The Effects of CeO2 and Co Doping on the Properties and the Performance of the Ni/Al2O3-MgO Catalyst for the Combined Steam and CO2 Reforming of Methane Using Ultra-Low Steam to Carbon Ratio
Bibliographic record
Abstract
In this paper, the 10 wt% Ni/Al2O3-MgO (10Ni/MA), 5 wt% Ni-5 wt% Ce/Al2O3-MgO (5Ni5Ce/MA), and 5 wt% Ni-5 wt% Co/Al2O3-MgO (5Ni5Co/MA) catalysts were prepared by an impregnation method. The effects of CeO2 and Co doping on the physicochemical properties of the Ni/Al2O3-MgO catalyst were comprehensively studied by N2 adsorption-desorption, X-ray diffraction (XRD), transmission electron microscopy (TEM), H2 temperature programmed reduction (H2-TPR), CO2 temperature programmed reduction (CO2-TPD), and thermogravimetric analysis (TGA). The effects on catalytic performance for the combined steam and CO2 reforming of methane with the low steam-to-carbon ratio (S/C ratio) were evaluated at 620 °C under atmospheric pressure. The appearance of CeO2 and Co enhanced the oxygen species at the surface that decreased the coke deposits from 17% for the Ni/MA catalyst to 11–12% for the 5Ni5Ce/MA and 5Ni5Co/MA catalysts. The oxygen vacancies in the 5Ni5Ce/MA catalyst promoted water activation and dissociation, producing surface oxygen with a relatively high H2/CO ratio (1.6). With the relatively low H2/CO ratio (1.3), the oxygen species at the surface was enhanced by CO2 activation-dissociation via the redox potential in the 5Ni5Co/MA catalyst. The improvement of H2O and CO2 dissociative adsorption allowed the 5Ni5Ce/MA and 5Ni5Co/MA catalysts to resist the carbon formation, requiring only a low amount of steam to be added.
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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".