Investigating the Effect of Channel Width and Surface Wettability on Liquid Water Content in PEMFCs Using Synchrotron Radiography
Bibliographic record
Abstract
Polymer electrolyte membrane (PEM) fuel cells show promise as electrochemical energy conversion devices for transport, stationary, and portable applications. Despite their potential for high efficiencies, the widespread adoption of PEM fuel cells has been hindered by their current durability and cost. These obstacles are related to a lack of fundamental understanding of the water transport mechanisms in the gas diffusion layers (GDLs) [1]. Another aspect in the improvement of fuel cell performance requires an understanding of the effects of flow channel geometry and wettability on water management in the PEM fuel cells. For instance, liquid water transferred from the GDL to the flow channel may be constrained by water slugs in the channel, which may result in increased saturation inside the GDL [2,3]. Moreover, liquid water flux from the GDL can be influenced by the rib/channel geometry due to reaction distribution, heat transport, and water transport [2,3]. In this study, a PEM fuel cell, with an active area of 0.48 cm2, was designed for synchrotron imaging to acquire through-plane radiographs of the cell. Using Toray GDLs coated with an MPL (TGP-H-60), each fuel cell experiment was performed at specified operating conditions controlled with a Scribner 850e test station. Two different channel geometries were studied: one with a channel width of 0.6 mm; the other, 0.2 mm. In addition, the effect of channel wettability was also investigated. For each channel geometry, two sets of bipolar plates were prepared; they were either coated with a hydrophilic or hydrophobic coating. Visualizations of the dynamic liquid water content in operating fuel cells were performed at the Biomedical Imaging and Therapy – Bending Magnet (05B1-1) beamline at the Canadian Light Source Inc. (Saskatoon, Canada) using conventional absorption imaging. A monochromatic X-ray beam set to an energy level of 18 keV was used to acquire two- dimensional radiographs. By applying the principles of the Beer-Lambert law, the raw radiographs were processed to isolate the water content, in the form of a water thickness distribution. The results of the current study aim to improve the understanding of how the channel surface wettability and channel dimensions influence the water management of PEM fuel cells. This is important as the channel water morphology can have an effect on the GDL water distribution, which is linked to the overall fuel cell performance. As the interface between the liquid water in the GDL and the liquid water in the channel is not fully understood, this study will shed light on interface effect by taking advantage of the fine spatial and time resolutions of the synchrotron X-ray radiography. References [1] M. Mathias, J. Roth, J. Fleming, and W.L. Lehnert, "Diffusion media materials and characterisation," in Handbook of Fuel Cells: Fundamentals, Technology, and Applications, W. Vielstich, A. Lamm and H. A. Gasteiger, Eds. Chichester, England; Hoboken, N.J.; Wiley, 2003, pp. 1-21. [2] R. Anderson, L. Zhang, Y. Ding, M. Blanco, X. Bi, and D.P. Wilkinson, "A critical review of two-phase flow in gas flow channels of proton exchange membrane fuel cells," J. Power Sources, 195, pp. 4531-4553, 2010. [3] A. Herescu and J.S. Allen, "The influence of channel wettability and geometry on water plug formation and drop location in a proton excxhange membrane fuel cell flow field," J. Power Sources, 216, pp. 337-344, 2012.
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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 | 0.000 | 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".