High-Throughput Studies of Li-La-Zr-O Garnet Solid Electrolytes
Bibliographic record
Abstract
Development of next-generation lithium-ion batteries has increasingly focused on all-solid batteries employing either ceramic, polymer, or glass electrolytes in order to address shortcomings in currently commercialized liquid electrolyte lithium-ion batteries including safety, limited lifetime and lower energy densities resulting from the instability with respect to Li metal anodes. [1] Lithium lanthanum zirconium oxide (LLZO) is a leading candidate for solid Li-batteries due to its high lithium-ion conductivity, stability in air and with Li metal, and compatibility with high-voltage cathodes. [2] , [3] , [4] Despite significant progress being made in its development, our understanding of LLZO is limited by the relatively small number of compositions which have been studied; especially considering the leading contender is a pseudo-quaternary oxide (Ga-doped LLZO). [5] Herein, we have applied a high-throughput methodology for synthesizing, characterizing, and testing sets of 64 LLZO electrolytes at the mg-scale. This has allowed the study of the entire Li-La-Zr-O pseudo-ternary system by enabling a single researcher to explore hundreds of compositions in a single week. We employ a sol-gel synthesis method whereby a robot dispenses reagent solutions in order to vary the composition across a well-plate. After drying the samples and burning off the citrates, the resulting powders are pressed into pellets using a custom-made 64-pellet die and the pellets are sintered at the desired temperature. The high-throughput characterization techniques utilized include powder X-ray diffraction, electrochemical impedance spectrometry and electrochemical cycling in order to test electrolyte stability; with each method performed on 64 samples simultaneously. The resulting structural phase diagrams will be presented for various sintering temperatures along with the extracted electrochemical properties. Given that ionic conductivity in ceramic electrolytes depends heavily on the presence/absence of certain structural defects, the complete understanding of the phase diagrams will significantly accelerate the development of this important class of advanced materials. [1] D. Aurbach et al., Electrochimica Acta 2004, 50, 247-254. [2] F. Zheng, et al., J. Power Sources 2018, 389, 198-213. [3] Q. Liu, et al., J. Power Sources 2018, 389, 120-134. [4] T. Thompson, et al., ACS Energy Letters 2017, 2, 462-468. [5] J-F. Wu, et al., ACS Applied Mater Interfaces 2017, 9, 1542-1552
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".