On the Active Surface State of Nickel‐Ceria Solid Oxide Fuel Cell Anodes During Methane Electrooxidation
Bibliographic record
Abstract
Abstract Solid oxide fuel cells (SOFCs) have grown in recognition as a viable technology able to convert chemical energy directly into electricity, with higher efficiencies than conventional thermal engines. Direct feeding of the SOFCs anode with hydrocarbons from fossil or renewable sources, appears more attractive compared to the use of hydrogen as a fuel. The addition of mixed oxide‐ion/electron conductors, like gadolinium‐doped ceria (GDC), to commonly used nickel‐based anodes is a well–known strategy that significantly enhances the performance of the SOFCs. Here we provide in situ experimental evidence of the active surface oxidation state and composition of Ni/GDC anodes during methane electroxidation using realistic solid oxide electrode assemblies. Ambient pressure X‐ray photoelectron and near edge X‐ray absorption fine structure spectroscopies (APPES and NEXAFS respectively) combined with on line electrical and gas phase measurements, were used to directly associate the surface state and the electrocatalytic performance of Ni/GDC anodes working at intermediate temperatures (700°C). A reduced anode surface (Ce3+ and Ni), with an optimum Ni to Ce surface composition, were found to be the most favorable configuration for maximum cell currents. Experimental results are rationalized on the basis of first principles calculations, proposing a detailed mechanism of the cell function.
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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".