Switching of Redox Levels Leads to High Reductive Stability in Water-in-Salt Electrolytes

Abstract

Developing nonflammable electrolytes with wide electrochemical windows is of great importance for energy storage devices. Water-in-salt electrolytes (WiSE) have attracted great interests due to their widely opened electrochemical windows and high stability. Previous theoretical investigations have revealed changes in solvation shell of water molecules result in opening of HOMO–LUMO gaps of water, leading to the formation of an anion-derived solid-electrolyte-interphase (SEI) in WiSE. However, how solvation structures affect electrochemical windows at atomic level is still a puzzle, which hinders optimization and design of aqueous electrolytes. Herein, machine learning molecular dynamics and free energy calculation method are applied to compute redox potentials of anions of Li-salts and water of aqueous electrolytes at a range of salt concentrations. Furthermore, an analysis based on local solvation structures is employed to demonstrate the structure–property relations. Our calculation shows that the hydrogen evolution reaction is impeded in WiSE due to switching of the order of redox levels of the anion and H2O, leading to formation of SEI and high reductive stability. Level switching is caused by the special solvation environments of isolated water molecules. Our work provides new insight into the electrochemistry of aqueous electrolytes which would benefit the electrolyte design in energy storage devices.

Nanostructure-Based Plasmon-Enhanced Raman Spectroscopic Strategies for Characterization of the Solid–Electrolyte Interphase: Opportunities and Challenges

“…This will facilitate operando investigations under conditions consistent with or very close to actual battery operation, providing information from multiple perspectives. An interesting and important direction worth mentioning is the application of artificial intelligence (AI) in characterization of Li-based batteries. AI has emerged as a promising approach that could revolutionize the current paradigm of battery research and development into the fourth paradigm, i.e., data-intensive scientific discovery. , Especially, the machine learning (ML), as a fruitful branch of AI, has already been deployed in battery research to assist in the design of electrode materials and electrolytes. − In recent years, significant efforts have also been devoted to predicting electrolyte decomposition pathway and understanding of formation mechanism related to the SEI through ML. − These AI-based studies offer more accurate qualitative and quantitative methods associated with experimental design and computational chemistry to screen and analyze large amounts of data generated from experiments and simulations, identifying patterns and relationships that can provide insights into the underlying reaction mechanisms related to SEI formation and evolution. This possesses significant advantages over traditional trial-and-error approaches.…”

Constant-potential molecular dynamics simulation and its application in rechargeable batteries

“…Thirdly, constant-potential MD modeling of battery interfaces can be combined with machine learning potentials (MLPs), which would hopefully lead to fast computation as in CMD and high electronic accuracy as in AIMD at the same time. 6,7,[88][89][90][91] Currently, MLPs have already been extensively utilized in the study of bulk liquid 92,93 and solid battery materials, [94][95][96] especially to investigate their transport properties. 91,[97][98][99] Some studies have demonstrated the possibility to model interfaces with MLPs, [100][101][102][103][104][105] even though these interfaces are not held at constant potentials and may not be related to batteries.…”

Advancing Accurate and Efficient Surface Behavior Modeling of Al Clusters with Machine Learning Potential

“…To date, various approaches have been proposed to construct MLPs, including the Behler–Parrinello neural network, Gaussian approximation potential, gradient-domain method, and Deep Potential-Smooth Edition (DeepPot-SE) approach . However, existing MLPs have been developed primarily for bulk, – flat surface, and interface, – with little attention given to studying the clusters on the surface, particularly clusters of varying sizes on surfaces. To gain atomic insight into various surface behaviors exhibited by adsorbed clusters, there is an urgent need to develop specialized MLPs that can precisely depict adsorbed clusters of varying sizes.…”