Bibliographic record
Abstract
Fischer-Tropsch Synthesis (FTS) is a non-crude oil-based technique for conversion of syngas to a wide range of hydrocarbons including light olefins, and liquid transportation fuels.Syngas, which is a combination of H 2 and CO gases can be produced through natural gas reforming, biomass or coal gasification processes.FTS is a catalytic polymerization reaction, therefore rational design of the catalyst including active sites, support and promoters plays a significant role in improving the selectivity of the process to desired range of products.This mini review presents a summary of the different catalyst designs in FTS to synthesize light olefins or heavier hydrocarbons.Iron and cobalt are the industrially used active metals for FTS, due to the lower price and higher hydrogenation activity, respectively.Supports are a key factor in designing efficient FTS catalysts, which provide mechanical, thermal strength for the active sites and a high surface area for dispersion of the active metal particles.Herein we discuss two categories of the widely used support material for FTS: metal oxide supports and carbon materials.Another significant element in formulating a high-performance catalyst for FTS process is the promoters, which can be divided into electronical and structural promoters.This work highlights the roles of active metal, support and promoters in understanding the structure-activity correlations to design high-performance FTS catalysts for production clean and sustainable fuels.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".