Incombustible Polymer Electrolyte Boosting Safety of Solid‐State Lithium Batteries: A Review
Bibliographic record
Abstract
Abstract Lithium‐ion batteries with their portability, high energy density, and reusability are frequently used in today's world. Under extreme conditions, lithium‐ion batteries leak, burn, and even explode. Therefore, improving the safety of lithium‐ion batteries has become a focus of attention. Researchers believe using a solid electrolyte instead of a liquid one can solve the lithium battery safety issue. Due to the low price, good processability and high safety of the solid polymer electrolytes, increasing attention have been paid to them. However, polymer electrolytes can also decompose and burn under extreme conditions. Moreover, lithium dendrites are formed continuously due to the uneven charge distribution on the surface of the lithium metal anode. A short circuit caused by a lithium dendrite can cause the battery to thermal runaway. As a result, the safety of polymer solid‐state batteries remains a challenge. In this review, the thermal runaway mechanism of the batteries is summarized, and the batteries abuse test standard is introduced. In addition, the recent works on the high‐safety polymer electrolytes and the solution strategies of lithium anode problems in polymer batteries are reviewed. Finally, the development direction of safe polymer solid lithium batteries is prospected.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".