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
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Garnet‐type Li 7 La 3 Zr 2 O 12 electrolytes have garnered significant attention as promising solid‐state electrolyte candidates in all‐solid‐state lithium batteries (ASSLBs). However, its susceptibility to forming Li 2 CO 3 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 Mg 2+ doping occurs primarily at the La site (24c). This substitution, given the substantial disparity in ionic radii between Mg 2+ and La 3+ , effectively narrows the transport bottleneck for Li‐ions, resulting in a decreased ionic conductivity and an increased activation energy. Li 6 . 6 La 2 . 8 Mg 0 . 2 Zr 1 . 4 Ta 0 . 6 O 12 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 Li 2 CO 3 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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it