Solvent-free selective hydrogenation of nitroaromatics to azoxy compounds over Co single atoms decorated on Nb2O5 nanomeshes
Bibliographic record
Abstract
Abstract The solvent-free selective hydrogenation of nitroaromatics to azoxy compounds is highly important, yet challenging. Herein, we report an efficient strategy to construct individually dispersed Co atoms decorated on niobium pentaoxide nanomeshes with unique geometric and electronic properties. The use of this supported Co single atom catalysts in the selective hydrogenation of nitrobenzene to azoxybenzene results in high catalytic activity and selectivity, with 99% selectivity and 99% conversion within 0.5 h. Remarkably, it delivers an exceptionally high turnover frequency of 40377 h –1 , which is amongst similar state-of-the-art catalysts. In addition, it demonstrates remarkable recyclability, reaction scalability, and wide substrate scope. Density functional theory calculations reveal that the catalytic activity and selectivity are significantly promoted by the unique electronic properties and strong electronic metal-support interaction in Co 1 /Nb 2 O 5 . The absence of precious metals, toxic solvents, and reagents makes this catalyst more appealing for synthesizing azoxy compounds from nitroaromatics. Our findings suggest the great potential of this strategy to access single atom catalysts with boosted activity and selectivity, thus offering blueprints for the design of nanomaterials for organocatalysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".