Understanding the Role of Solvents on the Morphological Structure and Li-Ion Conductivity of Poly(vinylidene fluoride)-Based Polymer Electrolytes
Bibliographic record
Abstract
Polymer-based solid-state electrolytes (SSEs) are promising candidates to enhance the performances of current lithium-ion batteries (LiBs), as they possess advantages of facile processing and flexibility over ceramic SSEs. However, polymer SSEs such as poly(ethylene oxide) (PEO) suffer from low ionic conductivity, a limited voltage stability window, and thermal stability. Poly(vinylidene fluoride) (PVDF)-based polymer electrolytes (PPEs) with lean solvent confinement provide improved ionic conductivity and outstanding chemical/electrochemical stability. In this study, we report the effects of different solvents on the morphological structure and ionic conductivity of PPEs. We demonstrate that solvents with relatively high boiling points (dimethylformamide (DMF), dimethyl sulfoxide (DMSO), N-Methyl-2-pyrrolidone (NMP), and dimethylacetamide (DMA)) can be trapped in PPEs, and they all have positive effects on the ionic conductivity. The ionic conductivity is related to the quantity of the trapped solvent; for a PPE with DMF retention of ∼20%, the ionic conductivity is about 0.1 mS cm −1 . Increasing the amount of lithium salt was found to improve the solvent retention but at the cost of membranes’ mechanical property. It is also possible to introduce a low boiling point co-solvent in order to reduce the production cost and drying duration for manufacturing PPEs.
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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 | 0.001 |
| 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".