Dopant Concentration–Porosity–Li-Ion Conductivity Relationship in Garnet-Type Li<sub>5+2<i>x</i></sub>La<sub>3</sub>Ta<sub>2–<i>x</i></sub>Y<sub><i>x</i></sub>O<sub>12</sub> (0.05 ≤ <i>x</i> ≤ 0.75) and Their Stability in Water and 1 M LiCl
Bibliographic record
Abstract
Highly Li-ion conductive Y-doped garnet-type Li5+2xLa3Ta2-xYxO12 (0.05 ≤ x ≤ 0.75) were studied to understand the effects of yttrium- and lithium-doping on crystal structure, porosity, and Li-ion conductivity using (7)Li MAS NMR, electrochemical ac impedance spectroscopy, and scanning electron microscopy (SEM), as well as ex situ and in situ powder X-ray diffraction (PXRD) to further explore the potential application of garnets in all-solid-state Li-ion batteries. Solid-state (7)Li MAS NMR studies showed an increase in the Li-ion mobility as a function of Y- and Li-doping in Li5+2xLa3Ta2-xYxO12, which is consistent with the results from ac impedance spectroscopy. The SEM studies on sintered pellets indicated a systematic decrease in porosity and an increase in sinterability as the Y- and Li-doping levels increase in Li5+2xLa3Ta2-xYxO12. These results are consistent with the calculated porosity and densities using the Archimedes method. Using the variable-temperature in situ PXRD in the temperature range of 30-700 °C, a thermal expansion coefficient of 7.25 × 10(-6) K(-1) was observed for Li6La3Ta1.5Y0.5O12. To further explore the possibility of a new application for the Li-stuffed garnets, the stability of these materials in aqueous LiCl solution was also studied. A high degree of structural stability was observed in these materials upon 1 M LiCl treatment, making them suitable candidates for further studies as protective layers for lithium electrodes in aqueous lithium 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".