Bilayer Cathode Structure to Improve Ice Tolerance in PEM Fuel Cells
Bibliographic record
Abstract
In polymer electrolyte fuel cells, the freezing of product water deteriorates fuel cell performance at sub-zero temperatures. When ice forms in the membrane electrode assembly (MEA), catalyst sites are blocked from oxygen resulting in large portions of the MEA to become inactive during Freeze Start- up (FSU). Improving the ice tolerance of the CCM is important to increase the robustness of the MEA to Freeze-Start up failure mechanisms. It is believed that the cathode catalyst layer is one of the key sub-components for ice tolerance, which is the ability of the MEA to accumulate ice before the MEA becomes inoperable. Isothermal Constant Current (ICC) measurements in units of (C/cm2) are commonly used to indicate this capability. ICC data shows a clear correlation of ice tolerance with thickness of the catalyst layer. However, many of the control factors which can be used to improve ice tolerance in MEAs by increasing the thickness of the catalyst layer are limited by design constraints related to material cost and performance trade-offs. It would be difficult to achieve catalyst layer thickness >20um without seriously impacting performance under hot conditions or mass transport at high current densities. Using a carbon/ionomer layer adjacent to the cathode catalyst layer allows to tune-in cathode attributes related to water management and ice distribution under freezing conditions, such as: thickness, porosity, hydrophilicity, interfaces and electric resistance. By changing attributes of the carbon bilayers separately from the cathode catalyst layer, the impact to the ORR reaction under normal or hot/dry operating conditions and mass transport region can be minimized.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".