Performance Optimization of Electrospun Lithium-Ion Conducting PAN/PEO Solid Polymer Electrolyte
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Storing energy in rechargeable lithium-ion batteries is essential for a renewable energy supply. Replacing liquid with solid electrolytes in all-solid-state batteries minimizes safety concerns while increasing energy density. This study introduces a solid polymer electrolyte membrane that can be produced in a scalable, one-step process. The polymer blend consisting of the matrix-giving polyacrylonitrile (PAN) and the ion-conducting poly(ethylene oxide) (PEO) with lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) as the conducting salt is electrospun, ensuring mechanical flexibility and low crystallinity. Flexible, free-standing membranes exhibit fiber retention up to 100 °C, enabling a wide thermal application window above PEO’s melting point. Adjusting the plasticizer ratio, humidity, and drying conditions allows fine-tuning of the membrane’s morphology, porosity, and ionic conductivity, reaching 0.1 mS cm –1 at 328 K. A slight increase in cell pressure from 0.6 to 2.1 MPa decreases porosity and further increases ionic conductivity without affecting the fiber structure, enabling low-pressure utilization. Moreover, variable-temperature 7 Li solid-state nuclear magnetic resonance spectroscopy studies of the dry membrane further demonstrated rapid local Li-ion exchange processes with very low activation energies. An electrochemical window between 0 and 4.5 V, and reversible lithium-ion transport, confirmed by galvanostatic cycling, imply the promising application of high-performance electrospun solid polymer electrolytes.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".