Effects of Cathode Corrosion on Through-Plane Water Transport in Proton Exchange Membrane Fuel Cells
Bibliographic record
Abstract
The corrosion of carbon in the cathodes of proton-exchange-membrane fuel cells leads to electrode collapse, reduced active catalyst area, and increased surface hydrophilicity. While these effects have been linked to performance degradation over cell lifetime, the role of corrosion in the evolving water balance has not been clear. In this study, neutron imaging was used to evaluate the through-plane water distribution within several cells over the course of accelerated stress testing using potential holds and square-wave cycling. A dramatic decrease in water retention was observed in each cell after the cathode was severely corroded. The increasing hydrophilic effect of carbon surface oxidation (quantified by ex situ X-ray photoelectron spectroscopy) was overwhelmed by the drying effect of increased internal heat generation. To evaluate this mechanism, the various observed electrode changes are included in a multiphase, non-isothermal one-dimensional cell model, and the simulated alterations to cell performance and water content are compared with those observed experimentally. Simulation results are consistent with the idea that collapse and compaction of the catalyst layer is the dominant limitation to cell performance and not the lower amounts of active Pt surface area, and that higher temperature gradients result in drying out of the cell.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".