Precise Tailoring of Lithium‐Ion Transport for Ultralong‐Cycling Dendrite‐Free All‐Solid‐State Lithium Metal Batteries
Bibliographic record
Abstract
Abstract All‐solid‐state lithium metal batteries can address crucial challenges regarding insufficient battery cycling life and energy density. The demonstration of long‐cycling dendrite‐free all‐solid‐state lithium metal batteries requires precise tailoring of lithium‐ion transport of solid‐state electrolytes (SSEs). In this work, a proof of concept is reported for precise tailoring of lithium‐ion transport of a halide SSE, Li 3 InCl 6 , including intragranular (within grains) but also intergranular (between grains) lithium‐ion transport. Lithium‐ion migration tailoring mechanism in crystals is developed by unexpected enhanced Li, In, and Cl vacancy populations and lower energy barrier for hopping. The lithium‐ion transport tailoring mechanism between the grains is determined by the elimination of voids between grains and the formation of unexpected supersonic conducting grain boundaries, boosting the lithium dendrite suppression ability of SSE. Due to boosted lithium‐ion conduction and dendrite‐suppression ability, the all‐solid‐state lithium metal batteries coupled with Ni‐rich LiNi 0.83 Co 0.12 Mn 0.05 O 2 cathodes and lithium metal anodes demonstrate breakthroughs in electrochemical performance by achieving extremely long cycling life at a high current density of 0.5 C (2000 cycles, 93.7% capacity retention). This concept of precise tailoring of lithium‐ion transport provides a cost, time, and energy efficient solution to conquer the remaining challenges in all‐solid‐state lithium‐metal batteries for fast developing electric vehicle markets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".