Catalytic Dry Reforming and Cracking of Ethylene for Carbon Nanofilaments and Hydrogen Production Using a Catalyst Derived from a Mining Residue
Bibliographic record
Abstract
In this study, iron-rich mining residue (UGSO) was used as a support to prepare a new Ni-based catalyst via a solid-state reaction protocol. Ni-UGSO with different Ni weight percentages wt.% (5, 10, and 13) were tested for C2H4 dry reforming (DR) and catalytic cracking (CC) after activation with H2. The reactions were conducted in a differential fixed-bed reactor at 550–750 °C and standard atmospheric pressure, using 0.5 g of catalyst. Pure gases were fed at a molar ratio of C2H4/CO2 = 3 for the DR reaction and C2H4/Ar = 3 for the CC reaction. The flow rate is defined by a GHSV = 4800 mLSTP/h.gcat. The catalyst performance is evaluated by calculating the C2H4 conversion as well as carbon and H2 yields. All fresh, activated, and spent catalysts, as well as deposited carbon, were characterized by Brunauer–Emmett–Teller (BET), X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive X-ray spectrometry (EDX), transmission electron microscopy (TEM), temperature programmed reduction (TPR), and thermogravimetric analysis (TGA). The results so far show that the highest carbon and H2 yields are obtained with Ni-UGSO 13% at 750 °C for the CC reaction and at 650 °C for the DR reaction. The deposited carbon was found to be filamentous and of various sizes (i.e., diameters and lengths). The analyses of the results show that iron is responsible for the growth of carbon nanofilaments (CNF) and nickel is responsible for the split of C–C bonds. In terms of conversion and yield efficiencies, the performance of the catalytic formulations tested is proven at least equivalent to other Ni-based catalyst performances described by the literature.
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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) | 0.000 | 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".