Exploration of Metal Alloys as Zero‐Resistance Interfacial Modification Layers for Garnet‐Type Solid Electrolytes
Bibliographic record
Abstract
Abstract A solid‐state battery with a lithium‐metal anode and a garnet‐type solid electrolyte has been widely regarded as one of the most promising solutions to boost the safety and energy density of current lithium‐ion batteries. However, lithiophobic property of garnet‐type solid electrolytes hinders the establishment of a good physical contact with lithium metal, bringing about a large lithium/garnet interfacial resistance that has remained as the greatest issue facing their practical application in solid‐state batteries. Herein, a melt‐quenching approach is developed by which varieties of interfacial modification layers based on metal alloys can be coated uniformly on the surface of the garnet. It is demonstrated that with an ultrathin, lithiophilic AgSn0.6Bi0.4Ox coating the interfacial resistance can be eliminated, and a dendrite‐free lithium plating and stripping on the lithium/garnet interface can be achieved at a high current density of 20 mA cm−2. The results reveal that the uniform coating on the garnet surface and the facile lithium diffusion through the coating layer are two major reasons for the excellent electrochemical performances. The all‐solid‐state full cell consisting of the surface modified garnet‐type solid electrolyte with a LiNi0.8Mn0.1Co0.1O2 cathode and a lithium–metal anode maintains 86% of its initial capacity after 1000 stable cycles at 1 C.
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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) | 0.000 | 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".