Mesoporous Ceria Catalyst Synthesis: Effects of Composition on Thermal Stability and Oxygen Depletion in Methane Rich and Lean Environments
Bibliographic record
Abstract
This work takes a closer look at ceria catalyst synthesis through micelle self-assembly. We compare surfactants, precursors, solvent systems, and doping. The surfactants are the building blocks upon which the ceria can crystallize. The samples are calcinated to test their thermal stability. Characterization is performed using pXRD as well as physisorption. The samples that exhibited a higher thermal stability were characterized to have a high surface area as well as low fluctuations in crystallite size, pore volume, and pore size. Ceria synthesized with cerium (III) nitrate hexahydrate and CTAB in a water:ethanol mixture using sodium hydroxide showed to be the most effective at providing a thermally stable product. Doping the catalyst with titanium increased the thermal stability significantly. Select samples were run in a variety of fuel to oxygen ratios to determine the best conditions in which we could perform partial methane oxidation to recuperate hydrogen gas. Most of the experiments show oxygen depletion with minor changes in other gas levels indicating that there is no oxidation occurring. Curiously the oxygen levels do decrease. There is a possibility that there is a reaction occurring initially at room temperature and being exacerbated with further temperature increase.
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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".