High protonic resistance of hydrocarbon-based cathodes in PEM fuel cells under low humidity conditions: Origin, implication, and mitigation
Bibliographic record
Abstract
Hydrocarbon-based electrodes for proton-exchange membrane fuel cells face challenges in closing the performance gap with electrodes based on perfluorosulfonic acid ionomers, particularly under low humidity conditions. Alongside increased oxygen transport resistance and higher kinetic-induced overpotentials, the protonic resistance of these fluorine-free electrodes is the primary hurdle to improved performance. This study systematically investigates the origin and impact of the cathode protonic resistance on fuel cell performance, utilizing sulfonated phenylated polyphenylenes as hydrocarbon ionomers. Electrochemical characterization at low relative humidity (≤50 %) reveal a high protonic resistance arising from both lower conductivity of the hydrocarbon thin film compared to the bulk membrane and increased cathode tortuosity at a gas transport-optimized ionomer to carbon (I/C) ratio of 0.2. The poor protonic resistance at low relative humidities leads to a non-homogeneous current distribution across the thickness of the cathode electrode, resulting in lower catalyst utilization. To address this issue, reducing the thickness of the cathode CL while maintaining a constant Pt loading (i.e., increasing the Pt on carbon ratio) significantly reduces protonic resistance. This improvement compensates for the kinetic disadvantages of highly loaded carbon particles and results in a considerable performance increase by 40 % at 0.75 V under low relative humidities. • First thin film (22 nm) measurements on sulfonated phenylated polyphenylenes. • Evidence on HC ionomer distribution in the CL: Tortuosity, possible pore intrusion. • Barely any catalyst contribution near the GDL to ORR due to protonic resistance. • Thin CLs as remediation: 40 % increase in current density at 0.75 V and 50 % RH.
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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 | 0.000 |
| 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 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".