Enhancing the Redox Tolerance of Anode Supported Solid Oxide Fuel Cells by Microstructural Modification
Bibliographic record
Abstract
The most commonly used solid oxide fuel cell (SOFC) anode material is a two phase, nickel and yttria stabilized zirconia (Ni/YSZ) cermet. During fuel cell operation, this material is exposed to a reducing environment and thus remains a cermet. However, the metallic component of the anode may reoxidize in a commercial SOFC system due to situations such as seal leakage, fuel supply interruption or system shutdown. The reduction and oxidation of nickel will result in large bulk volume changes, which may have a significant effect on the integrity of interfaces within a fuel cell and thus result in performance degradation. Following an initial study of the redox kinetics and dimensional changes after reduction and oxidation, as well as a baseline characterization of the electrochemical performance degradation and microstructural changes after redox cycling, two modifications to the anode microstructure were made in order to enhance cell redox tolerance. The Ni content of the AFL was functionally graded in order to produce an AFL layer with minimal expansion during oxidation near the electrolyte and good electronic conductivity and thermal expansion match near the anode substrate. An oxidation barrier layer was printed on the bottom of the cell in order to restrict the ability of oxygen to flow into the anode. Both types of microstructural modification significantly improved the cell redox tolerance compared with standard baseline redox tests.
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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 | 0.000 |
| 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 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".