Experimental and Computational Study of Mg and Ta‐Doped Li<sub>7</sub>La<sub>3</sub>Zr<sub>2</sub>O<sub>12</sub> Garnet‐Type Solid Electrolytes for All‐Solid‐State Lithium Batteries
Bibliographic record
Abstract
Abstract Garnet‐type Li7La3Zr2O12 electrolytes have garnered significant attention as promising solid‐state electrolyte candidates in all‐solid‐state lithium batteries (ASSLBs). However, its susceptibility to forming Li2CO3 upon atmospheric exposure leads to performance degradation, limiting its application. This study introduces a co‐doping strategy of Mg and Ta to enhance the properties of garnet electrolytes. Pure cubic Mg and Ta‐doped LLZO electrolytes are successfully synthesized using the solid‐state reaction method. Experimental results, coupled with density functional theory (DFT) calculation, reveal that Mg2+ doping occurs primarily at the La site (24c). This substitution, given the substantial disparity in ionic radii between Mg2+ and La3+, effectively narrows the transport bottleneck for Li‐ions, resulting in a decreased ionic conductivity and an increased activation energy. Li6.6La2.8Mg0.2Zr1.4Ta0.6O12 exhibits a relative density of ≈92.6%, demonstrating outstanding performance with a room temperature ionic conductivity of 4.31 × 10−4 S cm−1 and low electronic conductivity of 2.48 × 10−8 S cm−1. Notably, after 4 months of atmospheric exposure, its ionic conductivity decreased to ≈78% of the initial value, attributable to Li2CO3 formation. Furthermore, the material demonstrated exceptional long‐term cycle stability over 1000 h at a current density of 0.1 mA cm−2 at 25 °C, indicating effective suppression of Li dendrite formation.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".