Cubic garnet solid polymer electrolyte for room temperature operable all-solid-state-battery
Bibliographic record
Abstract
Solid polymer electrolytes are promising candidates for implementation in next-generation all-solid-state batteries (ASSBs) which could replace conventional batteries used today. However, the materialization of ASSBs on a mass scale is restricted by the low ionic conductivity and high interfacial resistance of solid electrolytes. In this work, succinonitrile (SN) with lithium (trifluoromethylsulphonyl)imide (LiTFSI) and Al-doped Li7La3Zr2O12 (Al-LLZO) nanoparticles were used to improve the ionic conductivity of a polyethylene oxide-based composite electrolyte. The Al-LLZO nanoparticles were synthesized by a facile synthesis process at low temperatures, which contributed to an enhancement in the ionic conductivity. A solid polymer electrolyte with 7.5 wt% of Al-LLZO and 15 wt% of SN achieved a high ionic conductivity of 4.17 × 10−4 Scm−1 at room temperature and a large value of 0.451 for the lithium-ion transport number at 60 °C. By adding 10 wt% SN and 10 wt% of Al-LLZO in the LiFePO4 cathode, the cell could operate at 25 °C with a specific capacity of 130 mAh g−1 and 89% capacity retention after 200 cycles at current density of 20 mA g−1. This study therefore proposes a solution to improve the ionic conductivity of solid polymer electrolytes in all-solid-state batteries.
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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) | 0.002 | 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".