Thin, Highly Ionic Conductive, and Mechanically Robust Frame‐Based Solid Electrolyte Membrane for All‐Solid‐State Li Batteries
Bibliographic record
Abstract
Abstract A thin but robust solid electrolyte layer is crucial for realizing the theoretical energy density of all‐solid‐state batteries (ASSBs) beyond state‐of‐the‐art Li‐ion batteries (LIBs). This study proposes a simple but practical strategy for fabricating thin solid electrolyte membranes using 5‐µm perforated polyethylene separators with 35% open areas as the supporting component, which ensures mechanical robustness for commercial‐level cell assembly. The thickness of this frame‐based solid electrolyte (f‐SE) membrane can be reduced to ≈45 µm, even after coating the Li6PS5Cl (LPSCl) solid electrolyte composite. Despite a slightly lower ionic conductivity compared to that of thick LPSCl pellets, the f‐SE membranes show high conductance and low overpotential in Li||Li symmetric cells. Their incorporation into LiNi0.7Co0.15Mn0.15O2 full cells increases the reversible capacity and rate capability compared to those of cells with conventional LPSCl pellets. The f‐SE membrane cells exhibit excellent cycling stability over 250 cycles, while maintaining high‐capacity retention and Coulombic efficiency. Notably, the f‐SE membranes significantly increase the energy density of ASSBs (314 Wh kg−1), exceeding the values reported for sulfide‐based cells. These results highlight the crucial role of f‐SE membranes in improving the mechanical properties and energy density of ASSBs, thereby contributing to the development of next‐generation Li battery technologies.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".