High-selective Fischer–Tropsch synthesis to jet fuel over confined iron catalysts inside carbon nanocages
Bibliographic record
Abstract
Selective production of specific products, such as jet fuel, in Fischer–Tropsch synthesis (FTS) is a huge challenge due to the Anderson–Schulz–Flory (ASF) distribution law. Herein, by filling K-promoted Fe-based active species, which usually produces medium-to-short chain hydrocarbons in high-temperature FTS, into the hierarchical carbon nanocages (hCNC), jet fuel with high selectivity of 60% is directly obtained in FTS at 300 °C, exceeding the ASF maximum limitation of ca. 41%. Through the theoretical simulations, we attribute this performance to the CO enrichment inside the nanocavities due to the sieving effect of the micropores across the hCNC shells (~ 6 Å) and the increased collision frequency in confined space. These two factors thereby promote the CO conversion and carbon-chain growth longer over the catalytically active Fe5C2 phase, resulting in the remarkable selectivity to jet fuel. The effects of the length and size of micropores on the CO/H2 diffusion and FTS performance are examined, which corroborate the crucial role of micropores in the high-selective FTS to jet fuel. This work not only provides a remarkable catalyst to the selective jet fuel synthesis, but also offers an alternative way to design advanced catalysts for FTS.
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.
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".