Solid State Electrolytes for Next-Generation Lithium Ion Batteries
Bibliographic record
Abstract
Lithium ion batteries achieve much attention because of their wide applications ranging from portable electronics to transportation and grid storage. The next-generation lithium ion batteries require a highly conducting solid electrolyte which ensures safe operation. Garnet-type oxides with nominal chemical formula, Li5La3Ta2O12 are promising Li+ conductors which show high ionic conductivity, low electronic conductivity and electrochemical stability.1 It is important to understand the fundamental electrical transport mechanism of these garnet-type solid-state Li+ conductors with respect to change in temperature and lithium content in the structure. Li+ stuffing into Li5La3Ta2O12 have proven to improve the ionic conductivity of garnet-type oxides.2 The present study reports highly conducting lithium-stuffed Li5+2xLa3Ta2-xYxO12 (0.05 ≤ x ≤ 0.75) electrolytes.3 Effect of Y3+ and Li+ doping in Li5La3Ta2O12 on the structural, morphological and electrical properties are studied in this work. Detailed analysis of crystal structure, and electrical and dielectric properties are also performed, in order to investigate the Li+ migration pathways in the crystal structure, using different techniques such as powder X-ray diffraction, solid state 7Li MAS NMR, and electrochemical impedance spectroscopy.3-4 The x = 0.50 and 0.75 members in the series of Li5+2xLa3Ta2-xYxO12 have exhibited highest conductivity of ~ 10-4 Scm-1 at 23 ºC. In addition, their stability in aqueous LiCl solution make them suitable candidate as protective layers for lithium electrodes in lithium-air batteries.3 References 1.Thangadurai, V.; Kaack, H.; Weppner, W. J. F. J. Am. Ceram. Soc. 2003, 86, 437-440. 2. Thangadurai, V.; Narayanan, S., Pinzaru, D. Chem. Soc. Rev. 2014,43, 4714-4727. 3. Narayanan, S.; Ramezanipour, F.; Thangadurai, V. Inorg. Chem. 2015, 54(14), 6968-6977. 4. Baral, A. K.; Narayanan, S.; Ramezanipour, F.; Thangadurai, V. Phys. Chem. Chem. Phys. 2014, 16, 11356-11365.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".