Fabrication of a Dendrite‐Free all Solid‐State Li Metal Battery via Polymer Composite/Garnet/Polymer Composite Layered Electrolyte
Bibliographic record
Abstract
Abstract While all‐solid‐state Li metal batteries based on ceramic solid‐electrolytes offer higher energy density and better safety features over their liquid counterparts, critical challenges in their design such as high electrode–electrolyte interface resistance and formation of Li‐dendrites still remain unsolved. To address the issues, an intimate contact between Li and the solid‐state electrolyte is necessary. Herein, a flexible and mechanically robust polymer membrane comprising of poly(ethylene oxide), lithium perchlorate, and garnet particles is used as an interlayer between Li metal and garnet ceramic electrolyte. The Li salt enhances the ionic conductivity of the membranes and ensures their flexible nature while garnet particles enhance their mechanical strength. The cells comprising of composite membranes results in four times smaller charge transfer resistance at the interface and demonstrate stable and reversible Li plating/stripping voltage profiles. Further, the polymer composite membrane mechanically blocks the formation of Li dendrites at reasonably high currents (0.1 mA cm −2 ) in the structures even after prolonged cycling (140 h) owing to their enhanced toughness. With smaller charge transfer resistance (≈400 Ω cm 2 at room temperature), stability at extended periods of cycling and no Li‐dendrite formations, the structures can prove a viable electrolyte candidate for advanced solid‐state Li 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) | 0.000 | 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".