Ultrasound-assisted impregnation for high temperature Fischer-Tropsch catalysts
Bibliographic record
Abstract
A fraction of the petroleum extracted from oil reservoirs contains associated natural gas. Rather than building infrastructure to recover low volumes of this natural gas, the industry flares or vents it to the atmosphere, which contributes to atmospheric greenhouse gas emissions but also reduces the air quality locally because it contains gaseous sulphur and nitrogen compounds. Converting the natural gas (NG) to hydrocarbons with a small-scale two-step gas-to-liquids process, is an alternative to flaring and venting. In the first step, NG reacts with oxygen to form syngas (Catalytic Partial Oxidation) and in the second step the syngas reacts over metallic catalysts to form higher paraffins at 210 °C to 300 °C—Fischer Tropsch synthesis (FT). For the first time, we synthesize bimetallic FeCo FT catalysts with ultrasound. An ultrasonic horn agitates the solution during the entire impregnation process. The active phase dispersion of the sonicated catalysts was superior to the catalyst synthesized without ultrasound, while reducing the impregnation time by a factor of three. We tested our catalysts in a lab-scale, fixed-bed reactor at 270 °C and 300 °C, and achieved 80% conversion over 3-days on stream and a 40% yield of C2+.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".