Pore Network Modelling of Capillary Transport and Relative Diffusivity in Gas Diffusion Layers with Patterned Wettability
Bibliographic record
Abstract
Engineering the wettability and microstructure of gas diffusion layers offers a powerful strategy to optimize water management in polymer electrolyte fuel cells. To this goal, we recently developed a radiation grafting technique to synthesize GDLs with patterned wettability. Despite the promise of this approach, current designs are empirically-driven which hampers the development of advanced wettability patterns. To design materials with improved transport characteristics over a range of water saturations, physically representative models can be employed for the bottom-up design of gas diffusion layers with local variations in hydrophilicity. In this paper, pore network models using topology and size information extracted from high resolution tomographic images of three common gas diffusion layer materials are presented with patterned wettability. We study the influence of the substrate microstructure, the hydrophobic coating load, and the hydrophilic pattern width. It is shown that tuning the wettability leads to enhanced phase separation and increased diffusive transport which is attributed to decreased gas phase tortuosity. The network model elaborates on previous experimental studies, shedding light on the effectiveness of the radiation pattern transference and the importance of matching the masking pattern with the substrate microstructure. The work opens the door for exploration of advanced patterns, coupled with flow from gas flow field designs.
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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 | 0.001 |
| 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".