Effect of Excess Li on the Structural and Electrical Properties of Garnet-Type Li<sub>6</sub>La<sub>3</sub>Ta<sub>1.5</sub>Y<sub>0.5</sub>O<sub>12</sub>
Bibliographic record
Abstract
Volatility of lithium during preparation of lithium-stuffed garnet-type metal oxide solid Li ion electrolytes is a common problem, which affects phase formation, ionic conductivity, mechanical strength and density. Synthesis of Li-stuffed garnets has been performed generally using the conventional solid-state reactions at elevated temperature in air. The present study describes the effect of excess LiNO 3 (2.5 to 15 wt.%) addition during the ceramic synthesis on the structural and electrical properties of garnet-type Li 6 La 3 Ta 1.5 Y 0.5 O 12 . Powder X-ray diffraction (PXRD) confirmed that cubic phase was formed in all tested cases, and there is no significant variation in lattice parameter with amount of excess LiNO 3 used. However, increasing amounts of excess lithium decreased inter-particle contact and increased grain growth during sintering, producing sharply varied microstructures. PXRD showed no secondary phase and scanning electron microscopy (SEM) analysis showed rather uniform morphology and absence of "glassy" materials at the grain-boundaries. The bulk Li ion conductivity was found to increase with amount of excess lithium, reaching a maximum room temperature conductivity of 1.62 × 10 −4 Scm −1 for the sample prepared using 10 wt.% excess LiNO 3 . Raman microscopy study indicated the presence of Li 2 CO 3 in all aged Li 6 La 3 Ta 1.5 Y 0.5 O 12 samples prepared using excess LiNO 3 .
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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".