Highly active PtAu alloy nanoparticle catalysts for the reduction of 4-nitrophenol
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.
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 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".