The influence of transition metal oxides on the kinetics of Li<sub>2</sub>O<sub>2</sub>oxidation in Li–O<sub>2</sub>batteries: high activity of chromium oxides
Bibliographic record
Abstract
Reducing the energy loss associated with Li2O2 electrochemical oxidation is paramount to the development of efficient rechargeable lithium-oxygen (Li-O2) batteries for practical use. The influence of a series of perovskites with different eg filling on the kinetics of Li2O2 oxidation was examined using Li2O2-prefilled electrodes. While LaCrO3 is inactive for oxygen evolution upon water oxidation in alkaline solution, it was found to provide the highest specific current towards Li2O2 oxidation among all the perovskites examined. Further exploration of Cr-based catalysts showed that Cr nanoparticles (Cr NP) with an average particle size of 40 nm, having oxidized surfaces, had comparable surface area activities to LaCrO3 but much greater mass activities. Unlike Pt/C and Ru/C that promote electrolyte oxidation in addition to Li2O2 oxidation, no evidence of enhanced electrolyte oxidation was found for Cr NP relative to Vulcan carbon. X-ray absorption spectroscopy at the O K and Cr L edge revealed a redox process of Cr(3+) ↔ Cr(6+) on the surface of Cr NP upon Li2O2 oxidation, which might be responsible for the enhanced oxidation kinetics of Li2O2 and the reduced charging voltages of Li-O2 batteries.
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 | 0.001 |
| 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 | 0.001 | 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".