ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026
Microstructure-Resolved Modeling to Predicting and Regulating Lithium Plating-Stripping Dynamics on Graphite Electrodes.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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10 authors.
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Abstract
The lithium plating reaction in graphite electrodes acts as a root cause for the accelerated degradation and the internal short circuits in lithium-ion batteries. Here, an electrochemical model based on multi-scale microstructural images was established to identify lithium plating-stripping processes, thereby supporting the predictive outcomes of electrochemical monitoring techniques. Experiments revealed that the open-circuit voltage differential curve (dOCV/dt) led to ambiguous delineation of the safe state-of-charge (SOC) operating range. The established lithium plating-stripping model was used to compare with experimental results, revealing the dynamic evolution of electrode-scale kinetics and quantified the impact of lithium metal residue on electrode performance. Ex situ X-ray computed tomography (XCT) captured micrometer-resolution microstructural details of graphite electrodes and plated lithium, enabling further correlation of spatially heterogeneous lithium plating-stripping reactions with electrode microstructure. The sensitivity of lithium plating to electrode microstructure was examined at the particle scale, attributed to competition between electrode kinetic rates and active reaction areas. Theoretical mechanism analysis and experimental results from high-energy-density electrodes demonstrated that positioning small particles on the current collector side effectively mitigates solid-state diffusion polarization while confining side reactions to a limited area. The integration of experiments and multiscale modeling elucidates the relationship between lithium plating-stripping reactions and electrode structure, providing mechanistic insights for similar structural optimization designs.
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