Preparation of a Novel Super Active Fischer-Tropsch Cobalt Catalyst Supported on Carbon Nanotubes
Bibliographic record
Abstract
The potential of carbon nanotubes (CNT) supported cobalt catalysts for Fischer-Tropsch (FT) reaction is shown. Using the wet impregnation method cobalt on carbon nanotubes catalysts were prepared with cobalt loading varying from 15 to 45 wt. %. The catalysts are characterized by different methods including: BET physisorption, X-ray diffraction, hydrogen chemisorption, and temperature-programmed reduction. The activity and product selectivity of the catalysts were assessed and compared with alumina supported cobalt catalysts using a continuous-stirred tank reactor (CSTR). Using carbon nanotubes as cobalt catalyst support was found to decrease the reduction temperature of Co3O4 to CoO from 440 to 347 oC and that of CoO to Coo from 640 to 574 oC. The strong metal-support interactions are reduced to a large extent and the reducibility of the catalysts improved by 67.7 %. CNT aided in well dispersion of metal clusters and average cobalt clusters size of the reduced cobalts is decreased from 5.3 to 4.9 nm. Results are presented showing that the hydrocarbon yield obtained by CNT supported cobalt catalyst is 74.6 % more than that obtained from cobalt on alumina supports. The maximum concentration of active surface Coo sites and FTS activity for CNT supported catalysts are achieved 40 wt. % cobalt loading. CNT caused a slight decrease in the FTS product distribution to lower molecular weight hydrocarbons.
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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) | 0.001 | 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".