Metal–Oxide Scale Interfacial Imperfections and Performance of Stainless Steels Utilized as Interconnects in Solid Oxide Fuel Cells
Bibliographic record
Abstract
Ferritic stainless steels currently used as the interconnect materials in solid oxide fuel cells do not exhibit adequate electronic conductivity over expected periods of service. In addition to the relatively poor conductivity of the oxide scales, the metal–oxide scale interfacial defects contribute to significant conductivity losses. In this work, the metal–oxide scale interfacial defects are studied for two grades of stainless steels, AISI-SAE 430 and ZMG232. Scanning electron microscopy, as well as surface science analysis techniques including Auger electron spectroscopy, X-ray photoelectron spectroscopy, and secondary-ion mass spectroscopy, was used to study the metal–oxide scale interface for AISI-SAE 430 and for ZMG232 coupons. Oxide scale spallation occurred during rapid cooling of oxidized AISI-SAE 430 steels. Large, micrometer-sized cavities appeared beneath the spalled scales on AISI-SAE 430. The metal substrate is not in contact with the scale in these locations. Moreover, for this steel, nonmetallic and metallic impurities, such as Si, S, Cl, N, F, Pb, V, and Al, segregate at the metal–oxide scale interface and result in reduced metal-to-scale adhesion and contact area. However, no marked impurity segregation and spallation occur for ZMG232, which contains Zr and La as reactive elements that prevent impurity segregation.
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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 | 0.001 |
| 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 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".