Insights into the solvation chemistry in liquid electrolytes for lithium-based rechargeable batteries

Abstract

Lithium-based rechargeable batteries have dominated the energy storage field and attracted considerable research interest due to their excellent electrochemical performance. As indispensable and ubiquitous components, electrolytes play a pivotal role in not only transporting lithium ions, but also expanding the electrochemical stable potential window, suppressing the side reactions, and manipulating the redox mechanism, all of which are closely associated with the behavior of solvation chemistry in electrolytes. Thus, comprehensively understanding the solvation chemistry in electrolytes is of significant importance. Here we critically reviewed the development of electrolytes in various lithium-based rechargeable batteries including lithium–metal batteries (LMBs), nonaqueous lithium-ion batteries (LIBs), lithium–sulfur batteries (LSBs), lithium–oxygen batteries (LOBs), and aqueous lithium-ion batteries (ALIBs), and emphasized the effects of interactions between cations, anions, and solvents on solvation chemistry, and functions of solvation chemistry in different types of electrolytes (strong solvating electrolytes, moderate solvating electrolytes, and weak solvating electrolytes) on the electrochemical performance and redox mechanism in the abovementioned rechargeable batteries. Specifically, the significant effects of solvation chemistry on the stability of electrode–electrolyte interphases, suppression of lithium dendrites in LMBs, inhibition of the co-intercalation of solvents in LIBs, improvement of anodic stability at high cut-off voltages in LMBs, LIBs and ALIBs, regulation of redox pathways in LSBs and LOBs, and inhibition of hydrogen/oxygen evolution reactions in LOBs are thoroughly summarized. Finally, the review concludes with a prospective outlook, where practical issues of electrolytes, advanced in situ/operando techniques to illustrate the mechanism of solvation chemistry, and advanced theoretical calculation and simulation techniques such as “material knowledge informed machine learning” and “artificial intelligence (AI) + big data” driven strategies for high-performance electrolytes have been proposed.