Unlocking the door to highly efficient Ag-based nanoparticles catalysts for NaBH4-assisted nitrophenol reduction
Bibliographic record
Abstract
Ag-based nanoparticles (NPs) catalysts have recently attracted increasing attention in NaBH 4 -assisted nitrophenol reduction, especially in 4-nitrophenol (4-NP) reduction. Moreover, Ag-based NPs catalysts are considered to be very promising for practical applications because of their fascinating advantages, e.g., easy preparation, relatively low cost and less toxicity, high activity and good stability. Basically, the size and shape of Ag NPs are well known as the key factors for achieving highly efficient catalytic reduction of 4-NP. In this review, three highly efficient Ag-based NPs catalysts (supported Ag NPs, anisotropic Ag NPs and bimetallic Ag NPs) are highlighted for the 4-NP reduction, including the catalytic mechanism and reaction rate caused by their adjustments in size and shape. Although high catalytic activity has been demonstrated by several Ag-based NPs catalysts, further improvement in the catalytic performance is still desired. In terms of the most recent progress in Ag-based NPs catalysts for 4-NP reduction, this review provides a comprehensive assessment on the material selection, synthesis and catalytic characterizations of these catalysts. Moreover, this review aims to correlate the catalytic performance of Ag-based NPs catalysts with their size and shape, guiding the development of novel cost-effective and high-performance catalysts.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".