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 CH4 and CO2) 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 H2 bond activation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".