Use of Plasma-Synthesized Nano-Catalysts for CO Hydrogenation in Low-Temperature Fischer–Tropsch Synthesis: Effect of Catalyst Pre-Treatment
Bibliographic record
Abstract
A study was done on the effect of temperature and catalyst pre-treatment on CO hydrogenation over plasma-synthesized catalysts during the Fischer–Tropsch synthesis (FTS). Nanometric Co/C, Fe/C, and 50%Co-50%Fe/C catalysts with BET specific surface area of ~80 m2 g–1 were tested at a 2 MPa pressure and a gas hourly space velocity (GHSV) of 2000 cm3 h−1 g−1 of a catalyst (at STP) in hydrogen-rich FTS feed gas (H2:CO = 2.2). After pre-treatment in both H2 and CO, transmission electron microscopy (TEM) showed that the used catalysts shifted from a mono-modal particle-size distribution (mean ~11 nm) to a multi-modal distribution with a substantial increase in the smaller nanoparticles (~5 nm), which was statistically significant. Further characterization was conducted by scanning electron microscopy (SEM with EDX elemental mapping), X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS). The average CO conversion at 500 K was 18% (Co/C), 17% (Fe/C), and 16% (Co-Fe/C); 46%, 37%, and 57% at 520 K; and 85%, 86% and 71% at 540 K respectively. The selectivity of Co/C for C5+ was ~98% with 8% gasoline, 61%, diesel and 28% wax (fractions) at 500 K; 22% gasoline, 50% diesel, and 19% wax at 520 K; and 24% gasoline, 34% diesel, and 11% wax at 540 K, besides CO2 and CH4 as by-products. Fe-containing catalysts manifested similar trends, with a poor conformity to the Anderson–Schulz–Flory (ASF) product distribution.
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How this classification was reachedexpand
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".