Synergistic Approach toward Developing Highly Compatible Garnet‐Liquid Electrolyte Interphase in Hybrid Solid‐State Lithium‐Metal Batteries
Bibliographic record
Abstract
Abstract The hybrid solid‐liquid electrolyte concept is one of the best approaches for counteracting the interface problems between solid electrolytes and Li anodes/cathodes. However, a solid‐liquid electrolyte layer forming at the interfaces degrades battery capacity and power during a longer cycle due to highly reactive chemical and electrochemical reactions. To solve this problem in the present study, a synthetic approach is demonstrated by combining AlCl3 Lewis acid and fluoroethylene carbonate as additives in a conventional LiPF6‐containing carbonate‐based electrolyte. This electrolyte design triggers the fluoroethylene carbonate polymerization by AlCl3 addition and can also form a mechanically robust and ionically conductive Al‐rich interphase on the surface of Li7La2.75Ba0.25Zr1.75Ta0.25O12 garnet‐type structured solid electrolytes, Li anodes and LiNi0.6Mn0.2Co0.2O2 cathodes. Benefitting from this approach, the assembled Li symmetric cell exhibits a remarkably high critical current density of 4.2 mA cm−2, and stable long‐term cycling over 3000 h at 0.5 mA cm−2 at 25 °C. The assembled hybrid full cell shows an impressive specific capacity retention of 92.2% at 1 C till 200 cycles. This work opens a new direction in developing safe, long‐lasting, and high‐energy hybrid solid‐state lithium‐metal batteries.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".