Fast Ion Transport in Li‐Rich Alloy Anode for High‐Energy‐Density All Solid‐State Lithium Metal Batteries
Bibliographic record
Abstract
Abstract All‐solid‐state Li batteries (ASSLBs) with solid‐polymer electrolytes are considered promising battery systems to achieve improved safety and high energy density. However, Li dendrite formation at the Li anode under high charging current density/capacity has limited their development. To tackle the issue, Li‐metal alloying has been proposed as an alternative strategy to suppress Li dendrite growth in ASSLBs. One drawback of alloying is the relatively lower operating cell voltages, which will inevitably lower energy density compared to cells with pure Li anode. Herein, a Li‐rich Li13In3 alloy electrode (LiRLIA) is proposed, where the Li13In3 alloy scaffold guides Li nucleation and hinders Li dendrite formation. Meanwhile, the free Li can recover Li's potential and facilitate fast charge transfer kinetics to realize high‐energy‐density ASSLBs. Benefitting from the stronger adsorption energy and lower diffusion energy barrier of Li on a Li13In3 substrate, Li prefers to deposit in the 3D Li13In3 scaffold selectively. Therefore, the Li–Li symmetric cell constructed with LiRLIA can operate at a high current density/capacity of 5 mA cm−2/5 mAh cm−2 for almost 1000 h.
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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".