Charting the known chemical space for non-aqueous lithium–air battery electrolyte solvents
Literature Information
Tamara Husch, Martin Korth
Li–air batteries are very promising candidates for powering future mobility, but finding a suitable electrolyte solvent for this technology turned out to be a major problem. We present a systematic computational investigation of the known chemical space for possible Li–air electrolyte solvents. It is shown that the problem of finding better Li–air electrolyte solvents is not only – as previously suggested – about maximizing Li+ and O2− solubilities, but also about finding the optimal balance of these solubilities with the viscosity of the solvent. As our results also show that trial-and-error experiments on known chemicals are unlikely to succeed, full chemical sub-spaces for the most promising compound classes are investigated, and suggestions are made for further experiments. The proposed screening approach is transferable and robust and can readily be applied to optimize electrolytes for other electrochemical devices. It goes beyond the current state-of-the-art both in width (considering the number of compounds screened and the way they are selected), as well as depth (considering the number and complexity of properties included).
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Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.
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