Synthesis and utilization of mesoporous alumina as a supporting material of Ce‐promoted Ni‐based catalysts in the methane reforming process
Bibliographic record
Abstract
Abstract Due to the large applications of hydrogen as a feedstock of chemical industries and as an energy carrier, its production on large scales with low costs has attracted researchers. Steam reforming of methane (SRM) is the most common process for producing H2‐rich syngas over Ni/Al2O3 catalysts, which suffer from coke deposition and Ni particles agglomeration. For overcoming these issues, we have synthesized mesoporous alumina (MA) as a supporting material of Ni particles, structure, and activity, which were compared with the bulk alumina (BA) supported catalysts in the SRM process for the first time. Besides, cerium as an appropriate promoter for lowering deposited coke was added to all prepared catalysts. The reaction temperature (600–700°C), Ni loading (10–25 wt.%), and Ce loading (1–5 wt.%) were the parameters that were optimized for maximizing H2 yield and CH4 conversion. Prepared samples were characterized by various techniques before and/or after reaction. The results of TEM and XRD depicted the formation of nanocrystalline and mesoporous structure for Ni‐MA catalysts compare to Ni‐BA samples. The observations indicated that 20Ni‐3Ce/MA had the highest catalytic performance, achieving a CH4 conversion of 91.0% and H2 yield of 92.8% at 700°C.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".