Hexavalent Ions Insertion in Garnet Li<sub>7</sub>La<sub>3</sub>Zr<sub>2</sub>O<sub>12</sub> Toward a Low Temperature Densification Reaction
Bibliographic record
Abstract
Abstract Nowadays, solid electrolytes are considered the main alternative to conventional liquid electrolytes in lithium batteries. The fabrication of these materials is however limited by the strict synthesis conditions, requiring high temperatures which can negatively impact the final performances. Here, it is shown that a modification of garnet‐based Li7La3Zr2O12 (LLZO) and the incorporation of tellurium can accelerate the synthesis process by lowering the formation temperature of cubic LLZO at temperatures below 700 °C. Optimized synthesis at 750 °C showed a decrease in particle size and cell parameter for samples with higher amounts of Te and the evaluation of electrochemical performances reported for LLZO Te0.25 a value of ionic conductivity of 5,15×10−5 S cm−1 after hot‐pressing at 700 °C, two orders of magnitude higher than commercial Al‐LLZO undergoing the same working conditions, and the highest value at this densification temperature. Partial segregation of Te‐rich phases occurs for high‐temperature densification. Our study shows the advantages of Te insertion on the sintering process of LLZO garnet and demonstrates the achievement of highly conductive LLZO with a low‐temperature treatment.
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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".