Reducing crystallinity in solid polymer electrolytes for lithium-metal batteries via statistical copolymerization
Bibliographic record
Abstract
Abstract The discovery that polyethylene oxide promotes ionic conductivity led to the development of solid polymer electrolytes. However, their conductivity is severely reduced by crystallinity. Here, statistical copolymerization is used to design macromolecular architectures where crystallinity is disrupted by a minimal amount of non-ethylene oxide comonomer units. Using the Flory exclusion model, we demonstrate that polymers containing 18 mol% comonomer and 18 wt% LiTFSI are devoid of crystallinity. A 10 mol% comonomer content is sufficient to reach a conductivity of 0.3 × 10 −4 S cm −1 at 25 °C. The Li + transference number is 0.6, indicating that the comonomer units not only limit the crystallinity but also weaken the strength of the Li + coordination to the polymer. The resulting solid polymer electrolyte is effective in an all-solid LFP|Li-metal battery operating at 25 °C, demonstrating that statistical copolymerization is an efficient tool for polymer electrolyte design.
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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".