Catalytic hydrogenation of polyaromatic hydrocarbon (PAH) compounds in supercritical carbon dioxide over supported palladium
Bibliographic record
Abstract
A series of supported palladium catalysts were evaluated for their ability to mediate the complete hydrogenation of polycyclic aromatic hydrocarbon (PAH) compounds. Benzo[a]pyrene (B[a]P) or phenanthrene (Phe) in hexane was merged with a hydrogen-carbon dioxide [5% (w/w) H(2)/CO(2)] stream and transferred to a flow through mini-reactor (capacity ca. 1 g) that was maintained at 90 degrees C under a back-pressure of 20.68 MPa. Effluent from the reactor trapped in hexane was monitored/quantified by gas chromatography-mass spectrometry. Catalyst formulations supported on iron powder, high density polyethylene (HDPE) or gamma-alumina were prepared and compared in terms of hydrogenation activity as measured by the quantity of substrate per unit time that could be perhydrogenated to toxicologically innocuous products. Both of the Pd preparations supported on gamma-alumina were more efficient than a commercial Pd(0) (5% w/w) on gamma-Al(2)O(3) formulation or preparations supported on HDPE or the iron powder. Bimetallic mixtures with Pd increased the hydrogenation activity when co-deposited with Cu or Ni but not with Ag or Co. However, increases in hydrogenation activity by increasing the loading of Pd (or bimetallic mixture) on this surface were limited. Despite using supercritical carbon dioxide (scCO(2)) to swell the surfaces of the polymer, the deposition of nanoparticles within the polyethylene formulation was appreciably less active than either the oxidic or the Fe(0) formulations.
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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) | 0.000 | 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 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".