The Impacts of Microporous Layer Degradation on Liquid Water Distributions in Polymer Electrolyte Membrane Fuel Cells Using Synchrotron Imaging
Notice bibliographique
Résumé
The polymer electrolyte membrane (PEM) fuel cell, featuring high power density and zero-local emissions, is a favorable candidate for replacing the traditional internal combustion engine in automotive vehicles. The gas diffusion layer (GDL) in the PEM fuel cell facilitates water management and transport of reactant gases. The GDL is typically coated with a microporous layer (MPL) to improve water management and minimize contact resistance at the catalyst layer/GDL interface, further enhancing overall cell performance (1, 2). Achieving long-term durability is one of the major obstacles to the realization of commercial success for PEM fuel cells. The majority of PEM fuel cell durability research has been focused on the impacts of catalyst layer and membrane degradation on the cell performance, rather than the GDL. However, high quantities of liquid water accumulation in the GDL were reported in a fuel cell with aged (degraded) GDLs compared to a fuel cell with pristine GDLs, leading to significant performance losses through increased mass transport resistance (3). Furthermore, the MPL experienced up to twice the mass loss from the carbon substrate attributed to a carbon-corrosion based accelerated degradation protocol (4). Therefore, a clear understanding of the effects of MPL degradation on the liquid water behavior via in operando visualization is essential. In this study, as-received pristine GDLs were immersed into a heated hydrogen peroxide solution to perform an accelerated degradation process. The porosity distributions of pristine and degraded GDLs were characterized using desktop micro-computed tomography. Pristine and degraded GDLs were then assembled identically in a customized fuel cell for performance comparison and in operando liquid water visualizations conducted using synchrotron X-ray radiography. Synchrotron X-ray radiography has been demonstrated as a highly precise and accurate tool for in operando visualizing liquid water distribution in PEM fuel cells (5). It was found that the presence of the MPL was beneficial for water management in the GDL, further enhancing the cell performance. However, the MPL itself was more prone to degradation (compared to the substrate), leading to significant performance losses. Figure 1 presents the experimental through-plane liquid water distributions in pristine and degraded Sigracet 29 BC GDLs at an operating current density of 2.0 A/cm2. The leftmost location (x = 0 μm) represents the through-plane position of the catalyst layer/MPL interface and the rightmost location (x= 162.5 μm) represents the through-plane position of GDL/flow field interface. Degradation of the MPL resulted in higher quantities of liquid water in the MPL region, as well as in the carbon substrate region. The impacts of MPL degradation on liquid water behavior will be presented in this work, further highlighting impacts of degradation on liquid water management in long-term fuel cell operation. Reference: 1. J. Lee, J. Hinebaugh and A. Bazylak, J.Power Sources., 227(2013). 2. I. Zenyuk, E. Kumbur and S. Litster, J.Power Sources., 241(2013). 3. H. Liu, M. G. George, N. Ge, R. Banerjee, S. Chevalier, J. Lee, P. Shrestha, D. Muirhead, J. Hinebaugh, R. Zeis, M. Messerschmidt, J. Scholta and A. Bazylak, ECS Trans., 75,14(2016). 4. Liu H., George M.G., Messerschmidt M., Zeis R., Kramer D., Scholta J. and A. Bazylak, Journal of the Electrochemical Society.,(Submitted). 5. S. Chevalier, N. Ge, J. Lee, R. Banerjee, H. Liu, G. George, P. Shrestha, D. Muirhead, J. Hinebaugh and Y. Tabuchi, J.Power Sources., (2016). Figure 1
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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 | 0,000 |
| 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 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».