(Digital Presentation) Post-Treatment Study on Blended Polymer for Solid-State Lithium Batteries
Bibliographic record
Abstract
The widely used Li batteries (LiBs) is the most established rechargeable energy storage device. 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 as they tend to be non-flammable, have enough mechanical strength to resist dendrite growth, and do not leak. However, these materials tend to be less conductive than liquid electrolytes. This problem can be solved by solid-state gel polymer electrolytes (GPEs), which have lately received more attention. In fact, present a possible solution to this dilemma as they combine the ionic conductivity of liquid electrolytes with the increased safety of SPE to develop of electrolytes with high ionic conductivity and good mechanical stability.1 This work presents a preparation of in-situ GPE from SPE which produce by dry process in order to take advantage of the easy processability of SPE and the higher ionic conductivity of GPE.2, 3 The initial SPE was prepared by combining two polymers with LiTFSI (bis(trifluorormethanesulfonyl)imide) via extrusion mixing. This method of GPE processing was also found to improve other aspects of the electrolyte such as thermal and electrochemical properties which were characterized using cycling voltammetry, electrochemical impedance spectroscopy, and thermal gravimetric analysis. Additionally, the salt-polymer interaction in the GPE was characterized using FTIR, NMR, and the homogeneity of the polymer blend study by SEM-EDX. The cell of LFP/electrolyte/ Li metal showed a high capacity near to the theoretical one at C/20 at temperature 60 C. Additionally, the ionic conductivity of the electrolyte is around 10-5 S/cm. These first results confirmed that this blend of the polymers is a good electrolyte candidate for lithium batteries. 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 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) | 0.000 | 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) | 0.014 | 0.002 |
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".