Investigating the Effect of Non-Uniform Microporous Layer Intrusion on Oxygen Transport in Dry and Partially Saturated Polymer Electrolyte Membrane Fuel Cell Gas Diffusion Layers
Bibliographic record
Abstract
State-of-the-art gas diffusion layers (GDL) used in polymer electrolyte membrane (PEM) fuel cells have a bi-layered structure comprising of a carbon fiber substrate and a microporous layer (MPL). Addition of the MPL has been shown to improve the fuel cell performance [1-2]. Earlier studies have also shown that the configuration of the MPL relative to the GDL has an effect on gas transport through the combined diffusion medium [3-4]. This study investigates the effect of the MPL intrusion and morphology on the oxygen transport in PEM fuel cell GDLs. In particular, we investigate the effect of varying MPL layer characteristics, both interior and exterior to the carbon fiber substrate in dry and saturated conditions, on the oxygen diffusivity and liquid water saturation at breakthrough conditions. Stochastic generation methods were utilized to create GDLs with varying MPL intrusion depths and morphologies. The generated morphologies of the non-uniform MPL layers were validated using reconstructed microscale X-ray-computed tomography. Pore network modelling was applied to simulate the oxygen transport behavior within these stochastically generated materials. The liquid water saturation was calculated using an invasion percolation algorithm. Three distinct scenarios were studied: varied MPL intrusion depth, varied MPL outer layer thickness, and constant MPL thickness with varied intrusion depth. In controlling the MPL intrusion, it was found that ~50-60 µm is the critical MPL thickness beyond which the saturated oxygen effective diffusion coefficient rapidly increases and the substrate saturation rapidly decreases (Figure 1). This study suggests that when the GDL is invaded by liquid water through cracks in the MPL, preferential MPL configurations exist for improving oxygen transport and minimizing substrate saturation. The results of this study can be used to guide the continued improvement of MPL fabrication. References [1] J. Lee, R. Yip, P. Antonacci, N. Ge, T. Kotaka, Y. Tabuchi, and A. Bazylak, “Synchrotron Investigation of Microporous Layer Thickness on Liquid Water Distribution in a PEM Fuel Cell,” J. Electrochem. Soc., vol. 162, no. 7, pp. F669–F676, Apr. 2015. [2] Z. Qi and A. Kaufman, “Improvement of water management by a microporous sublayer for PEM fuel cells,” J. Power Sources, vol. 109, no. 1, pp. 38–46, 2002. [3] C. Chan, N. Zamel, X. Li, and J. Shen, “Experimental measurement of effective diffusion coefficient of gas diffusion layer/microporous layer in PEM fuel cells,” Electrochim. Acta, vol. 65, pp. 13–21, 2012. [4] N. Zamel, J. Becker, and A. Wiegmann, “Estimating the thermal conductivity and diffusion coefficient of the microporous layer of polymer electrolyte membrane fuel cells,” J. Power Sources, vol. 207, pp. 70–80, 2012. Figure 1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".