Defect Engineering on Commercial Carbon for Economical H <sub>2</sub> O <sub>2</sub> Electrosynthesis Under Industrial‐Relevant Conditions
Bibliographic record
Abstract
Abstract Electrochemical H 2 O 2 production through two‐electron oxygen reduction reaction (2e − ORR) offers a sustainable and green alternative to the traditional anthraquinone process. However, the development of efficient catalysts that simultaneously achieve high selectivity, activity, and stability under industrially relevant production rates remains a significant challenge. This study presents a defect engineering strategy to optimize commercial Vulcan carbon for efficient H 2 O 2 electrosynthesis via 2e − ORR. By systematically modulating defect densities, it is identified that carbon materials with moderate defect concentrations (D10‐vulcan) achieve an optimal balance between activity and selectivity, demonstrating over 95% H 2 O 2 selectivity and sustained performance at 400 mA cm −2 for 200 h under industrial‐relevant conditions. Density functional theory (DFT) calculations reveal that edge defects and holes act as 4e − ORR active sites, while adjacent carbon atoms serve as 2e − active sites, providing a mechanistic understanding of defect‐mediated selectivity. The proposed “active site saturation” theory explains performance variations under high overpotentials and low oxygen availability, offering a scalable approach and electrocatalyst design guidance for cost‐effective H 2 O 2 production.
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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 | 0.000 | 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".