Masking Contaminant-Induced SOFC Anode Degradation with H<sub>2</sub>
Bibliographic record
Abstract
Common contaminants in solid oxide fuel cell (SOFC) fuels, including chlorine and sulfur, are believed to induce anode degradation through one of several, temperature-dependent mechanisms. At relatively low operating temperatures, mechanisms propose that contaminants adsorb to the Ni electrocatalyst, blocking active sites and reducing conversion efficiency. This effect is largely reversible and anodes recover performance when the contaminant is removed from the fuel feed. At higher temperatures, contaminants react with Ni to form non-conducting or volatile materials, thus impeding electrochemical oxidation and destroying anode microstructure. Using both electrochemical measurements and operando vibrational Raman spectroscopy in SOFCs operating at 700°C, we have discovered that small amounts of excess molecular hydrogen added to both methane contaminated with chlorine and a biogas simulant contaminated with chlorine masks, but does not prevent, Cl-induced anode degradation. Once the hydrogen is removed from the incident fuel feed, anodes fail abruptly and irreversibly, implying that Cl-induced degradation proceeded while the hydrogen was present despite impedance and voltammetry data showing no apparent signs of performance loss. Effects are more pronounced and occur more rapidly in SOFCs operating with the biogas simulant (a 50-50 mixture (by mole fraction) of CH 4 and CO 2 ) than for methane. These results are discussed in terms of a new high-temperature degradation mechanism that considers site specific catalytic activity on Ni surfaces and steric requirements for –CH and H 2 bond activation.
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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 | 0.001 | 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".