Evaluation of Redox Mediator’s Oxidation Stability in Lithium-Oxygen Batteries
Bibliographic record
Abstract
Employing organic redox mediators (ORMs) as mobile catalyst into the electrolyte system has been selected as an important strategy to lower the charging overpotentials in lithium-oxygen (Li-O2) batteries. Careful choice of molecular designs of ORMs can also tailor their redox potential and rate of electron-transfer to improve the catalytic efficiency. However, the stability of ORMs in Li–O2 cells was scarcely studied. In here, catalytic efficiency and stability of several important ORMs are assessed through in situ gas analysis and reactivity tests with singlet oxygen. Some well-known ORMs are detrimentally decomposed during the first cycle in Li–O2 cells, whereas nitroxyl-radical-based ORMs bear the most stable and efficient response. Analogous nitroxyl-radical derivatives further increase round-trip energy efficiency and electron-transfer kinetics. This study also emphasizes the evaluation of chemical stability of ORMs, which are mandatory for the long-term cyclability in Li–O2 cells. references W.-J. Kwak, H. Kim, H.-G. Jung, D. Aurbach, Y.-K. Sun, J. Electrochem. Soc. 2018, 165, A2274–A2293. W.-J. Kwak, H. Kim, Y. K. Petit, C. Leypold, T. T. Nguyen, N. Mahne, P. Redfern, L. A. Curtiss, H.-G. Jung, S. M. Borisov, S. A. Freunberger and Y.-K. Sun, Nat. Commun., 2019, 10, 1380.
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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) | 0.000 | 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 | 0.001 | 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".