In Situ Generation of a Gel Polymer Electrolyte via the Controlled Formation of Ethylene Carbonate in a Poly(ethylene carbonate)‐Hydrogenated Nitrile Butadiene Rubber Solid Polymer Electrolyte
Bibliographic record
Abstract
Abstract Electrolytes play an essential role in electrochemical energy storage devices. Liquid electrolytes have good ionic conductivity but tend to be flammable, prompting some of the safety concerns that are associated with these devices. Solid polymer electrolytes (SPEs) are presented as a potential solution to this problem. These materials have higher mechanical stability and can be formulated to be non‐flammable. However, ionic conductivity in solid polymer electrolytes tends to be several orders of magnitude lower than that of liquid electrolytes, significantly limiting device performance making electrolyte safety and performance difficult to optimize simultaneously. However, gel electrolytes which combine lower flammability, higher mechanical strength, and adequate ionic conductivity may present a solution to this challenge. To this end, the melt processing of hydrogenated nitrile rubber (HNBR) with poly(ethylene carbonate) (PEC) followed by the in situ formation of ethylene carbonate (EC) is reported. Conversion of PEC to EC is confirmed via NMR spectroscopy. Electrochemical testing reveals improved ionic conductivity following the conversion of the solid polymer electrolyte to the gel polymer electrolyte. Improvements in ionic conductivity, relative to the initial SPE, are attributed to decreased salt‐polymer interactions in favor of salt‐EC interactions as observed via differential scanning calorimetry, Fourier transform infrared, and nuclear magnetic resonance spectroscopy.
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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".