Comparing<i>in Situ</i>Carbon Tolerances of Sn-Infiltrated and BaO-Infiltrated Ni-YSZ Cermet Anodes in Solid Oxide Fuel Cells Exposed to Methane
Bibliographic record
Abstract
Experiments performed in this work explored how Ni-YSZ cermet anodes infiltrated with 1% Sn or 1% BaO mitigate carbon formation compared to undoped Ni-YSZ anodes in functioning solid oxide fuel cells (SOFCs). In situ vibrational Raman spectroscopy was used to study the early stages of carbon accumulation on the SOFC anodes at 730 °C with methane and under open circuit voltage (OCV) conditions. Additionally, carbon removal with different gas phase reforming agents was evaluated. The effects of these phenomena—carbon accumulation from methane and carbon removal by reforming agents—on the electrochemical capabilities of a device were monitored with electrochemical impedance and voltammetry measurements. Vibrational spectra showed that the undoped and 1% Sn infiltrated anodes were very susceptible to carbon formation from methane while considerably less carbon accumulated on the 1% BaO anodes. Electrochemical data, however, implied that carbon accumulated in different regions of the anode and that both Sn and BaO effectively reduced carbon accumulation but also inhibited electrochemical oxidation. For each anode, H 2 O was the most effective reforming agent for removing carbon followed by O 2 and then CO 2 . H 2 O and CO 2, however, left the anode only partially oxidized, while prolonged exposure to O 2 completely oxidized Ni to nickel oxide. The spectroscopic and electrochemical data showed strong correlations that provide mechanistic insight into the consequences of adding secondary materials to SOFC anodes with the intent of reducing carbon accumulation.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".