Suppr超能文献

解析固态锂金属电池中用于预测性失效缓解的电镀/剥离诱导应变演变的嵌入式传感器。

Unraveling plating/stripping-induced strain evolution embedded sensors for predictive failure mitigation in solid-state Li metal batteries.

作者信息

Zhang Hongye, Chen Zhipeng, Zhang Xinren, Shen Ziyi, Xu Fei, Wang Fenghui

机构信息

Department of Engineering Mechanics, Bio-Inspired and Advanced Energy Research Center, Northwestern Polytechnical University Xi'an 710129 Shaanxi China

State Key Laboratory of Solidification Processing, Center for Nano Energy Materials, School of Materials Science and Engineering, Northwestern Polytechnical University China

出版信息

Chem Sci. 2025 Jul 29;16(34):15697-15706. doi: 10.1039/d5sc03046c. eCollection 2025 Aug 27.

Abstract

Solid-state lithium metal batteries represent a critical frontier in energy storage technology, yet persistent interfacial instability between the Li metal anode and solid electrolytes generates detrimental electrochemical-mechanical interactions that undermine the cycling durability. To resolve this fundamental challenge, herein, we establish an innovative real-time strain monitoring that directly correlates micro-mechanical evolution with interfacial degradation during Li plating/stripping. It reveals that Li plating induces significant microstrain accumulation, while stripping processes only partially release mechanical stress. Systematic analysis identifies three characteristic strain evolution periods during cycling: initial linear growth, intermediate stabilization, and terminal exponential escalation prior to cell failure. Post-mortem characterization attributes the final strain surge to synergistic cumulative dendrite propagation, dead lithium agglomeration, and SEI disintegration. Parametric studies demonstrate that an elevated cell stack pressure from 200 KPa to 3500 KPa reduced the initial strain growth cycle by 50% and stabilized the strain value by 47.4%, whereas doubling the current density prolongs the 10-fold linear growth period and increases the 4-fold plateau strain one, severely curtailing battery longevity. Crucially, we establish predictive correlations between strain trajectory patterns and electrochemical failure signature. This mechano-analytical platform enables non-destructive interrogation of interfacial dynamics, providing an operational protocol for failure diagnosis and pressure-current parameter optimization to achieve durable solid-state battery systems.

摘要

固态锂金属电池是储能技术的一个关键前沿领域,但锂金属负极与固体电解质之间持续存在的界面不稳定性会产生有害的电化学-机械相互作用,从而破坏循环耐久性。为了解决这一根本挑战,在此我们建立了一种创新的实时应变监测方法,该方法直接将微机械演变与锂电镀/剥离过程中的界面降解联系起来。结果表明,锂电镀会导致显著的微应变积累,而剥离过程只能部分释放机械应力。系统分析确定了循环过程中的三个特征应变演变阶段:初始线性增长、中间稳定以及电池失效前的最终指数级上升。事后表征将最终的应变激增归因于协同累积的枝晶生长、死锂团聚和固体电解质界面(SEI)解体。参数研究表明,将电池堆叠压力从200千帕提高到3500千帕,可使初始应变增长周期缩短50%,并使应变值稳定47.4%,而将电流密度加倍会使线性增长周期延长10倍,并使平台应变值增加4倍,严重缩短电池寿命。至关重要的是,我们建立了应变轨迹模式与电化学失效特征之间的预测相关性。这个机械分析平台能够对界面动力学进行无损探测,为故障诊断和压力-电流参数优化提供了一个操作方案,以实现耐用的固态电池系统。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/10d8/12395013/fe6e9f17a81d/d5sc03046c-f1.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验