Tailor-Made Development of Fast Li Ion Conducting Garnet-Like Solid Electrolytes
Bibliographic record
Abstract
This paper reports a novel approach to designing advanced solid Li ion electrolytes for application in various solid state ionic devices, including Li ion secondary batteries, gas sensors, and electrochromic displays. The employed methodology involves a solid-solution reaction between the two best-known fast Li ion conductors in the garnet-family of compounds Li(6)BaLa(2)M(2)O(12) (M = Nb, Ta) and Li(7)La(3)Zr(2)O(12). Powder X-ray diffraction (PXRD), scanning electron microscopy (SEM), AC impedance, and (7)Li nuclear magnetic resonance (Li NMR) spectroscopy were employed to characterize phase formation, morphology, ionic conductivity, and Li ion coordination in Li(6.5)La(2.5)BaZrMO(12). PXRD shows for formation of a cubic garnet-like structure and AC impedance data is consistent with other known solid Li ion electrolytes. Li(6.5)La(2.5)BaZrTaO(12) exhibits a fast Li ion conductivity of about 6 x 10(-3) S cm(-1) at 100 degrees C, which is comparable to that of currently employed organic polymer electrolytes value at room temperature. The Nb analogue shows an order of magnitude lower ionic conductivity than that of the corresponding Ta member, which is consistent with the trend in garnet-type electrolytes reported in the literature. Samples sintered at 1100 degrees C shows the highest electrical conductivity compared to that of 900 degrees C. (7)Li MAS NMR shows a sharp single peak at 0 ppm with respect to LiCl, which may be attributed to fast migration of ions between various sites in the garnets, and also suggesting average distributions of Li ions at average octahedral coordination in Li(6.5)La(2.5)BaZrMO(12). The present work together with literature used to establish very important fundamental relationship of functional property-Li concentration-crystal structure-Li diffusion coefficient in the garnet family of Li ion electrolytes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".