(Invited) Solid State Electrolytes for Beyond Lithium Ion Batteries
Bibliographic record
Abstract
At present, lithium batteries conquer much attention because of their widespread applications ranging from portable electronics to transportation and grid storage. The next-generation safe and robust lithium batteries require highly conducting solid electrolytes with excellent chemical stability with elemental Li and high voltage cathodes. Garnet-type metal oxides such as Li5La3Ta2O12, Li6La2BaTa2O12, and Li7La3Zr2O12 are promising candidates because of their high bulk ionic conductivity, low electronic conductivity and high electrochemical stability (up to 6V/Li).1-3 It is critical to understand the ion transport mechanism in these garnet-type oxides with respect to change in lithium content and temperature to further develop highly conducting practical solid electrolytes for next generation Li batteries. The present study reports effect of Y-doping for Ta in Li5La3Ta2O12 on structural, chemical, morphological and electrical properties. Analysis of crystal structure and electrical properties are performed using powder X-ray diffraction, NMR, and ac impedance spectroscopy to understand the Li ion migration pathways in Li5+2xLa3Ta2-xYxO12 (0.05 ≤ x ≤ 0.75).4,5 The x = 0.75 member of Li5+2xLa3Ta2-xYxO12 exhibits the highest conductivity of 10-4 Scm-1 at 23 ºC. In addition, high stability in aqueous solution makes Li5+2xLa3Ta2-xYxO12 suitable to use as Li protection layer in lithium-aqueous batteries.3 References 1.V.Thangadurai, H. Kaack, and W. Weppner, J. Am. Ceram. Soc., 86, 437 (2003). 2. R. Murugan, V. Thangadurai and W. Weppner, Angewandte Chemie International Edition, 46, 7778 (2007). 3. V. Thangadurai, S. Narayanan, and D. Pinzaru, Chem. Soc. Rev. 43, 4714 (2014). 4. S. Narayanan, F. Ramezanipour, and V. Thangadurai, Inorg. Chem. 54, 6968 (2015). 5. A.K. Baral, S. Narayanan, F. Ramezanipour, and V. Thangadurai, Phys. Chem. Chem. Phys. 16 , 11356 (2014).
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.066 | 0.050 |
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".