Grain Boundary Electronic Insulation for High‐Performance All‐Solid‐State Lithium Batteries
Bibliographic record
Abstract
Abstract Sulfide electrolytes with high ionic conductivities are one of the most highly sought for all‐solid‐state lithium batteries (ASSLBs). However, the non‐negligible electronic conductivities of sulfide electrolytes (≈10 −8 S cm −1 ) lead to electron smooth transport through the sulfide electrolyte pellets, resulting in Li dendrite directly depositing at the grain boundaries (GBs) and serious self‐discharge. Here, a grain‐boundary electronic insulation (GBEI) strategy is proposed to block electron transport across the GBs, enabling Li−Li symmetric cells with 30 times longer cycling life and Li−LiCoO 2 full cells with three times lower self‐discharging rate than pristine sulfide electrolytes. The Li−LiCoO 2 ASSLBs deliver high capacity retention of 80 % at 650 cycles and stable cycling performance for over 2600 cycles at 0.5 mA cm −2 . The innovation of the GBEI strategy provides a new direction to pursue high‐performance ASSLBs via tailoring the electronic conductivity.
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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".