Enhancing Sulfur Tolerance of Ni-Based Cermet Anodes of Solid Oxide Fuel Cells by Ytterbium-Doped Barium Cerate Infiltration
Bibliographic record
Abstract
Conventional anode materials for solid oxide fuel cells (SOFCs) are Ni-based cermets, which are highly susceptible to deactivation by contaminants in hydrocarbon fuels. Hydrogen sulfide is one of the commonly existed contaminants in readily available natural gas and gasification product gases of pyrolysis of biomasses. Development of sulfur tolerant anode materials is thus one of the critical challenges for commercial viability and practical application of SOFC technologies. Here we report a viable approach to enhance substantially the sulfur poisoning resistance of a Ni-gadolinia-doped ceria (Ni-GDC) anode through impregnation of proton conducting perovskite BaCe0.9Yb0.1O3-δ (BCYb). The impregnation of BCYb nanoparticles improves the electrochemical performance of the Ni-GDC anode in both H2 and H2S containing fuels. Moreover, more importantly, the enhanced stability is observed in 500 ppm of H2S/H2. The SEM and XPS analysis indicate that the infiltrated BCYb fine particles inhibit the adsorption of sulfur and facilitate sulfur removal from active sites, thus preventing the detrimental interaction between sulfur and Ni-GDC and the formation of cerium sulfide. The preliminary results of the cell with the BCYb+Ni-GDC anode in methane fuel containing 5000 ppm of H2S show the promising potential of the BCYb infiltration approach in the development of highly active and stable Ni-GDC-based anodes fed with hydrocarbon fuels containing a high concentration of sulfur compounds.
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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) | 0.000 | 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 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".