Development of Garnet-Type Li Ion Electrolytes for All-Solid-State Li Ion Batteries
Bibliographic record
Abstract
The high energy density Li ion batteries require a replacement of currently used organic polymer electrolyte with highly conducting solid-state materials in terms of safety reasons. Solid electrolytes exhibiting high total Li ion conductivity, negligible electronic conductivity and wide electrochemical window (up to 6 V/Li/Li+) are preferable for application in solid-state Li ion batteries [1,2]. Garnet-like oxides, with general formula, Li5La3M2O12 (M = Nb, Ta) are emerged as a promising class of Li+ conductors among several other solid-state materials [1]. The increase in Li content has proven to be increasing the Li ion conductivity of garnets and Li7La3Zr2O12 have a highest conductivity of the order of 10-4 Scm-1 at ambient condition [3]. In this talk, we report the effect of excess Li added during synthesis to avoid Li volatilization on the ionic conductivity of garnet-type materials and discuss the recent developments on optimization of ionic conductivity of garnets. References 1. V. Thangadurai, H. Kaack and W. J. F. Weppner, J. Am. Ceram. Soc., 86, 437 (2003). 2. J. B. Goodenough and K.-S. Park, J. Amer. Chem. Soc., 135, 1167 (2013). 3. R. Murugan, V. Thangadurai and W. Weppner, Angew. Chem. Int. Ed. Engl., 46, 7778 (2007).
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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) | 0.001 | 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 | 0.001 |
| 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".