The role of ceria in promoting Ni catalysts supported on phosphate‐modified zirconia for the partial oxidation of methane
Bibliographic record
Abstract
Abstract The catalytic partial oxidation of methane (POM) is aimed at the mitigation of CH 4 (a highly potent greenhouse gas) from the environment and the synthesis of syngas with a high H 2 /CO ratio. Herein, to enhance the POM reaction, Ni‐supported phosphate‐modified‐zirconia were synthesized with promotor “Ce” to achieve high H 2 /CO ratio (2.4–3.2). The catalysts were characterized by surface area and porosity, X‐ray diffraction, RAMAN, temperature‐programmed experiments (TPR, CO 2 ‐TPD, and TPO), and TEM. Increasing the ceria addition over 10Ni/PO 4 + ZrO 2 resulted in lower crystallinity, higher dispersion of active sites, and enhanced the surface area of catalyst. The unique and prominent reducibility and basicity of NiO‐species and surface oxide ions, respectively, are particularly notable at 4 wt.% ceria loading. At a reaction temperature of 600°C, the highest concentration of active sites and a unique concentration of moderate strength basic sites can be achieved with 4 wt.% ceria loading over 10Ni/PO 4 + ZrO 2 . This leads to 44% conversion of CH 4 , 36% yield of H 2 , 35% yield of CO 2 , and H 2 /CO ratio of 3.16 for the POM reaction. The cyclic H 2 TPR‐O 2 TPO‐H 2 TPR experiment confirms the reorganization of the active site towards high temperature under oxidizing gas O 2 and reducing gas H 2 gas stream during the POM reaction.
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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".