Characterization of Gel Polymer Electrolytes Based on Poly (Ionic Liquid)-Glyme Mixtures for Lithium Metal Batteries
Bibliographic record
Abstract
Solid polymer electrolytes (SPE) have generated an extensive and sustained interest for its application in lithium metal batteries.[ 1 ] Compared with liquid electrolytes, solid polymer electrolytes have higher safety and thermal stability, since they can provide a physical barrier layer to prevent efficiently lithium dendrite growth and avoid thermal runaway under high temperature or impact. Despite the substantial benefits, some limitations remain to be improved, such as a low ionic conductivity at room temperature and low transference number. Several studies are being conducted to overcome these weaknesses and develop new generation of solid polymer electrolyte lithium metal batteries.[ 2 ] Most of the research on SPEs is focused on polyethylene oxide (PEO) mixed with lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) salt and its derivative. In general, PEO-based solid electrolytes have shown good dimensional stability, good safety, and can mechanically prevent dendritic growth. However, they exhibit low ionic conductivity and rather low cationic transference number (t+) of ca. 0.15. To achieve sufficient conductivity at low temperatures, polymers that are difficult to crystallize are proposed. Ionic liquids (IL) are poor crystal formers; therefore, polymers of IL are potentially good alternatives to PEO for lower temperature conductivity. An Iongel membrane based on the poly(dimethyldiallylammonium) polyDADMA-TFSI poly(ionic liquid) has gained attention in polymer metal batteries due to its favorable mechanical properties and stability against Li-metal.[ 3 ] The present study provides an insight into the properties of free-standing membranes using poly (ionic liquid)-glyme mixtures compared to typical polymer salt blends (PEO-LiTFSI) and PDADMA-LiTFSI. The benefit of tetraethylene glycol dimethyl ether (G4) is highlighted and appears to be the key component to obtain higher ionic conductivity regardless on the type of polymer used. The impact of the increase in LiG4TFSI/poly ionic liquid ratio within the electrolyte on the transference number, ionic conductivity and mechanical properties was further analyzed. For 33-66 wt% composition for PDADMAT-LiG4TFSI and PEO-LiG4TFSI membranes, a good compromise between the physicochemical and electrochemical properties was achieved. References [1] P. Yao, H. Yu, Z. Ding, Y. Liu, J. Lu, M. Lavorgna, J. Wu, X. Liu, Frontiers in Chemistry 2019, 7. [2] J. R. Nair, L. Imholt, G. Brunklaus, M. Winter, The Electrochemical Society Interface 2019, 28, 55-61. [3] aG. B. Appetecchi, G. T. Kim, M. Montanino, M. Carewska, R. Marcilla, D. Mecerreyes, I. De Meatza, Journal of Power Sources 2010, 195, 3668-3675; bA. Fdz De Anastro, N. Lago, C. Berlanga, M. Galcerán, M. Hilder, M. Forsyth, D. Mecerreyes, Journal of Membrane Science 2019, 582, 435-441; cG. M. A. Girard, X. Wang, R. Yunis, D. R. MacFarlane, A. J. Bhattacharyya, M. Forsyth, P. C. Howlett, Batteries & Supercaps 2019, 2, 229-239.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".