(Invited) Polymer Blends As Electrolytes in All-Solid-State Batteries
Bibliographic record
Abstract
Advantages of polymer electrolytes include material flexibility, good interfacial compatibility and easy processability. However, these materials typically possess room temperature ionic conductivities, oxidation stability windows and mechanical strengths that are too low for them to be useful electrolyte materials in all-solid-state batteries (ASSB). One reason for this is that polymer properties that favor improved ionic conductivity such as high polymer chain mobility are generally not compatible with good mechanical strength making these properties difficult to optimize simultaneously. One strategy that has been investigated to this effect is polymer blending. The idea is that polymers with good ionic conductivity can be blended with thermoplastic or elastomeric materials that have high mechanical resistance to create a new electrolyte material with the properties of its combined parts. In this work, polymers with good ionic conductivity were combined with thermoplastic materials with high mechanical strength via melt processing methods to yield solid polymer electrolytes. The resultant electrolyte materials show promising results when implemented in composite electrodes and electrolytes for use in ASSB which will be discussed in this talk.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".