The Effect of Flow Channel Surface Properties and Structures on Water Removal and Fuel Cell Performance
Bibliographic record
Abstract
The polymer electrolyte membrane (PEM) fuel cell is a zero emission power generation system that has long been considered as a replacement for conventional fossil fuel combustion systems. However, before constituting a viable market for commercial use, the fuel cell efficiency and reliability need to be improved significantly. It has been shown that water management has significant effect on the power and reliability of the cell as the electrolyte membrane must be well hydrated to allow for ion transfer while excess water blocks the activation sites on the cathode side. The latter effect is known as flooding which occurs at large current densities and compromises the normal operation of the fuel cell. To enhance water management, a prodigious amount of numerical models and experimental studies have been conducted to optimize the properties and structures of different layers. One of the key results of these studies has been the design of the flow field patterns on relatively hydrophobic surface of a graphite plate which is believed to provide a better mechanism for removing water droplets from the cathode flow channel. However, the wettability gradient between the catalyst layer (i.e., hydrophilic) and the flow channel (which is currently hydrophobic) introduces problems as the water droplets formed at the catalyst layer will not likely detach and hence create a film of liquid that will block the activation sites. If the flow channel is made out of a material that is more hydrophilic than the catalyst layer, water removal and transport will be enhanced as water naturally moves from low surface energy to high surface energy sites. Another major factor in controlling water in the PEM fuel cell is the flow field architecture. There has also been a large amount of research on different types of the flow field architectures. However, there have been no studies on the relative performance gains provided by changing the surface properties and the architecture separately. This paper presents an experimental analysis comparing two different flow fields with different surface properties, i.e., a hydrophilic gold flow channel and a hydrophobic graphite flow channel. The paper will also compare three different hydrophilic flow channel architectures: an open gold parallel flow channel, an aluminum foam filled parallel flow channel, and a woven wick filled parallel flow channel. This work will result in finding the optimum geometry and surface properties for achieving maximum performance in the flooding regime.
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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".