Experimental study and statistical analysis of hydrogen yield in methane steam reforming over a hydroxyapatite-supported nickel catalyst
Bibliographic record
Abstract
This study aimed to investigate the use of hydroxyapatite (HAp) as a catalyst support for the methane steam reforming (MSR) process. Catalysts were prepared via incipient wetness impregnation using nickel as the active metal and characterized by N 2 physisorption analysis, X-ray diffraction, H 2 temperature-programmed reduction, and transmission electron microscopy . The effects of four key parameters in hydrogen production—nickel loading (Ni wt%), inlet steam-to-methane (H 2 O/CH 4 ) ratio, space velocity (SV), and temperature (T)—were evaluated via a statistical analysis. Thermodynamic calculations were also used to compare the catalyst activity results with the equilibrium conditions . The findings confirmed the effectiveness and stability of HAp as a support, exhibiting high thermal stability, coke resistance , and a mesoporous structure with an average specific surface area of 65 m 2 ·g −1 . The calcined Ni/HAp catalyst mainly comprised crystalline NiO and displayed a higher surface area of 54 m 2 ·g −1 and superior dispersion at loadings of 5 and 10 wt% Ni compared to 15 wt% Ni. The temperature, SV, and inlet reactant ratio significantly influenced the process. A long-term stability test carried out under optimal conditions over 98 h demonstrated the consistent activity and stability of the 10 wt%Ni/HAp catalyst, maintaining a high methane conversion of 99 % and yielding 90 % hydrogen, 80 % carbon monoxide , and 20 % carbon dioxide , with no observed carbon deposition on the catalyst surface .
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".