Tailoring Flow Field Channel Aspect Ratio for Efficient Mass Transport and Compression in Fuel Cells
Bibliographic record
Abstract
The adverse effects of global warming have made it critical to transition our reliance on fossil fuels to more sustainable energy sources. Polymer electrolyte membrane fuel cells (PEMFCs) can facilitate this transition by providing on-demand power with zero local carbon emissions. However, the high cost and poor durability of fuel cells hinder their widespread adoption. Particularly, PEMFC performance is strongly dependent on the flow fields which should be designed to optimize the transport of reactants and byproducts while maintaining uniform compression with subsequent layers. An important flow field design parameter is the channel aspect ratio which directly influences compression and the transport of reactants and products. Although novel flow field configurations have been studied previously, a comprehensive investigation on the effects of channel aspect ratio on cell performance has yet to be performed. In this study, we compared the electrochemical performance across varying flow field channel aspect ratios (channel width by height) from 0.5-2.0 with a fixed active area. The ohmic and mass transport resistances were quantified using electrochemical impedance spectroscopy. Operando X-ray imaging was performed to spatially resolve water saturation under the land and channel regions of the flow fields. We observed that lower channel aspect ratios (or higher number of channels for the given active area) led to lower ohmic resistance but resulted in higher mass transport losses at higher current densities due to significant water saturation under the hydrophilic ribs of the flow field. Notably, from these results we elucidated that there exists an ideal channel to rib width ratio to facilitate efficient mass transport and effective contact between the porous microstructures.
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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 | 0.001 |
| 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 | 0.001 | 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".