Highly active PtAu alloy nanoparticle catalysts for the reduction of 4-nitrophenol
Why this work is in the frame
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Bibliographic record
Abstract
To enhance the catalytic activity of gold nanoparticles (AuNPs) for the hydrogenation of nitro-aromatic chemicals, Pt was introduced into AuNPs to form "bare" PtAu alloy NPs using a physical approach, pulsed laser ablation in liquid (PLAL), on single metal-mixture targets. These PLAL-NPs are deemed to favor catalysis due to the absence of any surfactant molecules on their unique "bare and clean" surface. The PLAL-NPs were facilely assembled onto CeO2 nanotubes (NTs) by simply mixing them without conducting any surface functionalization, representing another advantage of these NPs. Their catalytic activity was assessed in 4-nitrophenol (4-NP) hydrogenation. The reaction catalyzed by alloy-NP/CeO2-NT catalysts demonstrates a remarkably higher reaction rate in comparison with that catalyzed by pure Au and Pt NPs, and other similar Au and Pt containing catalysts reported recently. A "volcano-like" catalytic activity dependence of the alloy NPs on their chemical composition suggests a strong synergistic effect between Au and Pt in the 4-NP reduction, far beyond the simple sum of their individual contributions. It leads to the significantly enhanced catalytic activity of Pt30Au70 and Pt50Au50 alloy NPs, outperforming not only each single constituent, but also their physical mixtures and most recently reported AuNP based nanocatalysts. The favorable d-band center shift of Pt after alloying, and co-operative actions between Pt clusters and nearby Au (or mixed PtAu) sites were proposed as possible mechanisms to explain such a strong synergistic effect on catalysis.
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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 it