Selective Production of C<sub>4</sub> Hydrocarbons from Syngas Using Fe‐Co/ZrO<sub>2</sub> and SO<sub>4</sub><sup>2—</sup>/ZrO<sub>2</sub> Catalysts
Bibliographic record
Abstract
Abstract Modified Fischer‐Tropsch (MFT) syntheses were carried out to convert synthesis gas to C4 hydrocarbons over Fe‐Co/ZrO2 (FT) and SO42—/ZrO2 (SZ) catalysts in a dual reactor system, keeping the FT to SZ catalysts ratio at 1:1.5. Five Fe‐Co/ZrO2 catalysts with different Fe and Co loading, and SZ with 15 wt% SO42— were prepared and extensively characterized using various physico‐chemical methods. The FT synthesis process was initially performed using a Fe‐Co/ZrO2 catalyst in a single reactor and the effects of Fe and Co mass ratio, reaction temperature, space velocity on the production of C4 hydrocarbons and C2‐C4 olefins were investigated. Results indicated that a 3.71% Fe—8.76% Co/ZrO2 mixed oxide catalyst alone at 260°C and 5 h—1 gave high selectivities of C2‐C4 olefins (∼26.1 wt%) and total C4 hydrocarbon product (∼16.2 wt%). The MFT process 150°C gave higher C4 (∼31.6 wt%), isobutane (∼22.9 wt%) and C2‐C4 (31.1 wt%) selectivities.
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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".