Invited: Advances in Structural Characterization of PEM Fuel Cell Catalyst Layers By Soft X-Ray Scanning Transmission Microscopy
Notice bibliographique
Résumé
Proton exchange membrane fuel cells (PEMFCs) are among the most attractive alternative energy sources for automotive application due to their high power density, high efficiency, and potential to significantly reduce adverse environmental impact. However, high material costs and durability issues remain major challenges for large scale commercialization [1, 2]. The catalyst coated membrane (CCM), as a main component in PEMFCs, that employs expensive Pt based porous electrode layers responsible for the generation of pathways for reactant transport enabling efficient electron and proton conductivity and managing product water removal. Improved understanding of the relationship between material properties, electrode structure and overall fuel cell performance is essential for material and process down-selection in order to establish the most cost effective PEMFC manufacturing process. For that reason, the development of new, non destructive characterization methods to allow evaluation of catalyst layer composition and microstructure with high spatial resolution and individual component mapping is needed. In recent years, a novel synchrotron based technique: soft X-ray scanning transmission microscopy (STXM) has emerged and extended the number of standard available microscopic characterization tools and significantly improving our understanding of how materials are distributed from micro to nano-scales in fuel cell electrodes. STXM is able to image a fuel cell catalyst layer and differentiate each chemically specific material component due to its near edge X-ray specific absorption (NEXAFS) signature [3-11]. The spatial resolution of this method is 30 nm. We have developed several STXM applications to characterize a) materials in the dry CCM in 2D cross sections, b) in 3D, and c) while applying environmental changes in 2D. Using two-energy mapping at F 1s and C 1s edges, a 2D material reconstruction methodology for catalyst layer micro-structural characterization and ionomer mapping was developed [7]. Both conventional and nanostructured thin film (NSTF) based catalyst layers are being investigated [9, 10]. Applying a tomographic approach, a STXM spectro-tomography method was established and proven to provide a 3D reconstruction of the cathode catalyst layer with spatially resolved carbon and ionomer species [8]. Due to its ability to spectroscopically differentiate gas, liquid and solid water using the O 1s absorption edge, STXM is being applied to study hydrated CCMs [7]. An environmental wet cell for in situ STXM studies of CCM components under controllable temperature and relative humidity conditions has been developed and used to map all three phases of water in the electrode under various conditions including those relevant to cold start. In this paper and the coming talk, we will review progress in development of STXM methodology for characterization of catalyst layer structures. Efforts in deriving parameters to aid structure-properties-performance correlations will be discussed. STXM measurements are carried out at the Canadian Light Source and at the Advanced Light source. References: 1. Y. Wang, K.S. Chen, J. Mishler, S.C. Cho, X.C. Adroher, Applied Energy 88 (2011) 981. 2. J. Wu, X.Z. Yuan, J.J. Martin, H. Wang, J. Zhang, J. Shen, S. Wu and W. Merida, Journal of Power Sources , 184 (2008) 104-119. 3. D. Bessarabov and A.P. Hitchcock, Membrane Technology 6 (2009) 6. 4. D. Susac, J. Wang, Z. Martin, A.P. Hitchcock, J. Stumper and D. Bessarabov, ECS Transactions , 33 (2010) 391. 5. V. Berejnov, Z. Martin, M. West, S. Kundu, D. Bessarabov, J. Stumper, D. Susac and A.P. Hitchcock, Phys. Chem. Chem. Phys. 14 (2012) 4835. 6. V. Berejnov, D. Susac, J. Stumper and A.P. Hitchcock, ECS Transactions 41 (2011) 395. 7. D. Susac, V. Berejnov, A.P. Hitchcock and J. Stumper, ECS Transactions 41 (2011) 629. 8. V. Berejnov, D. Susac, J. Stumper and A.P. Hitchcock, ECS Transactions 50 (2012) 361. 9. V. Lee, D. Susac, S. Kundu, V. Berejnov, R.T. Atanasoski, A.P. Hitchcock, J. Stumper, ECS Transactions 58 (2013) 473. 10. V. Lee, V. Berejnov, M. West, S. Kundu, D. Susac, J. Stumper, R.T. Atanasoski, M. Debe and A.P. Hitchcock, J. Power Sources (in review) 11. M.S. Saha, M. Tam, V. Berejnov, D. Susac, S. McDermid, A.P. Hitchcock and J. Stumper, ECS Transactions 58 (2013) 797.
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 | 0,001 | 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 ».