Ultrasound-assisted impregnation for high temperature Fischer-Tropsch catalysts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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+.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 it