Catalyst Design for Electrochemical Oxygen Reduction toward Hydrogen Peroxide
Bibliographic record
Abstract
Abstract Precise electrochemical synthesis under ambient conditions has provided emerging opportunities for renewable energy utilization. Among many promising systems, the production of hydrogen peroxide (H2O2) from the cathodic oxygen reduction reaction (ORR) has attracted considerable interest in past decades due to the increasing market demands and the vital role of ORR in the electrocatalysis field. This work describes recent advances in cathodic materials for H2O2 synthesis from 2e- ORR. By using Pt as a stereotype, the tuning knobs are overviewed, including the intrinsic binding strength of oxygenated species, the intermediate diffusion path and the isolation of Pt–Pt ensembles that enable 2e- ORR pathway from 4e- total reduction. This knowledge is successfully applied to other transition metal systems and leads to the discovery of more efficient alloy catalysts with balanced improvement on both activity and selectivity. In addition, mesostructure engineering and heteroatoms doping strategies on carbon‐based materials, which significantly boost the H2O2 production efficiency as compared to intact carbon sites, are also reviewed. Finally, future directions and challenges of transferring developed catalysts from lab scale tests to pilot plant operations are briefly outlooked.
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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 | 0.000 |
| 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 | 0.001 | 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".