Highly Conductive Li Garnets by a Multielement Doping Strategy
Bibliographic record
Abstract
Highly conductive Li7La3Zr2O12 (LLZ) garnet-type solid electrolytes were further optimized to improve Li-ion conduction by La(3+)-sites substitution with Ba(2+) and Zr(4+)-sites substitution with Ta(5+) and Nb(5+). Garnet-structured metal oxides of the nominal chemical compositions Li6.65La2.75Ba0.25Zr1.4Ta0.5Nb0.1O12, Li6.4La3Zr1.4Ta0.6-xNbxO12 (x = 0, 0.1, 0.2, and 0.3), and the parent LLZ, as a reference, were prepared via conventional solid-state reaction to investigate the effect of multielement doping on ionic conductivity. The phase formation, morphology, and Li ion conductivity were characterized using powder X-ray diffraction (PXRD), scanning electron microscopy, and alternating current impedance spectroscopy methods, respectively. In addition, solid-state (27)Al and (7)Li magic-angle spinning (MAS) NMR was used to study the effect of "Al doping" on the investigated multielement doped Li-stuffed garnet metal oxides. All the prepared samples obtained the cubic garnet-type structure (space group: Ia3̅d; No. 230) at 1150 °C, similar to that of cubic LLZ. Except for Li6.4La3Zr1.4Ta0.6O12, all the members show Al content by Al MAS NMR. However, it was not possible to detect Al-based impurity phases using PXRD in any of the investigated garnets. Among the samples investigated in this work, "Al-free" Li6.4La3Zr1.4Ta0.6O12 demonstrated a bulk Li ion conductivity of 0.72 mS cm(-1) at 25 °C, with apparent activation energy of 0.26 eV, significantly higher than the parent LLZ.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".