A Microfluidic Pore Network Approach to Investigate Water Transport in Fuel Cell Porous Transport Layers
Bibliographic record
Abstract
Pore network modelling has traditionally been used to study displacement processes in idealized porous media related to geological flows, with applications ranging from groundwater hydrology to enhanced oil recovery. Very recently, pore network modelling has been applied to model the gas diffusion layer (GDL) of a polymer electrolyte membrane (PEM) fuel cell. Discrete pore network models have the potential to elucidate transport phenomena in the GDL with high computational efficiency, in contrast to continuum or molecular dynamics modelling that require extensive computational resources. However, the challenge in studying the GDL with pore network modelling lies in defining the network parameters that accurately describe the porous media as well as the conditions of fluid invasion that represent realistic transport processes. In this work, we discuss the first stage of developing and validating a GDL-representative pore network model. We begin with a two-dimensional pore network model with a single mobile phase invading a hydrophobic media, whereby the slow capillary dominated flow process follows invasion percolation. Pore network geometries are designed, and transparent hydrophobic microfluidic networks are fabricated from silicon elastomer PDMS using a soft lithography technique. These microfluidic networks are designed to have channel size distributions and wettability properties of typical GDL materials. Comparisons between the numerical and experimental flow patterns show reasonable agreement. Furthermore, the fractal dimension and saturation are measured during invasion, revealing different operating regimes that can be applied to GDL operation. Future work for model development will also be discussed.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".