Nitrogen‐containing microporous carbon with specific morphology for non‐metallic catalytic <scp>NO</scp> oxidation at room temperature: The effect of morphology and nitrogen doping
Bibliographic record
Abstract
Abstract The optimization of the gas diffusion path and surface coordination environment through morphology control can improve the intrinsic activity of the catalyst in NO oxidation reactions. Microporous nanosheets, nanowires, and spheres of carbon were constructed using resorcinol and formaldehyde as carbon sources, melamine as nitrogen source, and graphene oxide or carbon nanowires as structure‐directing agents to reveal the effects of morphology and nitrogen‐doping on NO oxidation activity at room temperature. With the increase of coating thickness, the ultramicroporous structure becomes pronounced and the nitrogen content increases, which contribute to the improvement of steady‐state NO conversion. The 2D microporous nanosheets (TDC‐200) with sheet structure shows prominent diffusion and adsorption capability than 1D nanowires and sphere, which shortens the gas diffusion path and enhances the efficient utilization of ultramicropores, thereby presenting the highest NO oxidation activity of 78.4% at room temperature. The results of DFT calculations further demonstrate that doping of nitrogen atoms could significantly reduce the (2NO + O 2 ) ads energy barrier and accelerate the reaction. This study provides a deeper understanding of the NO oxidation on non‐metallic catalyst.
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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".