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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".