A single iron site confined in a graphene matrix for the catalytic oxidation of benzene at room temperature
Bibliographic record
Abstract
Coordinatively unsaturated (CUS) iron sites are highly active in catalytic oxidation reactions; however, maintaining the CUS structure of iron during heterogeneous catalytic reactions is a great challenge. Here, we report a strategy to stabilize single-atom CUS iron sites by embedding highly dispersed FeN4 centers in the graphene matrix. The atomic structure of FeN4 centers in graphene was revealed for the first time by combining high-resolution transmission electron microscopy/high-angle annular dark-field scanning transmission electron microscopy with low-temperature scanning tunneling microscopy. These confined single-atom iron sites exhibit high performance in the direct catalytic oxidation of benzene to phenol at room temperature, with a conversion of 23.4% and a yield of 18.7%, and can even proceed efficiently at 0°C with a phenol yield of 8.3% after 24 hours. Both experimental measurements and density functional theory calculations indicate that the formation of the Fe═O intermediate structure is a key step to promoting the conversion of benzene to phenol. These findings could pave the way toward highly efficient nonprecious catalysts for low-temperature oxidation reactions in heterogeneous catalysis and electrocatalysis.
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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".