Fischer-Tropsch Synthesis: Comparisons of Al2O3- and TiO2-Supported Co Catalysts Prepared by Aqueous Impregnation and CVD Methods
Bibliographic record
Abstract
Fischer-Tropsch synthesis (FTS) is a reaction that converts coal, natural gas, or biomass-derived syngas into long-chain hydrocarbons. This process has gained increasing attention recently, due to limited petroleum resources and increasing oil price.1 Cobalt-based catalysts are widely used and studied. They offer low application temperatures, high selectivities for C5+ products, and low water-gas shift activity.2-6 The active sites for FTS are known to be situated on metallic cobalt phase; therefore, the catalytic activity is a function of both dispersion and cobalt reducibility.5,7,8 In order to improve dispersion of cobalt species, cobalt precursors are preferentially dispersed on porous materials such as SiO2,9 Al2O3,10 and TiO2.11 3.1 Introduction .................................................................................................... 31 3.2 Experimental .................................................................................................. 32 3.2.1 Support Preparation and Cobalt Loading ........................................... 32 3.2.2 Characterization.................................................................................. 33 3.2.3 Catalytic Evaluation ............................................................................34
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".