Dopant synergy vs. competition in codoped Li7La3Zr2O12 garnet solid electrolytes
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Bibliographic record
Abstract
LLZO is typically singly doped with Ga or Ta to reach ionic conductivities over 1 mS/cm, and some recent studies have examined the potential conductivity benefits of codoping the material. However, to date codoped LLZO fails to out-perform singly doped LLZO in terms of conductivity, and no study has sufficiently explored the consequences on other necessary properties. We have previously screened 59 possible single dopants to determine their impact on these properties. No single dopant addresses all requirements, thus, here we explore triple doping. We choose Ga for high ionic conductivity and low electronic conductivity, Dy for high voltage stability, and Ti for low voltage stability. The benefits and tradeoffs of codoping are determined with 64 samples spanning the triple doping space. We find fundamental limitations to codoping. The high ionic conductivity of Ga-doped LLZO is lowered by the addition of the other dopants, though this is mitigated partly by increased Ga levels. Electronic conductivity, by contrast, shows that a critical level of Ga is needed to obtain low conductivities, and this is resilient to the other dopants such that codoping is effective here. By contrast, the high voltage stability is systematically limited by the poor performance of Ga, and Dy is not effective in overcoming this, but Ti alone does help the low voltage stability of the Ga containing materials. These fundamental limitations suggest that multilayer designs will likely be required for viable solid batteries. • The impact of codoping on the required properties for practical garnet solid electrolytes was determined. • The benefits and limitations of codoping garnets have been identified for the first time. • Ga, Dy and Ti all serve to improve different key properties of solid electrolytes. • There is some tolerance to codoping in terms of electrochemical stability window. • There is a cost in terms of ionic conductivity for adding codopants to improve the stability window.
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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 | 0.000 | 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) | 0.000 | 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