Investigation of Ni/SiO2 Fiber Catalysts Prepared by Different Methods on Hydrogen production from Ethanol Steam Reforming
Bibliographic record
Abstract
Ni/SiO2 (Ni/SF) catalysts were prepared by electrospinning of the SF followed by impregnation. The performance of the Ni/SF catalysts for hydrogen production from ethanol steam reforming at various conditions was investigated in comparison with a conventional Ni/silica porous (Ni/SP) catalyst. The influence of the Ni/SF catalyst preparation methods on the catalytic activity and stability in ethanol steam reforming was also studied. The catalysts were prepared by three different preparation techniques: impregnation (IM), deposition precipitation (DP) and strong electrostatic adsorption (SEA). The Ni/SF catalyst exhibited higher performances and stability than the Ni/SP catalyst. The H2 yields of 55% and 47% were achieved at 600 °C using the Ni/SF and Ni/SP catalysts, respectively. The preparation methods had a significant effect on the catalytic activity and stability of the Ni/SF catalyst, where that prepared by the SEA method had a smaller Ni particle size and higher dispersion, and also exhibited the highest catalytic activity and stability compared to the Ni/SF catalysts prepared by IM and DP methods. The maximum H2 yield produced from the catalyst prepared by SEA was 65%, while that from the catalysts prepared by DP and IM were 60% and 55%, respectively, under the same conditions. The activity of the fiber catalysts prepared by SEA, DP and IM remained almost constant at all times during a 16 h stability test.
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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) | 0.001 | 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".