Robotics & Machine Learning Daily News2024,Issue(Dec.4) :142-143.

New Machine Learning Data Have Been Reported by Investigators at Iowa State Univ ersity (Flexible Doorway Controlled Na+ Ion Diffusion In Napso Glassy Electrolyt es From Machine-learning Force Field Simulations)

爱荷华州立大学的研究人员报告了新的机器学习数据(来自机器学习力场模拟的柔性门控钠离子在Napso玻璃电解质中的扩散)

Robotics & Machine Learning Daily News2024,Issue(Dec.4) :142-143.

New Machine Learning Data Have Been Reported by Investigators at Iowa State Univ ersity (Flexible Doorway Controlled Na+ Ion Diffusion In Napso Glassy Electrolyt es From Machine-learning Force Field Simulations)

爱荷华州立大学的研究人员报告了新的机器学习数据(来自机器学习力场模拟的柔性门控钠离子在Napso玻璃电解质中的扩散)

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摘要

由一名新闻记者-机器人与机器学习日报的工作人员新闻编辑每日新闻-机器学习的最新研究结果已经发表。据新闻报道由NewsR X通讯员从爱荷华州艾姆斯发起,研究称,“全固态钠电池”(ASSSBs),使用不易燃固体电解质(SSEs)和丰富的钠金属阳极有吸引力的候选人安全,成本效益高的电网规模能源愤怒。最近的研究表明氧气掺杂提高了Na3PS4-xOx(NaPSO)的离子导电性、机械强度和成形性玻璃质固态电解质(GSEs),为开发持久、能量密集、和负担得起的ASSB。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Current study results on Machine Learn ing have been published. According to newsoriginating from Ames, Iowa, by NewsR x correspondents, research stated, “All-solid-state sodium batteries(ASSSBs), f eaturing nonflammable solid-state electrolytes (SSEs) and abundant sodium metal anodes, areattractive candidates for safe, cost-effective grid-scale energy sto rage. Recent research shows that oxygendoping increases the ionic conductivity, mechanical strength, and formability of Na3PS4-xOx (NaPSO)glassy solid-state e lectrolytes (GSEs), offering a promising approach for developing durable, energy -dense,and affordable ASSSBs.”

Key words

Ames/Iowa/United States/North and Cen tral America/Chalcogens/Cyborgs/Electrolytes/Emerging Technologies/Inorgani c Chemicals/Machine Learning/Iowa State University

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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