Effect of hydrogen addition on formation of hydrogen and carbon from methane decomposition over Ni/Al<sub>2</sub>O<sub>3</sub>
Bibliographic record
Abstract
Abstract The effects of hydrogen addition on the formation of hydrogen and carbon from methane decomposition over Ni/Al2O3 were studied. The results show that the added hydrogen in methane greatly affects the methane conversion, hydrogen output rate, and the properties of the carbon deposits on the surface of the Ni/Al2O3. The methane conversion and hydrogen output rate are significantly improved by the addition of hydrogen. As the flowrate of hydrogen increases from 0 to 25 mL/min, the initial activity of Ni/Al2O3 decreases sharply, while the stability increases first and then decreases due to the suppression of hydrogen to CH4 decomposition in the thermodynamics equilibrium. When the addition flowrate of the hydrogen is 15 mL/min, that is, 37.5% of the methane flowrate, a much higher methane conversion and the best stability of Ni/Al2O3 are obtained. The addition of a specific amount of hydrogen benefits the methane decomposition; however, the excessive hydrogen will suppress the decomposition. Most of the carbon that deposits on the surface of Ni/Al2O3 is filamentous carbon when hydrogen is added to the methane, however, encapsulated carbon is mainly produced when no hydrogen is added. In addition, the formation of encapsulated carbon, which deactivates the catalyst, is inhibited by the added hydrogen.
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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".