Hydrogen production by a new selective partial oxidation of methane in air using reducted La{sub 0.5}Sr{sub 0.5}CoO{sub 3-{delta}} as catalyst for fuel cell applications
Bibliographic record
Abstract
Although methane is one of the most difficult hydrocarbons to oxidize, it has been proposed as a candidate for partial oxidation of catalysts. The majority of catalysts used for total oxidation of methane are supported by noble metals such as platinum (Pt), palladium (Pd), rhodium (Rh), or ruthenium (Ru). However, the search for alternative catalysts has been accelerated because of their excessive cost and relatively limited thermal stability. Strontium (Sr)-doped perovskites have been proposed as cathodes for single oxide fuel cells (SOFCs), because of their high catalytic activity and thermal and chemical stability with the electrolytes at high temperatures. Sr-doped lanthanide (Ln) CoO{sub 3} or LSC is regarded as one of the most promising cathode materials for intermediate temperature SOFCs. This article presented a study that examined the catalytic oxidation of methane on LSC perovskites. Reactions were carried out in a quartz tube at atmospheric pressures, between 500 to 880 degrees Celsius. Considerable changes in product selectivity were observed as a function of temperature, partial gases pressure, total flux rates of the gas mixture and the reduction state of the cobaltite. It was concluded that the oxygen concentration in the LSC perovskite structure significantly changes the selectivity of the methane oxidation. 17 refs., 1 tab., 5 figs.
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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".