首页|New Machine Learning Findings from Beihang University Described (Design and Opti mization of Multicomponent Electrolytes for Lithium-sulfur Battery: a Machine Le arning Concept and Outlook)
New Machine Learning Findings from Beihang University Described (Design and Opti mization of Multicomponent Electrolytes for Lithium-sulfur Battery: a Machine Le arning Concept and Outlook)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - Researchers detail new data in Machine Learning. According to news reporting fromBeijing, People’s Republic of China, by NewsRx journalists, research stated, “Lithium-sulfur batteries(LSBs) have a ttracted increasing attention in the past decades due to their great potential t o thenext-generation high-energy-density storage systems. As important as elect rodes, electrolytes that couldstrongly determine battery performance via compon ent regulation have left big difficulties in clarifyingtheir complex interactio ns caused by multicomponent as well as the intricate formation mechanism of passivation layers at the electrolyte-electrode interfaces.”
BeijingPeople’s Republic of ChinaAsi aChalcogensCyborgsElectrolytesEmerging TechnologiesInorganic ChemicalsMachine LearningSulfurBeihang University
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