An Air‐Stable and Li‐Metal‐Compatible Glass‐Ceramic Electrolyte enabling High‐Performance All‐Solid‐State Li Metal Batteries
Bibliographic record
Abstract
Abstract The development of all‐solid‐state Li metal batteries (ASSLMBs) has attracted significant attention due to their potential to maximize energy density and improved safety compared to the conventional liquid‐electrolyte‐based Li‐ion batteries. However, it is very challenging to fabricate an ideal solid‐state electrolyte (SSE) that simultaneously possesses high ionic conductivity, excellent air‐stability, and good Li metal compatibility. Herein, a new glass‐ceramic Li 3.2 P 0.8 Sn 0.2 S 4 (gc‐Li 3.2 P 0.8 Sn 0.2 S 4 ) SSE is synthesized to satisfy the aforementioned requirements, enabling high‐performance ASSLMBs at room temperature (RT). Compared with the conventional Li 3 PS 4 glass‐ceramics, the present gc‐Li 3.2 P 0.8 Sn 0.2 S 4 SSE with 12% amorphous content has an enlarged unit cell and a high Li + ion concentration, which leads to 6.2‐times higher ionic conductivity (1.21 × 10 −3 S cm −1 at RT) after a simple cold sintering process. The (P/Sn)S 4 tetrahedron inside the gc‐Li 3.2 P 0.8 Sn 0.2 S 4 SSE is verified to show a strong resistance toward reaction with H 2 O in 5%‐humidity air, demonstrating excellent air‐stability. Moreover, the gc‐Li 3.2 P 0.8 Sn 0.2 S 4 SSE triggers the formation of Li–Sn alloys at the Li/SSE interface, serving as an essential component to stabilize the interface and deliver good electrochemical performance in both symmetric and full cells. The discovery of this gc‐Li 3.2 P 0.8 Sn 0.2 S 4 superionic conductor enriches the choice of advanced SSEs and accelerates the commercialization of ASSLMBs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 teacher head, 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".