Characterization of Micro-Porous Layer Structure and Properties
Notice bibliographique
Résumé
In order to accelerate commercialization of polymer electrolyte membrane fuel cells (PEMFCs) as a substitution to internal combustion engines in automotive applications, considerable research efforts have been devoted on the materials used in the system, notably the membrane, catalyst layer, and gas diffusion layer (GDL) of the membrane electrode assembly (MEA). The GDL is one of the vital components of the MEA that has a variety of functions significant for the overall cell performance and durability. It is typically a dual-layer carbon-based material composed of a macro-porous substrate, which usually contains carbon fibers, binder, and PTFE, and a thin, delicate micro-porous layer (MPL), which is usually made of carbon nano-particles and PTFE. The presence of the MPL supports high performance at high current densities, which is important for automotive applications. However, reliable assessment of the MPL structure and properties is a major challenge, and literature data are scarce. In the present study, a customized 3D morphological MPL characterization method developed by our group [1] is applied to analyze the structure and properties of two different MPL materials. The proposed method includes focused ion beam (FIB) milling, scanning electron microscopy (SEM), 3D reconstruction, and material property simulations in order to accurately investigate the MPL microstructure, porosity, pore size distribution, and effective transport properties [1]. Two different dual beam FIB-SEM systems are utilized and compared for high-resolution nano-tomography of the MPL samples: an older FEI Strata DB 235 and a brand new FEI Helios NanoLab TM 650. 3D reconstruction is the most prominent step of the framework, which includes image acquisition, image processing, and segmentation of the captured SEM images of the milled structure. Figure 1 illustrates the 3D structure of the MPL models. Noticeable differences are observed in the two materials; specifically, the second material appears to have a more compact structure with lower porosity. The obtained results are validated with a previously measured MPL pore size distribution (PSD) [1]. Good agreement is observed by comparing the simulated PSD of the first MPL model with the measured data. However, the second MPL model exhibits a systematic shift to smaller pore sizes. The calculated properties reveal several major differences between the two MPL materials: the second material is found to have a 9% lower porosity, smaller pore sizes, a 40% lower effective diffusivity for both oxygen and water vapor, and a 2x higher effective thermal conductivity. All of these differences are attributed to the more compact, low-porosity structure of the second MPL material, having less open pore structure responsible for diffusion of reactants and products. While the lower diffusivity may limit its reactant mass transport effectiveness at high current density operation, the higher thermal conductivity can be beneficial for thermal management of the MEA. Overall, the present MPL characterization framework is demonstrated to accurately detect small variations in the structure of different MPL materials and their impact on the effective transport properties. Acknowledgments: The research was supported by Mercedes-Benz Canada, Fuel Cell Division and the Natural Sciences and Engineering Research Council of Canada. Figure Caption: Figure 1: 3D reconstructed models for the two MPL materials analyzed in this work. A porosity difference is evident by visual comparison of the two structures. References: [1] A. Nanjundappa, A.S. Alavijeh, M. El Hannach, D. Harvey, E. Kjeang, Electrochimica Acta 110 (2013) 349–357.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| 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 | 0,000 |
| 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 tête enseignante, 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 ».