Phase Evolution of a Prenucleator for Fast Li Nucleation in All‐Solid‐State Lithium Batteries
Bibliographic record
Abstract
Abstract Undesirable Li dendrite growth under high current densities due to the nonuniform Li nucleation and growth has significantly hindered the development of high‐rate all‐solid‐state lithium batteries (ASSLBs). Herein, the phase evolution of a Li prenucleator (MoS2) is shown in working ASSLBs that renders a highly active nucleator (Mo), where Mo promotes fast Li nucleation and Li dendrite suppression. During plating, Li shows strong affinity with Mo, which guides Li fast nucleating and selectively depositing on Mo surface with a large specific surface, thus reducing the local current density. Moreover, a fast diffusion of Li atom on Mo (110) surface promotes uniform Li deposition and limits the Li dendrite growth. Benefitting from the reduced local current density as well as the improved Li dendrite suppression, Li–Li symmetric cells within MoS2 prenucleator demonstrate excellent electrochemical performance, achieving cycle lifetimes as high as 1000 h for 1 mA cm−2/1 mAh cm−2 and 780 h for 0.5 mA cm−2/2 mAh cm−2. Additionally, developed Li‐LFP ASSLBs demonstrate high capacity retention of 78% with an ultra‐long cycling life of 3000 cycles under a high current density of 1 mA cm−2. The general concept has the potential to be extended to other metal‐sulfide prenucleators.
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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".