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    Highly Potent and Low-Volume Concentration Additives for Durable Aqueous Zinc Batteries: Machine Learning-Enabled Performance Rationalization

    Year: 2024

    Authors: Shang, Yuan; Kundi, Varun; Pal, Ipsita; Kim, Ha Na; Zhong, Haoyin; Kumar, Priyank; Kundu, Dipan

    Keywords: aqueous zinc batteries; dynamic SEI; electrolyte additives; low additive concentration; machine learning; practical evaluation; Zn metal anode

    The essential virtues of aqueous zinc battery chemistry stem from the energy-dense zinc metal anode and mild aqueous electrolytes. Yet, their incompatibility – as exposed by zinc's corrosion and associated dendrite problem – poses a challenge to achieving improved cycle life under practically relevant parameters. While electrolyte additives are a scalable strategy, additives that can function at low volume concentrations remain elusive. Here, through screening alkanol and alkanediol chemistries, 1,2-butanediol and pentanediol are unveiled as highly potent additives, which operate at a practical 1 volume% concentration owing to their ability to furnish dynamic solid–electrolyte interphase through pronounced interfacial filming. This unique mechanistic action renders effective corrosion and dendrite mitigation, resulting in up to five to twenty-fold zinc cyclability enhancement with a high Coulombic efficiency (up to 99.9%) and improved full-cell performance under demanding conditions, including at elevated temperatures. A machine learning-based analysis is presented to rationalize the additive performance relative to critical physicochemical descriptors, which can pave the way for a rational approach to efficient additive discoveries.
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