Hydrogen Production for SOFCs Application via Autothermal Reforming of Volatile Organic Compounds on Ru-pyrochlore Catalysts
Bibliographic record
Abstract
Volatile Organic Compounds (VOCs) from industrial coating operations could be valuable energy resources if collected and used effectively. A promising approach is to generate syngas stream for Solid Oxide Fuel Cells (SOFCs) application by autothermal reforming of them. However, conventional nickel-based catalysts with whatever promoters/supports or formulations are not very successful for this process for lacking of activities or deactivating quickly due to sintering or carbon deposition at the presence of high percentage of aromatic compounds or sulfur chemicals. By contrast, there also exist many big challenges for precious metal-based catalysts in terms of longevity and cost effectiveness. In this paper, Ruthenium noble metal dispersed in a formulation of pyrochlore structure was explored, and various VOCs with wide range of monoaromatics additives of various species and fairly amount of naphthalene was used as fuels to simulate actual VOCs. It was found that this catalyst not only had quite good initial activity but also showed very good stability during the longevity tests, which was considered as important as, if not more important than, the former for commercialization. Eventually, actual VOC collected from a painting-booth was tested for this autothermal reforming process. The longevity test verified the high activities and good stabilities of this catalyst. Most likely the precious metal atomically dispersed in a stable structure of pyrochlore contributed to the super performance, although fundamental studies to understand the reaction mechanism was much needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".