Alternative Fuel Cell Technologies for Cogenerating Electrical Power and Syngas from Greenhouse Gases
Bibliographic record
Abstract
Increasing environment awareness and energy demands are the reasons for emerging energy technologies with ecofriendliness and high efficiency. Of the various candidates, the solid oxide fuel cell (SOFC) is very appealing because of its high efficiency and fuel flexibility. Traditionally, SOFCs directly convert the chemical energies of the readily available fuels into electricity with H 2 O and CO 2 as the products, which is very promising in terms of the energy efficiency yet leads to CO 2 emission in practice. In fact, SOFCs are able to allow in situ CO 2 –CH 4 reforming and H 2 selective electro-oxidation in their anodes. Such a process enables a sustainable path to produce electrical power and syngas from CO 2 but is hindered by several issues. This Perspective discusses the main technical challenges of this process and available approaches achieved so far. The potential future directions for advancing this technology are also pointed out.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".