Tuneable bimetallic PdxCu100-x catalysts for selective butadiene hydrogenation
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Bibliographic record
Abstract
Pd-based catalysts play an important role in selective hydrogenation reactions for the removal of polyunsaturated hydrocarbon traces ( e.g. alkadienes) from alkene feedstocks. To improve the selectivity, Pd can be diluted with a more selective metal such as Cu, Ag or Au. We report a detailed study on the effect of the Pd:Cu ratio on the catalytic performance of carbon-supported Cu-rich catalysts for the selective hydrogenation of butadiene in an excess of propylene. Bimetallic Pd x Cu 100-x nanoparticles of 7–8 nm diameter with tuneable Pd content of 0.6–13 % were prepared colloidally. Catalytic turnover frequencies for butadiene hydrogenation increased with Pd-fraction up to 1.0 s −1 for Pd 7 Cu 93 and Pd 13 Cu 87 . The butene selectivity, measured at 90 % conversion, was roughly 80 % for the catalysts with a Pd fraction above 3 % and slightly increased with lower Pd concentrations. Operando X-ray absorption spectroscopy identified an electron density transfer from Cu to Pd in the bimetallic catalysts and a slight preferential clustering of Pd. The trend in catalytic activity was ascribed to an increased Pd ensemble size, indicated by higher Pd-Pd coordination numbers. For bimetallic Pd x Cu 100-x /C catalysts, a Pd content of 3–7 % retains a high selectivity of 78 % at 90 % conversion, while improving the activity 3–4 orders of magnitude compared to pure Cu catalysts. These insights on how to control the activity-selectivity balance through metal nanoparticle compositions contributes to the rational design of bimetallic catalysts for selective hydrogenation reactions. • Well-defined method for tuneable Pd-fraction in PdCu/C catalysts, keeping size and shape constant. • Co-existence of Cu and Pd within individual nanoparticles, with charge transfer from Cu to Pd. • Fraction-dependent activity, which increases with Pd-fraction, while selectivity remains similar.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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