Gold Nanoparticle‐Catalyzed Solvent Switchable Selective Partial Reduction of Nitrobenzene to <i>N</i>‐Phenylhydroxylamine and Azoxybenzene
Bibliographic record
Abstract
Abstract Impregnation of phosphine‐decorated polymer‐immobilized ionic liquid with the tetrachloroaurate anion results in reduction of the gold(III) to gold(I) with concomitant oxidation of the phosphine to its oxide. In situ reduction of the resulting precursor, AuCl@O = PPh 2 ‐PEGPIILS, generated the corresponding O = PPh 2 ‐PEGPIIL‐stabilized AuNPs, AuNP@O = PPh 2 ‐PEGPIILS, which is a highly active and selective catalyst for the solvent‐dependent partial reduction of nitrobenzene to N ‐phenylhydroxylamine in water and azoxybenzene in ethanol. The initial TOFs are comparable to those obtained with gold nanoparticles generated by reduction of tetrachloroaurate‐impregnated phosphine oxide‐decorated polymer‐immobilized ionic liquid AuCl 4 @O = PPh 2 ‐PEGPIILS, i.e., the activity and selectivity profiles do not appear to depend on whether the AuNPs are generated from Au(III) or in situ‐generated Au(I). In stark contrast, gold nanoparticles prepared by NaBH 4 reduction of AuCl@PPh 2 ‐PEGPIILS based on gold(I) confined in phosphine‐modified polymer‐immobilized ionic liquid gave markedly lower initial TOFs. The use of dimethylamine borane (DMAB) as the hydrogen donor resulted in a substantial and dramatic enhancement in activity for reductions conducted in water compared with NaBH 4 and the initial TOF of 20,400 mol nitrobenzene converted mol Au −1 h −1 obtained with AuNPs generated in situ from AuCl 4 @O = PPh 2 ‐PEGPIILS is among the highest to be reported for the metal nanoparticle catalyzed selective reduction of nitrobenzene to N ‐phenylhydroxylamine; this is a significant improvement on existing protocols, which should enable the partial selective reduction of nitroarenes to be conducted in water with a low catalyst loading under extremely mild conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".