Highly Active Ni Nanoparticles on N‐doped Mesoporous Carbon with Tunable Selectivity for the One‐Pot Transfer Hydroalkylation of Nitroarenes with EtOH in the Absence of H<sub>2</sub>
Bibliographic record
Abstract
Abstract Cost‐effective and environmentally friendly conversion of nitroarenes into value‐added products is desirable but still challenging. In this work, highly dispersed Ni nanoparticles (NPs) supported on N‐doped mesoporous carbon (Ni/NC‐x) were synthesized via novel ion exchange‐pyrolysis strategy. Their catalytic performance was investigated for one‐pot transfer hydroalkylation of nitrobenzene (NB) with EtOH in absence of H2. Interestingly, the catalytic performance could be easily manipulated by tuning the morphology and electronic state of Ni NPs via varying the pyrolysis temperature. It was found that the Ni/NC‐650 achieved 100 % nitrobenzene conversion and approx. 90 % selectivity of N,N‐diethyl aniline at 240 °C for 5 h, more active than those of homogeneous catalysts or supported Ni catalysts prepared by impregnation (Ni/NC‐650‐IM, Ni/SiO2). This can be ascribed to the higher dispersion and better reducibility as well as richer surface basicity of the catalyst. More interestingly, the Ni/NC‐650 catalyst achieved complete conversion of various nitroarenes, yielding imines, secondary amines, or tertiary amines selectively by simply controlling the reaction temperature at 180, 200 and 240 °C, respectively. The one‐pot hydrogen‐free process with non‐noble metal catalysts, as demonstrated in this work, shows great promise for selective conversion of nitroarenes with ethanol to various anilines at industrial scale, from an economic, environmental, and safety viewpoint.
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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".