<scp> CO <sub>2</sub> </scp> reforming of methane over the growth of hierarchical <scp>Ni</scp> nanosheets/ <scp> Al <sub>2</sub> O <sub>3</sub> ‐MgO </scp> synthesized via the ammonia vapour diffusion impregnation
Bibliographic record
Abstract
Abstract A novel ammonia vapour diffusion‐assisted impregnation technique was developed to synthesize the Al 2 O 3 ‐MgO‐supported hierarchical Ni nanosheets. The resulting catalysts with different times for ammonia vapour treatments (at 12, 18, and 20 hours) were prepared to investigate the growth of Ni nanosheets on the catalyst surface. All catalysts were tested for CO 2 reforming of methane and a comprehensive characterization study was conducted by XRD, N 2 adsorption‐desorption, H 2 ‐TPD, H 2 ‐TPR, CO 2 ‐TPD, and TGA. The Ni nanosheets were obtained using the ammonia vapour treatment for 20 hours, improving the selectivity toward H 2 generation without a lower CH 4 conversion. When compared to the reference catalyst prepared by a conventional impregnation method, the H 2 /CO ratio for CO 2 reforming of the methane process was enhanced by 0.35. Additionally, the carbon deposition was reduced by half using hierarchical Ni nanosheets for the CO 2 reforming of methane at 620°C for 20 hours. The mechanism of this improvement was achieved by the increase in medium basicity associated with a strong metal‐support interaction that promotes the CO 2 activation‐dissociation pathways, preventing carbon formation and inhibiting the reverse water gas shift.
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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".