In‐Plane Topological‐Defect‐Enriched Graphene as an Efficient Metal‐Free Catalyst for pH‐Universal H<sub>2</sub>O<sub>2</sub> Electrosynthesis
Bibliographic record
Abstract
Abstract Developing efficient metal‐free catalysts to directly synthesize hydrogen peroxide (H2O2) through a 2‐electron (2e) oxygen reduction reaction (ORR) is crucial for substituting the traditional energy‐intensive anthraquinone process. Here, in‐plane topological defects enriched graphene with pentagon‐S and pyrrolic‐N coordination (SNC) is synthesized via the process of hydrothermal and nitridation. In SNC, pentagon‐S and pyrrolic‐N originating from thiourea precursor are covalently grafted onto the basal plane of the graphene framework, building unsymmetrical dumbbell‐like S─C─N motifs, which effectively modulates atomic and electronic structures of graphene. The SNC catalyst delivers ultrahigh H2O2 productivity of 8.1, 7.3, and 3.9 mol gcatalyst−1 h−1 in alkaline, neutral, and acidic electrolytes, respectively, together with long‐term operational stability in pH‐universal electrolytes, outperforming most reported carbon catalysts. Theoretical calculations further unveil that defective S─C─N motifs efficiently optimize the binding strength to OOH* intermediate and substantially diminish the kinetic barrier for reducing O2 to H2O2, thereby promoting the intrinsic activity of 2e‐ORR.
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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) | 0.000 | 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".