Multiscale Simulation of Solid Electrolyte Interface Formation in Fluorinated Diluted Electrolytes with Lithium Anodes

Precycling Strategy in Suitable Voltage to Improve the Stability of Interfacial Film and Suppress the Decline of LiNi0.6Mn0.2Co0.2O2 Cathode at Elevated Temperatures

Layered ternary oxide LiNixMnyCo1-x-yO2 is a promising cathode candidate for high-energy lithium-ion batteries (LIBs). However, the capacity of LIBs is significantly restricted by several factors, including the repeated dissolution-regeneration of the interfacial film at high temperatures, the dissolution of transition metals, and the increase of impedance. Herein, a new precycling strategy in suitable voltage scope at room temperature is proposed to construct a uniform, thermally stable, and insoluble cathode-electrolyte interface (CEI), which helps to maintain stable cycling performances at high temperatures. Specifically, after 5 precycles in the range of 3.85-4.3 V at room temperature, a CEI layer containing numerous inorganic components and oligomers is formed on the surface of LiNi0.6Mn0.2Co0.2O2. Subsequently, the harmful side reactions are effectively suppressed, endowing the cell with an excellent capacity retention of 84.67% after 50 cycles at 0.5C and 55 °C, much higher than that of 65.61% under the conventional film-forming process conditions. This work emphasizes the crucial role of the precycling strategy in regulating the characteristics of CEI layer on the surface of cathode electrode, opening up a new avenue for the high-temperature application of positive electrodes of LIBs.

Insight into uniform filming of LiF-rich interphase via synergistic adsorption for high-performance lithium metal anode

Multi-scale simulation is an important basis for constructing digital batteries to improve battery design and application. LiF-rich solid electrolyte interphase (SEI) is experimentally proven to be crucial for the electrochemical performance of lithium metal batteries. However, the LiF-rich SEI is sensitive to various electrolyte formulas and the fundamental mechanism is still unclear. Herein, the structure and formation mechanism of LiF-rich SEI in different electrolyte formulas have been reviewed. On this basis, it further discussed the possible filming mechanism of LiF-rich SEI determined by the initial adsorption of the electrolyte-derived species on the lithium metal anode (LMA). It proposed that individual LiF species follow the Volmer-Weber mode of film growth due to its poor wettability on LMA. Whereas, the synergistic adsorption of additive-derived species with LiF promotes the Frank-Vander Merwe mode of film growth, resulting in uniform LiF deposition on the LMA surface. This perspective provides new insight into the correlation between high LiF content, wettability of LiF, and highperformance of uniform LiF-rich SEI. It disclosed the importance of additive assistant synergistic adsorption on the uniform growth of LiF-rich SEI, contributing to the reasonable design of electrolyte formulas and high-performance LMA, and enlightening the way for multi-scale simulation of SEI.

Probing the Mechanically Stable Solid Electrolyte Interphase and the Implications in Design Strategies

The inevitable volume expansion of secondary battery anodes during cycling imposes forces on the solid electrolyte interphase (SEI). The battery performance is closely related to the capability of SEI to maintain intact under the cyclic loading conditions, which basically boils down to the mechanical properties of SEI. The volatile and complex nature of SEI as well as its nanoscale thickness and environmental sensitivity make the interpretation of its mechanical behavior many roadblocks. Widely varied approaches are adopted to investigate the mechanical properties of SEI, and diverse opinions are generated. The lack of consensus at both technical and theoretical levels has hindered the development of effective design strategies to maximize the mechanical stability of SEIs. Here, the essential and desirable mechanical properties of SEI, the available mechanical characterization methods, and important issues meriting attention for higher test accuracy are outlined. Previous attempts to optimize battery performance by tuning SEI mechanical properties are also scrutinized, inconsistencies in these efforts are elucidated, and the underlying causes are explored. Finally, a set of research protocols is proposed to accelerate the achievement of superior battery cycling performance by improving the mechanical stability of SEI.

Applying Classical, Ab Initio, and Machine-Learning Molecular Dynamics Simulations to the Liquid Electrolyte for Rechargeable Batteries

Rechargeable batteries have become indispensable implements in our daily life and are considered a promising technology to construct sustainable energy systems in the future. The liquid electrolyte is one of the most important parts of a battery and is extremely critical in stabilizing the electrode-electrolyte interfaces and constructing safe and long-life-span batteries. Tremendous efforts have been devoted to developing new electrolyte solvents, salts, additives, and recipes, where molecular dynamics (MD) simulations play an increasingly important role in exploring electrolyte structures, physicochemical properties such as ionic conductivity, and interfacial reaction mechanisms. This review affords an overview of applying MD simulations in the study of liquid electrolytes for rechargeable batteries. First, the fundamentals and recent theoretical progress in three-class MD simulations are summarized, including classical, ab initio, and machine-learning MD simulations (section 2). Next, the application of MD simulations to the exploration of liquid electrolytes, including probing bulk and interfacial structures (section 3), deriving macroscopic properties such as ionic conductivity and dielectric constant of electrolytes (section 4), and revealing the electrode-electrolyte interfacial reaction mechanisms (section 5), are sequentially presented. Finally, a general conclusion and an insightful perspective on current challenges and future directions in applying MD simulations to liquid electrolytes are provided. Machine-learning technologies are highlighted to figure out these challenging issues facing MD simulations and electrolyte research and promote the rational design of advanced electrolytes for next-generation rechargeable batteries.