A Novel Quaternary Composite Solid State Electrolyte for Li Batteries
Bibliographic record
Abstract
All-solid-state batteries continue to grow as an alternative to replace the traditional liquid-based ones not only because they provide increased safety but also higher power and energy density [1]. However, current solid-state electrolytes are either brittle (ceramics) or suffer low ionic conductivity at room temperature (polymers) [1]. Therefore, composite solid electrolytes are considered as a viable option. We have developed flexible and easy film-forming composite solid state electrolytes (CSEs). Our promising CSE is a quaternary composite that consists of the Solid polymer electrolyte (SPE) Poly(ethylene oxide)/ Lithium bis(trifluoromethanesulfonyl)imide (PEO/ LiTFSI), the fast conducting perovskite inorganic ceramic Li0.33La0.557TiO3 (LLTO) and the plasticizer Succinonitrile (SN). Over 40 samples with different (PEO/LiTFSI/LLTO/SN) compositions were prepared and characterized. The optimal mechanical and electrochemical properties was found for the sample 10S which has the following composition: 70%-PEO 12% LiTFSI 9%-LLTO 9%-SN. 10S had the ionic conductivity of ̴ 10-3 S/cm at 55 ̊ C and young’s modulus of around 105 MPa. The all-solid-state battery made by sandwiching 10S between a lithium metal anode and an NMC 532-based cathode achieved specific discharge capacities of 161 mAh/g at C/20. Pulsed-field gradient NMR was performed at different temperatures to measure the diffusion coefficient. The results have shown an Arrhenius-type dependence with a slope switch at around 55 ̊ C which is a similar behavior as the one observed in the Arrhenius plot obtained from conductivity measurements. Further investigation is underway to elucidate the conductivity mechanism in this new type of quaternary composite by advanced NMR and other techniques Reference [1] M. Keller, A. Varzi, S. Passerini, J. Power Sources 2018, 392, 206. Figure 1
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".