Marginal defects modified graphene with S-C-N-C groups for highly selective oxygen reduction to H2O2
Bibliographic record
Abstract
Abstract Developing efficient metal-free catalysts to achieve electrochemical synthesis of hydrogen peroxide (H2O2) is crucial for substituting traditional energy-intensive anthraquinone process. Heteroatom-doped carbon materials have shown great potential toward 2e-pathway for catalyzing oxygen reduction to hydrogen peroxide (ORHP). However, conventional nanocarbon electrocatalysts show slow kinetics toward ORHP due to the weak binding strength with OOH* intermediate, resulting reduction of O2 to H2O. Here, sulfur and nitrogen dual-doped graphene (SNC) electrocatalyst consisting of S-C-N-C functional group are synthesized through hydrothermal self-assembly and nitridation processes with thiourea as sulfur source. In S-C-N-C functional group, pentagon-S and pyrrolic-N are covalently grafted onto the edge of graphene and produce marginal carbon ring defects, which provide highly active sites for catalyzing ORHP. The obtained SNC catalysts deliver an outstanding ORHP activity and selectivity for H2O2 production, while retaining remarkable stability. The experimental and computational results reveal that marginal S-C-N-C functional groups afford an appropriate adsorption strength with OOH* intermediate and a low reaction barrier as well, which are essential for the activity of ORHP.
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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".