In-Situ Modeling of Chemical Membrane Degradation and Mitigation in Ceria-Supported Fuel Cells
Bibliographic record
Abstract
Ceria-supported membrane electrode assemblies (MEAs) have recently been proposed to address chemical membrane degradation in polymer electrolyte fuel cells. Although ceria is known to effectively protect the membrane at open circuit voltage (OCV) conditions, its effectiveness has not been demonstrated for cell voltages below OCV and associated conditions relevant for field operation. In the present work, a comprehensive, transient in situ chemical degradation model for ceria stabilized MEAs is developed and applied to investigate the mitigation effectiveness of ceria additive. At high cell voltages, abundant Ce 3 + ions are available in the membrane to quench hydroxyl radicals which is the primary mitigation mechanism observed at OCV conditions. However, the mitigation is suppressed at low cell voltages, where electromigration drives Ce 3 + ions into the cathode catalyst layer (CL). Without an adequate amount of Ce 3 + in the membrane, the hydroxyl radical scavenging is significantly reduced, leading to a ten-fold reduction in mitigation effectiveness at cell voltages below 0.7 V. The simulated results also suggest that significant ceria precipitation may occur in the cathode CL due to the increased local Ce 3 + concentration at low to medium cell voltages. Ceria-supported MEAs may therefore experience higher rates of chemical membrane degradation at low cell voltages than at OCV.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".