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Updated: Jun 20, 2026

Identification and Quantification of Decomposition Mechanisms in Lithium-Ion Batteries; Input to Heat Flow Simulation for Modeling Thermal Runaway
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Identification and Quantification of Decomposition Mechanisms in Lithium-Ion Batteries; Input to Heat Flow Simulation for Modeling Thermal Runaway

Published on: March 7, 2022

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利用机器学习以选择性CO2选金属有机框架,识别离子电池中早期的热逃逸

Xian Wei1, Xin Li2, Xiong Wang3

  • 1College of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications, Nanjing 210023, China.

Nanomaterials (Basel, Switzerland)
|February 26, 2026
PubMed
概括

研究人员选了1470个金属有机框架 (MOFs),以找到可以选择性检测二氧化碳 (CO2) 的材料,以提高离子电池的安全性. AJOTEY显示出最佳性能,为开发先进的电池监控传感器提供了一条实用途径.

关键词:
二氧化碳的选择性识别.机器学习是机器学习.金属有机框架的框架.多元组件吸附方式多元组件吸附热逃跑预警 热逃跑预警 热逃跑预警

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