Iron and Nitrogen Co‐Doped Mesoporous Carbon‐Based Heterogeneous Catalysts for Selective Reduction of Nitroarenes
Bibliographic record
Abstract
Abstract A facile fabrication of Fe and N co‐doped mesoporous carbon (MC), as an efficient heterogeneous catalyst for the highly selective reduction of nitroarenes, is described. The Fe and N co‐doped MC nanosheets are easily synthesized via a hydrothermal reaction between citrate acid and magnesium citrate, followed by calcination in the presence of melamine and potassium ferrocyanide. The Fe−N complex provides a unique active site for the selective reduction of 1‐chloro‐4‐nitrobenzene, leading to the production of (E)‐1,2‐bis(4‐chlorophenyl)diazene with a selectivity of >96%, in <40 mins. Control experiments based on non‐doped, N‐doped, and Fe‐doped MC nanosheets demonstrate that selectivity greatly depends on the catalyst active component type, and that non‐doped MC significantly contributes to the high efficiencies observed in the selective synthesis of azoxy compound intermediates. A broad range of substrates, including extra‐functional groups on the nitroarenes rings, were successfully converted to the corresponding azo compounds at mild conditions with high selectivity. magnified image
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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".