PEMFC Catalyst Layers: The Role of Micropores and Mesopores on Water Sorption and Fuel Cell Activity
Bibliographic record
Abstract
The effects of carbon microstructure and ionomer loading on water vapor sorption and retention in catalyst layers (CLs) of PEM fuel cells are investigated using dynamic vapor sorption. Catalyst layers based on Ketjen Black and Vulcan XC-72 carbon blacks, which possess distinctly different surface areas, pore volumes, and microporosities, are studied. It is found that pores <20 nm diameter facilitate water uptake by capillary condensation in the intermediate range of relative humidities. A broad pore size distribution (PSD) is found to enhance water retention in Ketjen Black-based CLs whereas the narrower mesoporous PSD of Vulcan CLs is shown to have an enhanced water repelling action. Water vapor sorption and retention properties of CLs are correlated to electrochemical properties and fuel cell performance. Water sorption enhances electrochemical properties such as the electrochemically active surface area (ESA), double layer capacitance and proton conductivity, particularly when the ionomer content is very low. The hydrophilic properties of a CL on the anode and the cathode are adjusted by choosing the PSD of carbon and the ionomer content. It is shown that a reduction of ionomer content on either cathode or anode of an MEA does not necessarily have a significant detrimental effect on the MEA performance compared to the standard 30 wt % ionomer MEA. Under operation in air and high relative humidity, a cathode with a narrow pore size distribution and low ionomer content is shown to be beneficial due to its low water retention properties. In dry operating conditions, adequate ionomer content on the cathode is crucial, whereas it can be reduced on the anode without a significant impact on fuel cell performance.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".