Simulation of Performance Tradeoffs in Ceria Supported Polymer Electrolyte Fuel Cells
Bibliographic record
Abstract
Ceria-supported membrane electrode assemblies (MEAs) can effectively protect the membrane at open circuit voltage conditions; however, performance tradeoffs have been observed experimentally with the use of membrane additives. In the present work, a comprehensive, transient in situ membrane durability model for ceria-supported MEAs is developed and applied to investigate the fundamental mechanisms of the performance tradeoffs. The modeling results reveal that proton starvation may occur in the cathode catalyst layer due to local Ce 3 + accumulation and associated reductions in proton conductivity and oxygen reduction kinetics. Significant performance tradeoffs in the form of combined ohmic and kinetic voltage losses are therefore evident and shown to increase with current density. Reduced ceria additive loading and increased cathode ionomer volume fraction are proposed as potential mitigation strategies to reduce the voltage losses caused by proton starvation. A lower initial Ce 3 + concentration is demonstrated to reduce voltage losses without compromising membrane durability at high cell voltages. However, the harmful Fe 2 + concentration in the membrane increases with the Ce 3 + concentration, which suggests that ceria-supported MEAs can experience higher rates of degradation than baseline MEAs at low cell voltages. Strategic MEA design and optimization is recommended in order to ensure membrane durability at low cell voltages.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".