Diagnosing Degradation within PEM Fuel Cell Catalyst Layers Using Electrochemical Impedance Spectroscopy
Bibliographic record
Abstract
Cost and durability are major challenges for the commercialization of proton exchange membrane (PEM) fuel cells. Accordingly, there remains a need to understand the degradation of fuel cell components to mitigate or eliminate such degradation. Carbon support corrosion and Pt dissolution/aggregation are considered as the major contributors to the degradation of the carbon-supported Pt/Pt alloy catalysts. In this paper, a simple but effective test protocol is proposed in order to accelerate the lifetime testing of PEM fuel cells. An EIS diagnostic protocol has been used along with the standard potential cycling protocol to evaluate the degradation resistance of two different carbon catalyst materials and structures within an insignificant time period. Here we demonstrate how changes in the EIS response are indicative of specific modes of degradation. Thus the addition of EIS testing to accelerated durability protocols is highly recommended for all those studying catalysts layer durability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".