Implications of 4 e<sup>–</sup> Oxygen Reduction via Iodide Redox Mediation in Li–O<sub>2</sub> Batteries
Bibliographic record
Abstract
The nonaqueous lithium–oxygen (Li–O 2 ) electrochemistry has garnered significant attention because of its high theoretical specific energy compared to the state-of-the-art lithium-ion battery. The common active nonaqueous Li–O 2 battery cathode electrochemistry is the formation (discharge) and decomposition (charge) of lithium peroxide (Li 2 O 2 ). Recent reports suggest that the introduction of lithium iodide (LiI) to an ether-based electrolyte containing water at impurity levels induces a 4 e – oxygen reduction reaction forming lithium hydroxide (LiOH) potentially mitigating instability issues related to typical Li 2 O 2 formation. We provide quantitative analysis of the influence of LiI and H 2 O on the electrochemistry in a common Li–O 2 battery employing an ether-based electrolyte and a carbon cathode. We confirm, through numerous quantitative techniques, that the addition of LiI and H 2 O promotes efficient 4 e – oxygen reduction to LiOH on discharge, which is unexpected given that only 2 e – oxygen reduction is typically observed at undoped carbon electrodes. Unfortunately, LiOH is not reversibly oxidized to O 2 on charge, where instead a complicated mix of redox shuttling and side reactions is observed.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".