Screening of Heterogeneous Multimetallic Nanoparticle Catalysts Supported on Metal Oxides for Mono-, Poly-, and Heteroaromatic Hydrogenation Activity
Bibliographic record
Abstract
A series of oxide supported mono-, bi-, and trimetallic nanoparticle catalysts were synthesized and screened for catalytic activity for the hydrogenation of mono-, poly-, and heteroaromatic substrates. Seventy-two different catalysts were screened for catalytic activity for the hydrogenation of toluene, naphthalene, pyridine, indole, quinoline, thiophene, and benzothiophene under mild conditions; five of these seven substrates were successfully hydrogenated under the reaction conditions. Bulk kinetic studies, including temperature and pressure studies, were performed using select catalysts for the hydrogenation of one hydrocarbon (naphthalene) and one-heteroatom substituted-substrate (quinoline). A quinoline loading study was also conducted in which the ratio of substrate/catalyst was varied. Standard materials characterization techniques including transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), and X-ray diffraction (XRD) were also used to acquire information about the size, oxidation state, and crystallinity of the nanoparticle catalysts.
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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".