Mechanical mixtures of Me (Ni, Pd) Ce oxides and silica-supported heteropolyacids: Role and optimal concentration of each active species in n-hexane isomerization
Bibliographic record
Abstract
Catalytic properties of silica-supported heteropolyacids (HPA) in a mechanical mixture with reduced Me-Ce oxides (Me = Ni, Pd) in n -hexane isomerization are studied. The role of each component of the mixed oxides (Ce and, typically, Ni and Pd) and their optimum content has been illuminated: cerium is not only beneficial for eliminating or preventing coke deposition but is also effective for maintaining the Keggin structure of the highly-organized HPA during the reaction and probably allows a better dispersion of the second metal species. Nickel and palladium, present as Ni 0 and Pd 0 , reinforce the activation of the alkane, which is difficult to obtain by means of a direct attack by an acid, and, thus, enhance noticeably the activity of the catalyst. The best mechanical mixtures are obtained with 30–70 wt % NiCeO-HPW/SiO 2 and 50–50 wt % Pd 0.1 CeO-HPW/SiO 2 . These mixtures have the highest efficiency for a Ni/(Ni + W) atomic ratio of 0.66 and a Pd/(Pd + W) ratio of 0.40, respectively. Finally, the conversion of n -hexane is in the order HPW > HSiW > HBW, which seems to be consistent with the order of their acid strength as per the literature, but the isomerization selectivity appears to be slightly higher on HSiW.
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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".