Designing Polymer Electrolytes via Ring‐Opening Polymerization for Advanced Lithium Batteries
Bibliographic record
Abstract
Abstract Replacing liquid electrolytes with solid‐state polymer electrolytes (SPEs) can solve the safety hazards of Li metal batteries (LMBs) while increasing their energy density. However, there has been limited success so far in preparing advanced SPEs with controllable molecular structure and chemical composition, posing great obstacles to further promoting its application in LMBs. Recently, ring‐opening polymerization (ROP), including cationic ROP, anionic ROP, and ring‐opening metathesis polymerization, has become a dazzling new star in achieving SPEs due to its mild polymerization conditions and controllable chemical composition (molecular structure, functional group), etc. Besides, there is no small molecule released during the polymerization process, which means reduced interfacial side reaction. Hence, in this review, the merits of ROP in preparing SPEs and its mechanism as well as interfering factors, etc are evaluated from the perspective of synthetic chemistry. Furthermore, the review focuses on outlining the existing cases related to ROP as much as possible and summarize them from different ring structures (from triple ring to multivariate ring) and polymerization methods, hoping to provide a comprehensive understanding and serve as strategic guidance for designing high‐performance SPEs. Challenges and opportunities regarding this burgeoning field are also discussed at the end.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".