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 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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".