High Throughput Studies of Doped Li-La-Zr-O Garnet Solid Electrolytes
Bibliographic record
Abstract
Lithium lanthanum zirconium oxide (LLZO) is a leading candidate for solid Li-batteries due to its high lithium-ion conductivity, stability in air and against Li metal, and compatibility with high-voltage cathodes.1,2 Despite significant research being done, our understanding of LLZO is limited by the relatively small number of compositions which have been studied; both in the larger Li-La-Zr-O system and in the doped cubic LLZO system, which can accommodate an extremely wide range of dopants.3To study this large number of compositions, we have applied a high-throughput methodology for synthesizing, characterizing, and testing sets of 64 LLZO electrolytes at the mg-scale. We employ a citrate sol-gel synthesis method whereby reagent solutions are dispensed across a well-plate to give a composition gradient. After drying and calcining the gels, the resulting powders are pelleted and then sintered at the desired temperature. High-throughput characterization techniques utilized include powder X-ray diffraction, impedance spectroscopy, DC polarization, and electrochemical stability window testing. Using our methodology, we have recently studied over 700 samples to produce a full phase stability diagram for the Li-La-Zr-O pseudoternary system.4 We found there is significant solubility of Li in the La2Zr2O7 pyrochlore structure, and that both cubic and tetragonal undoped LLZO appearing throughout the system have similarly poor bulk conductivities. This previous study again emphasizes the key role dopants play in LLZO. Herein, our methodology is applied to a comprehensive doping study where 60 different dopants are evaluated under identical synthesis conditions to allow for a thorough understanding of dopant effects on structure, ionic conductivity, electronic conductivity, and electrochemical stability window. Our samples achieve high conductivities over 1 mS/cm and relative densities above 90% with our combinatorial synthesis. Dopant content is optimized for each promising dopant to compare best-case results and correlate performance with structural properties like lattice parameters and density. Since comparing dopants in the literature is often difficult due to varying synthesis methods, this systematic work is essential in rational design of these electrolytes. 1.Q. Liu, et al., J. Power Sources 2018, 389, 120-134. 2.T. Thompson, et al., ACS Energy Letters 2017, 2, 462-468. 3.F. Zheng, et al., J. Power Sources 2018, 389, 198-213. 4.E. Anderson et al., DOI:10.1016/j.ssi.2022.116087.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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) | 0.001 | 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".