Silver/graphene nanocomposites as catalysts for the reduction of <i>p</i>‐nitrophenol to <i>p</i>‐aminophenol: Materials preparation and reaction kinetics studies
Bibliographic record
Abstract
Abstract Ag/graphene nanocomposites have been demonstrated to be promising catalysts for hydrogenation reduction of p ‐nitrophenol ( p ‐NP) to p ‐aminophenol ( p ‐AP). Herein, we reported the synthesis of graphene‐supported silver nanoparticles (Ag/G) using one‐step and stabilizer‐free process. The obtained Ag/G nanocomposites were demonstrated to be efficient catalysts for the reduction of p ‐NP to p ‐AP. The reaction kinetics of the catalytic reduction, including the effects of catalyst concentrations and temperatures are reported. These results are of significant values for the scale‐up of using Ag/graphene catalysts for hydrogenation reduction.
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 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 | 0.001 |
| 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) | 0.000 | 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".