Solvent effects on the kinetics of 4-nitrophenol reduction by NaBH<sub>4</sub> in the presence of Ag and Au nanoparticles
Bibliographic record
Abstract
The reduction of 4-nitrophenol (4-NiP) to 4-aminophenol (4-AP) with an excess of sodium borohydride is commonly used as a model reaction to assess the catalytic activity of metallic nanoparticles. This reaction is considered both a potentially important step in industrial water treatment and an attractive, commercially relevant synthetic pathway. Surprisingly, an important factor, the role of the reaction medium on the reduction performance, has so far been overlooked. Here, we report a pronounced effect of the solvent on the reaction kinetics in the presence of silver and gold nanoparticles. We demonstrate that the addition of methanol, ethanol, or isopropanol to the reaction mixture leads to a dramatic decrease in the reaction rate. For typical concentrations of reactants, the reduction is completely suppressed in the presence of 50 vol% alcohols. 4-NiP reduction rate in aqueous alcohol mixtures can, however, be improved noticeably by increasing the borohydride concentration or the reaction temperature. The analysis of various factors responsible for solvent effects reveals that the decrease in the reduction rate in the presence of alcohols is related, amongst others, to a substantially higher oxygen solubility in alcohols compared to water. The results of this work show that the effects of solvent properties on reaction kinetics must be considered for unambiguous comparison and optimization of the catalytic performance of metallic nanoparticles in the liquid phase 4-NiP reduction.
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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 | 0.001 |
| 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 | 0.001 |
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".