(Invited) Chemical and Electrochemical Stability of Fast Lithium Ion Conducting Garnet-Type Metal Oxides in H<sub>2</sub>o, Aqueous Solution, CO<sub>2</sub>, Li and S
Bibliographic record
Abstract
Fast lithium ion conducting garnet-type metal oxides are promising electrolytes for next-generation all-solid-state Li batteries and beyond Li-ion intercalation batteries, including Li-air and Li-S.1 Li-stuffed garnets show high total Li-ion conductivity (> 10-4 S/cm at room temperature) and good chemical stability against reaction with elemental Li.2,3 In the presence of moisture and aqueous environments, lithium-stuffed garnets are known to undergo fast H+/Li+ exchange, 3 while they are known to react with carbon dioxide, forming Li2CO3, under ambient atmosphere.4 Li-stuffed garnets exhibit wide electrochemical stability window of 9 V vs. Li+/Li at room temperature.4 Li dendrite formation and high area specific resistance for the reaction are being addressed by surface modification.5,6 In this talk, an overview of the chemical and electrochemical stability of various lithium-based garnets, developed in authors’ group and also elsewhere, against moisture/humidity, carbon dioxide, sulfur, and metallic lithium will be discussed.4 References A. Manthiram, X. Yu and S. Wang, Nat. Rev. Mater., 2, 16103 (2017). V. Thangadurai, S. Narayanan and D. Pinzaru, Chem. Soc. Rev., 43, 4714 (2014). K. Hofsetetter, A.J. Samson and V. Thangadurai, Solid State Ionics, 318, 71 (2018). V. Thangadurai, K. Hofstetter, A.J. Samson and S. Narayanan, J. Power Sources, Submitted. X. G. Han, Y. H. Gong, K. Fu, X. F. He, G. T. Hitz, J. Q. Dai, A. Pearse, B. Y. Liu, H. Wang, G. Rublo, Y. F. Mo, V. Thangadurai, E. D. Wachsman and L. B. Hu, Nat. Mater., 16, 572 (2017). K. K. Fu, Y. H. Gong, B. Y. Liu, Y. Z. Zhu, S. M. Xu, Y. G. Yao, W. Luo, C. W. Wang, S. D. Lacey, J. Q. Dai, Y. N. Chen, Y. F. Mo, E. Wachsman and L. B. Hu, Sci, Adv., 3, 11 (2017).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.014 |
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".