Influence of Experimental Conditions on Reliability of Carbon Tolerance Studies on Ni/YSZ SOFC Anodes Operated with Methane
Bibliographic record
Abstract
Abstract The effect of experimental parameters on carbon accumulation in the Ni/YSZ anode of SOFC operated at 1,073 K with CH 4 , has been described in this paper. Experimental parameters including sealing of the cell to the cell holder, operating current, surface coverage by current collector paste on the surface of the anode, and the configuration of the current collector were evaluated in terms of carbon accumulation at the Ni/YSZ anode. The carbon accumulation was quantified using temperature‐programmed oxidation and cells were examined using scanning electron microscopy. The results suggested that variations in any of these experimental parameters could significantly increase or decrease the amount of carbon accumulation on Ni/YSZ anodes, and hence, the reliability of the carbon tolerance studies. In particular, the higher the air leakage rate, the less carbon that accumulated on equivalent anodes. The extent of surface coverage by current collector paste and the configuration of current collector also impacted the amount of carbon accumulation. Less carbon accumulated directly below and near the areas of current collector paste than on the anode areas directly exposed to CH 4 and far from the current collector paste. Additionally, variations in the fuel humidity and current levels also significantly influenced the carbon accumulation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".