Investigating the Influence of Humidity on Liquid Water Transport Mechanisms in Fuel Cell Gas Diffusion Layers Using Operando X-Ray Computed Tomography
Bibliographic record
Abstract
The intermittent nature of many renewable energy sources, such as solar and wind, presents the need to acquire on-demand renewable energy either through energy storage or energy production methods. The polymer electrolyte membrane (PEM) fuel cell is quickly becoming an attractive solution as it can produce high power densities under a rapid change in load while producing zero local carbon emissions [1] and is ideal for large-scale applications, such as automotive [2]. However, barriers to their widespread implementation are attributed to the high costs associated with mass transport losses at high current density operation due to inefficiencies in liquid water management in the gas diffusion layer (GDL) [3][4]. Understanding the relationship between liquid water distributions in the cathode GDL and transport properties of PEM fuel cells under varying operating conditions can lead to improved GDL configurations for improved performance. Previously, studies have been conducted to characterize the effect of operating temperature on liquid water pathways in GDLs by using 3D imaging techniques on operando PEM fuel cells [5]. High-speed, high-resolution 3D imaging techniques, such as computed tomography (CT), enable the visualization of dynamic pore-scale effects and are advantageous in elucidating transport mechanisms in the GDL. In this work, the effect of operating relative humidity on the formation of liquid water pathways in the GDL will be explored by imaging an operando PEM fuel cell with synchrotron X-ray CT. To date, a custom PEM fuel cell has been developed in-house which features a novel design with two rotary unions on either side of the cell. The rotary unions enable continuous rotation of the cell during imaging, and thus are central to achieving high temporal resolution for visualizing the development of preferential water pathways. High spatial resolution (pixel resolution of 1.44 microns per pixel [6]) is also obtained with the synchrotron beam, which allows for the visualization of liquid water in individual pores at the microscale in the GDL. Visualizing water in the micropores of the GDL can help us further understand water transport mechanisms within the complex structure of the GDL to ultimately develop methods to expel excess water efficiently. In the next steps of this research, we will use the results from electrochemical testing and the reconstructed images of the fuel cell to draw important conclusions about the relationship between liquid water distributions in the GDL and fuel cell performance. As well, the effect that operating relative humidity has on this relationship will be determined. The aim of this work is to inform optimal design for GDL materials for improved PEM fuel cell performance, which will ultimately accelerate their utilization on a global scale as a reliable and sustainable energy source. [1] P. Shrestha, CH. Lee, K. F. Fahy, M. Balakrishnan, N. Ge, and A. Bazylak, “Formation of Liquid Water Pathways in PEM Fuel Cells: A 3-D Pore-Scale Perspective,” Journal of The Electrochemical Society, vol. 167, no. 5, p. 054516, Jan. 2020, doi: 10.1149/1945-7111/ab7a0b. [2] S. Park, J. W. Lee, and B. N. Popov, “A review of gas diffusion layer in PEM fuel cells: Materials and designs,” International Journal of Hydrogen Energy, vol. 37, no. 7, pp. 5850– 5865, Apr. 2012, doi: 10.1016/J.IJHYDENE.2011.12.148. [3] Y. Nagai et al., “Improving water management in fuel cells through microporous layer modifications: Fast operando tomographic imaging of liquid water,” Journal of Power Sources, vol. 435, p. 226809, Sep. 2019, doi: 10.1016/J.JPOWSOUR.2019.226809. [4] U. U. Ince et al., “3D classification of polymer electrolyte membrane fuel cell materials from in situ X-ray tomographic datasets,” International Journal of Hydrogen Energy, vol. 45, no. 21, pp. 12161–12169, Apr. 2020, doi: 10.1016/J.IJHYDENE.2020.02.136. [5] Hong Xu, Shinya Nagashima, Hai P. Nguyen, Keisuke Kishita, Federica Marone, Felix N. Büchi, Jens Eller, Temperature dependent water transport mechanism in gas diffusion layers revealed by subsecond operando X-ray tomographic microscopy, Journal of Power Sources, Volume 490, 2021, 229492, ISSN 0378-7753, https://doi.org/10.1016/j.jpowsour.2021.229492. [6] Canadian Light Source, “Detectors,” BMIT. [Online]. Available: https://bmit.lightsource.ca/tech-info/detectors/. [Accessed: 05-Apr-2022].
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".