Dendrite nucleation in lithium-conductive ceramics
Literature Information
Solid-state lithium batteries cannot achieve reasonable power densities because of dendrites, whose formation mechanisms remain uncertain. This paper applies principles of chemomechanics to investigate the critical current above which dendrites form in lithium-conductive ceramics. Applied voltage induces stress in solid electrolytes; dendrites appear to nucleate in the exemplary garnet-oxide material Li7La3Zr2O12 (LLZO) when the interfacial pressure exceeds a particular value. The critical pressure of polycrystalline LLZO correlates well with the surface-energy changes incurred by lithium plating in its grain boundaries. A derived formula, validated by experiments, predicts quantitatively how critical current varies with properties including interfacial impedance, bulk permittivity, and grain size. As well as suggesting novel strategies to create more resilient ion-conductive ceramics, the proposed mechanism rationalizes experimental observations of bulk lithium plating and explains how LLZO exhibits an electrically activated transition from stable low-current cyclability to high-current dendrite nucleation.
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Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.










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