Utilization of sol‐gel CuO‐ZnO‐Al<sub>2</sub>O<sub>3</sub> catalysts in the methanol steam reforming for hydrogen production
Bibliographic record
Abstract
Abstract In this work five CuO‐ZnO‐Al2O3 catalysts were synthesized using the sol‐gel method, with different Cu percentages, for use in the methanol steam reforming reaction, at 300 °C, aiming to generate hydrogen for PEM (Polymer Electrolyte Membrane) fuel cells. The specific area and total pore volume of the materials decreased with increasing Cu content and consequently reduced the alumina content. Also, the area decreased with increasing the calcination temperature, due to the sintering and coalescence of Cu crystals. The metal dispersion decreased (from 32 to 4 %) with increasing the Cu amount (from 8.9 to 48.4 %). For this reaction, the catalyst with the second highest Cu concentration (40.6 %) was the most active and had the higher Cu area (34.8 m2Cu/gcat). Meanwhile, the catalyst with the lowest Cu content (8.9 %) had its active sites better used, presenting the highest turnover frequency, specific area, and metal dispersion. The experimental results indicated that there was an optimum composition for the catalyst, which would provide the best area and dispersion for the reaction, with a view to industrial application. This composition was statistically calculated to be 33 % (g/g) of Cu.
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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".