Empirical Modeling of Fuel Cell Durability: Cathode Catalyst Layer Degradation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Fuel cells for automotive applications do not yet match the durability and cost of conventional engines. Durability can be improved by better understanding degradation mechanisms of fuel cell components. A critical component is the cathode electrode, which facilitates the slow oxygen reduction reaction. In this work, fuel cells with state of the art electrodes are manufactured and subjected to degradation tests simulating two drive cycle conditions: load cycling, and start-up/shutdown cycling. The degradation data is used to empirically model the voltage loss due to cathode electrode degradation, to predict voltage loss throughout fuel cell lifetime, and compare to findings in literature. The model may be used as a starting point to better understand electrode degradation, and to develop fundamental models. Based on degradation results it is recommended to investigate coupling effects between the different drive cycle conditions, impact of mitigation factors, and effect of different catalyst loadings on the electrode durability.
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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 | 0.001 | 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 it