Structural and elemental influence from various MOFs on the performance of Fe@C catalysts for Fischer–Tropsch synthesis
Bibliographic record
Abstract
The structure and elementary composition of various commercial Fe-based MOFs used as precursors for Fischer–Tropsch synthesis (FTS) catalysts have a large influence on the high-temperature FTS activity and selectivity of the resulting Fe on carbon composites. The selected Fe-MOF topologies (MIL-68, MIL-88A, MIL-100, MIL-101, MIL-127, and Fe-BTC) differ from each other in terms of porosity, surface area, Fe and heteroatom content, crystal density and thermal stability. They are re-engineered towards FTS catalysts by means of simple pyrolysis at 500 °C under a N <sub>2</sub> atmosphere and afterwards characterized in terms of porosity, crystallite phase, bulk and surface Fe content, Fe nanoparticle size and oxidation state. We discovered that the Fe loading (36–46 wt%) and nanoparticle size (3.6–6.8 nm) of the obtained catalysts are directly related to the elementary composition and porosity of the initial MOFs. Furthermore, the carbonization leads to similar surface areas for the C matrix ( <italic>S</italic> <sub>BET</sub> between 570 and 670 m <sup>2</sup> g <sup>−1</sup> ), whereas the pore width distribution is completely different for the various MOFs. The high catalytic performance (FTY in the range of 1.9–4.6 × 10 <sup>−4</sup> mol <sub>CO</sub> g <sub>Fe</sub> <sup>−1</sup> s <sup>−1</sup> ) of the resulting materials could be correlated to the Fe particle size and corresponding surface area, and only minor deactivation was found for the N-containing catalysts. Elemental analysis of the catalysts containing deliberately added promoters and inherent impurities from the commercial MOFs revealed the subtle interplay between Fe particle size and complex catalyst composition in order to obtain high activity and stability next to a low CH <sub>4</sub> selectivity.
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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".