Investigation of Membrane Degradation in Fuel Cells through Nanoscale Electron Tomography
Notice bibliographique
Résumé
Operational reliability of polymer electrolyte fuel cells (PEFCs) depends on the durability of the components used in its construction. Membrane failure is a life-limiting factor in PEFCs [1] which develops across multiple length scales. Hence, an articulated knowledge congregating known degradation mechanisms at various scales is necessary to draw a complete understanding. The molecular level degradation of perfluorosulfonic acid (PFSA) membranes in PEFCs was found to initiate with OH radicals attacking side chain C-S and α-OCF2 bonds proceeding with further side chain degradation through β-OCF2and culminating in main chain cleavage and unzipping [2]. The molecular level degradation has a profound influence on the membrane macroscale stability [3]. The physical integrity of PFSA ionomer membranes depends on their internal morphology and distribution of hydrophilic and hydrophobic phases [4]. The degradation effects at an intermediate length scale therefore requires in depth study to bridge the molecular and macroscale degradation. Our preliminary research on the mesoscale characterization of membrane degradation was conducted using 2-D electron micrographs and compositional analysis and revealed that membrane failure initiated as local damage and cracks propagated along the direction of low concentration of ion-rich regions as a result of combined chemical/mechanical degradation [4]. However, the 2-D nature of these results constrained the ability to determine hydrophilic and hydrophobic phase volume fractions and their shapes, sizes, and distribution. Therefore, the objective of the present work is to investigate the mesoscale morphology and quantify the effects of PFSA membrane degradation with PEFC operation through tomographic reconstruction of hydrophilic and hydrophobic phases in three dimensions. In this work, standard PFSA ionomer membranes were subjected to a combined chemical and mechanical accelerated stress test (AST) used for rapid benchmarking of in-situ membrane stability [3]. The chemical phase of the AST generates hydroxyl radicals that attack both the side chain and main chain of the polymer, while the mechanical stress generated by humidity cycling accelerates mechanical degradation and failure. Contrast enhanced transmission electron microscopy-tomography (TEM-t) of pristine and degraded PFSA ionomer membranes was carried out to explore the overall morphology and mesoscale features induced by the combined chemical and mechanical degradation process. In order to enhance image contrast, the sulfonic acid end group sites in the membrane were selectively exchanged with Pb ions by soaking it in saturated lead acetate solution. The samples embedded in epoxy resin were sliced to thin films (~70-90 nm) using ultra microtome and collected on a Cu grid for imaging. Nanoscale 3-D image reconstruction followed by Marker-based Watershed segmentation was used to identify weak phase boundaries [5] in the tomograms and thereby reveal the hydrophilic and hydrophobic phase distributions in the membrane. The pristine membrane exhibited a randomly interconnected hydrophilic phase with a scaffolding hydrophobic phase (Figure 1(a)), as expected from morphological theory. The membrane subjected to combined chemical/mechanical degradation was found to have similar overall morphology while containing thinner ionomer bundles and reduced hydrophilic volume fraction with smaller hydrophilic pores in regions with elevated chemical degradation as shown in Figure 1(b). Numerical analysis of the phase-segmented tomograms was performed to achieve detailed quantification of the structural properties, which was not possible with previously applied 2-D approaches and therefore contributes important new information for nanoscale analysis of fuel cell membranes. ACKNOWLEDGMENTS Research funding provided by Automotive Partnership Canada (APC), Natural Sciences and Engineering Research Council of Canada (NSERC) and Ballard Power Systems is gratefully acknowledged. We also thank Ballard Power Systems for experimental support. We thank Chan Lim, FCReL and Trevor Lancon, FEI VSG Inc. for technical support. This work made use of the 4D LABS shared facilities supported by the Canada Foundation for Innovation, British Columbia Knowledge Development Fund, Western Economic Diversification Canada, and Simon Fraser University. REFERENCES: M.P. Rodgers, L.J. Bonville, H.R. Kunz, D.K. Slattery, J. M. Fenton, Chem. Rev. 112 6075 (2012). L. Ghassemzadeh and S. Holdcroft, J. Am. Chem. Soc. 135 8181 (2013). C. Lim, L. Ghassemzadeh, F. Van Hove, M. Lauritzen, J. Kolodziej, G.G. Wang, S. Holdcroft, E. Kjeang, J. Power Sources 257 102 (2014). S. Venkatesan, C. Lim, S. Holdcroft, E. Kjeang, J. Electrochem. Soc. 163 F637 (2016). X. Zhang, F. Jia, S. Luo, G. Liu, Q. Hu, Comput. Meth. Prog. Bio. 113 894 (2014). 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) | 0,001 | 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 ».