Steam reforming of tar model compounds over ni supported on CeO<sub>2</sub>and mayenite
Bibliographic record
Abstract
Abstract Ni‐CeO2, Ni/Co‐CeO2, and Ni‐Ca12Al14O33were synthesized by the auto‐combustion method and tested as catalysts in the steam reforming of tar model compounds in a fixed bed reactor. Toluene, phenol, andn‐heptane were chosen as representative of the different classes of organic compounds that can be found in tar. The catalysts were characterized by X‐ray diffraction (XRD) and temperature‐programmed reduction (TPR). From XRD analysis it was observed in all the synthesized catalysts the presence of two phases, NiO and CeO2or Ca12Al14O33. A stronger interaction of NiO with mayenite structure, compared to that of NiO with CeO2, was also shown by TPR analysis. The best performances in terms of conversion and stability were obtained when Ni supported on mayenite was used, confirming the higher redox properties of this support that confers to the catalyst a better resistance to deactivation by carbon deposition. The lower performances observed for Ni supported on CeO2in terms of conversion and activity were substantially improved by partial substitution of Ni with Co, confirming its ability to increase the Ni catalytic activity and to enhance the reforming of oxygenated species. The apparent kinetic parameters calculated for all the catalysts and the model compounds confirm the obtained experimental results.
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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".