MIL-101(Cr) supported Pt and Au-Pt composites as active catalysts for the selective hydrogenation of nitrobenzene under mild reaction conditions
Bibliographic record
Abstract
In this study, we report the successful synthesis of monometallic Pt(2 wt.%)/MIL-101(Cr) and bimetallic Au(1 wt.%)-Pt(1 wt.%)/MIL-101(Cr) catalysts with low dimension MeNPs by the double-solvent (DS) method. The catalysts were characterized by powder X-ray diffraction (PXRD), N2 sorption analysis (BET), thermogravimetric analysis (TGA), X-ray photoelectron spectroscopy (XPS), temperature-programmed reduction (TPR), and transmission electron microscopy (TEM). The characterization results demonstrated the presence of well-dispersed Pt and Au-Pt nanoparticles (2.9 and 2.7 nm, respectively), located mainly inside the pores of MIL-101(Cr). The stronger interaction of Au-Pt NPs with the support compared to Pt NPs was demonstrated by H2-TPR and XPS, which proved the existence of charge exchange between Pt NPs and Cr from MIL-101 in the case of Au(1 wt%)-Pt(1 wt%)/MIL-101(Cr), but not in the case of Pt(2 wt%)/MIL-101(Cr). The composite materials were tested in the catalytic selective hydrogenation of nitrobenzene to aniline in liquid phase under mild conditions (low temperature, low hydrogen pressure), and in the presence of a biomass-derived non-toxic solvent (ethanol). The bimetallic Au(1 wt%)-Pt(1 wt%)/MIL-101(Cr) catalyst showed superior catalytic activity as compared to the monometallic Pt(2 wt%)/MIL-101(Cr) catalyst. This is due to the synergetic effect between the two metals as demonstrated by the projected density of states studies.
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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 | 0.000 |
| 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".