Screening of Bimetallic Heterogeneous Nanoparticle Catalysts for Arene Hydrogenation Activity under Ambient Conditions
Bibliographic record
Abstract
This study focuses on the application of a simple screening approach to prepare and test heterogeneous mono- and bimetallic nanoparticle (NP) catalysts for arene hydrogenation activity under ambient conditions in a quick and time efficient manner, as well as detailed testing and characterization of identified active catalysts. Over 90 mono- and bimetallic NP catalysts supported on alumina were efficiently screened for arene hydrogenation activity under ambient conditions using toluene as a model substrate. Through this approach, four catalysts were determined to be active: RhPt/Al(2)O(3), RuPt/Al(2)O(3), IrPt/Al(2)O(3), and IrRh/Al(2)O(3). These catalysts were further synthesized and tested in bulk, and RhPt/Al(2)O(3) was confirmed to be the catalyst with the highest observed rate of all the bimetallic combinations screened. Further studies were then performed, and the metal loading, temperature, pressure, and substrate to metal ratios were varied to determine the effects of these variables on the activity of the RhPt/Al(2)O(3) catalyst and a CS(2) poisoning study was performed on this catalyst to determine the number of active sites. From the temperature studies, the activation energy was calculated to be 30.4 kJ/mol, which is moderate when compared to other arene hydrogenation catalysts (42.0 kJ/mol for the hydrogenation of toluene with Ru NPs, (1) and 34.7 and 45.6 kJ/mol for benzene hydrogenation with cuboctahedral and cubic Pt NPs, respectively, (2) have been reported). Transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), and Brunauer-Emmett-Teller (BET) surface area measurements were used to characterize the active catalysts, where it was observed that very small zero oxidation state metal NPs were well dispersed throughout the high-surface area alumina support.
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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 | 0.000 |
| 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 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".