrolytes using machine-learning methods.
开发先进的电解质被视为提高电池电化学性能的关键策略;然而,电解质设计的标准仍然难以捉摸。在本研究中,我们展示了一种通过分子间相互作用重新构建的电解质设计图。通过结合系统的核磁共振、傅里叶变换红外测量、分子动力学(MD)模拟和机器学习辅助分类,我们建立了电解质成分与电解质电化学可逆性之间的半定量相关性。我们提出了由功能性共溶剂和溶剂 - 溶剂相互作用导致的锂盐等效增量,用于有效的电解质设计和预测。通过溶剂 - 溶剂相互作用的性质对电解质设计图进行可控调节,在不同电解质体系中呈现出增加锂盐浓度的不同等效效果。基于此机制,我们展示了用于石墨||NCM811全电池的高可逆性和不可燃的磷酸盐基电解质。所提出的由分子间相互作用半定量确定的电解质设计图,为未来使用机器学习方法快速筛选和预测电解质提供了必要的实验基础。