Advancing Lithium Battery Performance through Gel Electrolytes: Investigating EC-Based Blends with HNBR and PEC for Enhanced Conductivity
Bibliographic record
Abstract
Li-ion batteries (LiBs), the most established rechargeable energy storage devices, are currently at closing the gap of their theoretical capacity. That is the main reason why Lithium metal batteries (LMB) have recently regained interest, mainly because of the higher theoretical energy densities achievable with this technology. Therefore, the development of new electrode and electrolyte materials is essential for improving battery performance. Solid polymer electrolytes (SPEs) have been presented as safer alternatives for liquid electrolytes which are used commercially in these devices as they tend to be non-flammable, have enough mechanical strength to resist lithium dendrite growth, and do not leak. However, solid polymer electrolytes are less conductive than liquid electrolytes. Improved ionic conductivity in solid electrolyte materials can be obtained through the use of solid-state gel polymer electrolytes (GPEs). These materials combine the ionic conductivity of liquid electrolytes with the increased safety of SPE resulting in electrolytes with high ionic conductivity and good mechanical stability. 1 This study introduces a novel approach to creating in-situ gel polymer electrolytes (GPEs) from solid polymer electrolytes (SPEs) using melt processing followed by sample heating. 2, 3 This method capitalizes on the easy processability of SPEs. Initially, the SPE was prepared by blending two polymers with LiTFSI (bis(trifluoromethanesulfonyl)imide) through extrusion mixing. The sample was then converted into a GPE via a controlled heating step. Compared to the initial SPE, the resultant GPE possessed improved thermal and electrochemical properties. In this presentation, we will provide supportive data derived from IR, DSC, and NMR analyses to highlight the differences linked to changes in salt-polymer interactions in the GPE. Verdier, N.; Lepage, D.; Zidani, R.; Prebe, A.; Ayme-Perrot, D.; Pellerin, C.; Dolle, M.; Rochefort, D., Cross-linked polyacrylonitrile-based elastomer used as gel polymer electrolyte in Li-ion battery. ACS Applied Energy Materials 2019, 3 (1), 1099-1110. Ma, C.; Cui, W.; Liu, X.; Ding, Y.; Wang, Y., In situ preparation of gel polymer electrolyte for lithium batteries: Progress and perspectives. InfoMat 2021 . Verdier, N.; Foran, G.; Lepage, D.; Prébé, A.; Aymé-Perrot, D.; Dollé, M., Challenges in Solvent-Free Methods for Manufacturing Electrodes and Electrolytes for Lithium-Based Batteries. Polymers 2021, 13 (3), 323.√
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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 | 0.001 |
| 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".