Production of C<sub>4</sub> Hydrocarbons from Modified Fischer−Tropsch Synthesis over Co−Ni−ZrO<sub>2</sub>/Sulfated-ZrO<sub>2</sub> Hybrid Catalysts
Bibliographic record
Abstract
Fischer−Tropsch synthesis was carried out at atmospheric pressure in a fixed-bed microreactor at temperatures and weight hourly space velocities (WHSV) ranging from 513 to 533 K and 5 to 25 h - 1, respectively, over hybrid catalysts (physical mixtures) containing Co−Ni−ZrO 2 and sulfated-ZrO 2 catalysts. The sulfated-ZrO 2 /Co−Ni−ZrO 2 catalyst weight ratios (SZ/CN) ranged from 0 to 3, whereas sulfate concentrations in sulfated-ZrO 2 catalyst (sulfate loading) ranged from 5 to 15 wt %. Fischer−Tropsch synthesis over Co−Ni−ZrO 2 catalyst alone produced a maximum C 4 hydrocarbon selectivity of 14.6 wt % at a temperature of 523 K and WHSV of 15 h - 1 . There was an impressive increase in C 4 hydrocarbons selectivity to a maximum of 32.4 wt % when catalyst HB5,1 (SZ/CN of 1 and sulfate loading of 5 wt %) was used. This catalyst also gave an extremely high selectivity for isobutane (maximum of 10.6 wt % of total hydrocarbon products) as compared to 0.1 wt % obtained with Co−Ni−ZrO 2 catalyst. A time-on-stream study on catalyst HB5,1 showed a decrease in activity of this catalyst with reaction time. In contrast, the use of hybrid catalyst HB5,0.5 (SZ/CN of 0.5 and sulfur loading of 5) where the overall sulfur content was low resulted in almost no deactivation. However, the activity obtained in the case of catalyst HB5,0.5 was lower than that obtained for catalyst HB5,1 but was much higher than that for Co−Ni−ZrO 2 catalyst. On the other hand, for hybrid catalysts HB5,2 and HB15,1,which had high overall concentrations of sulfur, there was no activity at all. The results show that interactions brought about by close proximity of Fischer−Tropsch catalyst active sites and acid sites produce favorable effects when the overall sulfur content in the hybrid catalyst is low.
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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) | 0.001 | 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".