Effect of ceria morphology on hydrogen production via methane steam reforming for membrane reformer
Bibliographic record
Abstract
Abstract Hydrogen is a potential energy carrier in comparison to conventional fuels due to its high energy content. Methane is an attractive source for ‘on‐site’ production of hydrogen by using membrane reformer due to its low cost. However, such reformers are not well studied and high temperature operation of steam methane reforming (SMR) makes the integration with membrane separation difficult. Further, the main product of SMR is CO and H 2 in which CO has an inhibition effect on the membrane separation process. Therefore, it is vital to synthesize a low temperature and low CO selective catalyst for a suitable integration with membrane reformer. Nickel‐based catalyst is widely used for SMR due to its low cost and high catalytic activity. CeO 2 is a favoured support as it mobilizes the lattice oxygen and reduces the coke formation and CO selectivity. Though several studies are reported on CeO 2 based support, the effect of CeO 2 surface morphology is not studied for SMR. In the current work, Ni/CeO 2 of different shapes (nanocube and nanorod) are synthesized. The complete characterization of the support was performed. The effect of support shape, calcination temperature, and reduction temperature on SMR activity is found at different operating temperatures. For each condition conversion, CO, CO 2 selectivity, and hydrogen yield are calculated. The results show the CeO 2 morphology has a considerable effect on conversion, CO selectivity, and hydrogen yield. It is found that ceria nanocube calcined at 550°C provides better performance at high temperature.
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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 | 0.001 |
| 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".