Ni catalysts supported on activated carbon from petcoke and their activity for toluene hydrogenation
Bibliographic record
Abstract
Abstract Petroleum coke (petcoke) is an abundant resource that can potentially be converted to catalyst support materials through activation to increase the surface area and reduce the sulphur content. In this work, potassium hydroxide (KOH) catalysed activation was employed with petcoke to produce activated carbons, which were characterised with nitrogen physisorption, X‐ray diffraction, scanning electron microscopy and temperature‐programmed reduction. With activation temperatures between 500 and 800°C, the surface area increased from 4 m2/g to between 200 and 2400 m2/g while the sulphur content was reduced from 6.6 wt% to between 1 and 0.2 wt%. Nickel catalysts (nominally 5 wt%) were prepared on the activated carbon supports using wet impregnation. The activities of these catalysts were measured for toluene hydrogenation in a plug‐flow reactor with a toluene liquid hourly space velocity of 2.4/h, a pressure of 1.38 MPa, and a H2/toluene mole ratio of 90. The catalytic activity varied between zero for nickel supported on petcoke to 98% conversion, with essentially 100% to methylcyclohexane for nickel supported on carbon activated at 750°C. Thus, activated carbon from petcoke was a suitable support for Ni‐based catalysts when used for toluene hydrogenation as a model reaction. © 2011 Canadian Society for Chemical Engineering
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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 | 0.001 |
| 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".