Investigating the Influence of Humidity on Liquid Water Transport Mechanisms in Fuel Cell Gas Diffusion Layers Using Operando X-Ray Computed Tomography
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».