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机器学习引导的溶解工程,用于耐用中性水性有机流动电池的奇拉尔生物体

Xu Liu¹, Haiyan Yu¹, Xiaotong Deng¹

¹Frontier Institute of Science and Technology, Interdisciplinary Research Center of Frontier science and technology, State Key Laboratory for Strength and Vibration of Mechanical Structures, Institute of New Concept Sensors and Molecular Materials, Shaanxi Key Laboratory of New Conceptual Sensors and Molecular Materials, Engineering Research Center of Key Materials for Efficient Utilization of Clean Energy of Shaanxi Province, National innovation Platform (Center) for Industry-Education Integration of Energy Storage Technology, Xi'an Key Laboratory of Electronic Devices and Material Chemistry, School of Physics, Xi'an Jiaotong University, Xi'an, Shaanxi Province, 710049, China.

Angewandte Chemie (International ed. in English)

|December 31, 2025

概括

机器学习预测了水性有机氧化还原流电池 (AORFB) 的稳定性生物原体. 这些新的电解质克服了可溶性极限和降解,实现了电网规模储能的创纪录稳定性和性能.

科学领域:

电化学电化学电化学
材料科学材料科学材料科学
机器学习机器学习

背景情况:

在水性有机氧化还原流电池 (AORFB) 中常规的N-化生物电解质遭受核性脱基化降解.
在高度AORFB系统中同时实现高溶解度和稳定性仍然是一个重大挑战.
试错分子设计方法已被证明不足以优化电解质性能.

研究的目的:

开发一种机器学习 (ML) 策略,用于为AORFBs设计新型,稳定和高度溶解的生物电解质.
为了解决高度AORFB系统中的可溶性-稳定性权衡问题.
为 ML 支持的电解质开发建立一个可通用的平台,以便实际部署 AORFB.

主要方法:

在1300多个AORFB研究中训练了大型语言模型 (LLM),以预测电解质特性.
设计和合成了带有 орто-二基基基因的性生物素,形成了适应pH的"溶解盔甲".
采用分子模拟和现场光谱来确认结构稳定机制.
在各种度下对R-/S-enantiomers和racemates进行了广泛的电化学循环测试.
通过2.5公斤规模合成和Ah级堆测试验证实了该策略.

主要成果:

带有ortho-dihydroxy图案的性生物原体显示出与种族化合物相比溶解度高1.66倍.
"溶解盔甲"保护了反应性C-N键,增强了不依赖于性而达到pH11的稳定性.
一个基于1M R的氧化还原对在3652个循环中实现了99.42%的容量保留记录.
基于1M R的AORFB在533个周期中显示了100%的保留率,超过了现有的viologen电解质.
从0.1到2.5M的度实现了稳定的循环,将降解与度脱.
通过大规模合成和堆测试 (98.65%在77个周期内保持) 证实了工业可扩展性.

结论:

基于ML的设计策略成功地为AORFBs创建了稳定,高溶解度的生物电解质.
орто-dihydroxy 图案提供了一个动态的"溶解盔甲",对电解质稳定性和性能至关重要.
这项工作建立了一个可扩展的,支持ML的平台,用于开发先进的电解质,为实际的AORFB应用铺平了道路.

关键词:

抗性抗性抗性基拉尔的生化物.机器学习就是机器学习.有机流动电池有机流动电池溶解效应是一种溶解效应.

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