Boosting Benzene Oxidation with a Spin‐State‐Controlled Nuclearity Effect on Iron Sub‐Nanocatalysts
Bibliographic record
Abstract
Abstract A fundamental understanding of the nature of nuclearity effects is important for the rational design of superior sub‐nanocatalysts with low nuclearity, but remains a long‐standing challenge. Using atomic layer deposition, we precisely synthesized Fe sub‐nanocatalysts with tunable nuclearity (Fe 1 –Fe 4 ) anchored on N,O‐co‐doped carbon nanorods (NOC). The electronic properties and spin configuration of the Fe sub‐nanocatalysts were nuclearity dependent and dominated the H 2 O 2 activation modes and adsorption strength of active O species on Fe sites toward C−H oxidation. The Fe 1 ‐NOC single atom catalyst exhibits state‐of‐the‐art activity for benzene oxidation to phenol, which is ascribed to its unique coordination environment (Fe 1 N 2 O 3 ) and medium spin state ( t 2g 4 e g 1 ); turnover frequencies of 407 h −1 at 25 °C and 1869 h −1 at 60 °C were obtained, which is 3.4, 5.7, and 13.6 times higher than those of Fe dimer, trimer, and tetramer catalysts, respectively.
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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".