Toward Practical Solid‐State Polymer Lithium Batteries by In Situ Polymerization Process: A Review
Bibliographic record
Abstract
Abstract Although there are various strategies for solid‐state polymer lithium batteries (SSPLBs) manufacturing, the most promising is the in situ polymerization process. The in situ polymerization process inherits good liquid electrolyte/electrode interfacial contact and is compatible with existing lithium‐ion batteries manufacturing processes, making it easy to achieve scale‐up production. However, most of the current studies on the in situ polymerization process are based on lab‐level coin cells, while practical pouch cells are much less studied. There is a huge difference between lab‐level coin SSPLBs and practical pouch SSPLBs. Here, as a complement to the existing reports and reviews, a systematic review of the challenges and design principles of in situ polymerization process for fabricating practical pouch SSPLBs is provided to enable a comprehensive understanding and strategic guidance for practical SSPLBs applications. This review thoroughly discusses recent advances regarding the fabrication of SSPLBs using in situ polymerization process and presents the existing challenges and future outlook for the fabrication of practical SSPLBs by in situ polymerization processes. Furthermore, the critical issues of electrode materials for manufacturing practical SSPLBs are highlighted during the in situ polymerization process, and an attempt is made to call more attention to the performance of the practical pouch SSPLBs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".