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Enregistrement W3118227745 · doi:10.1149/ma2020-02332131mtgabs

(Invited) Examining Catalyst Layer Design Strategies for Improved Fuel Cell Performance and Durability

2020· article· en· W3118227745 sur OpenAlexaboutno aff
Monica Dutta, A. P. Young, Vesna Colbow, Julie Bellerive, Shanna Knights

Notice bibliographique

RevueECS Meeting Abstracts · 2020
Typearticle
Langueen
DomaineEngineering
ThématiqueFuel Cells and Related Materials
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésProton exchange membrane fuel cellDurabilityMaterials scienceCommercializationCatalysisProcess engineeringNanotechnologyChemical engineeringComposite materialEngineeringFuel cellsChemistry

Résumé

récupéré en direct d'OpenAlex

Introduction The commercialization of fuel cell products for a variety of applications including forklifts, buses, cars and other heavy duty transport is well underway. Recent studies suggest that over the next 10 years, the total cost of ownership of fuel cell vehicles will become more favourable than both battery electric vehicles and internal combustion engine commercial vehicles1,2. This is driven, in part, by a forecasted almost 50% decrease in the cost of fuel cell systems. While this achievement is mainly due to product volume increases and related economies of scale, further improvements of fuel cell technology can be achieved through next generation catalyst layer designs. Design approaches related to the individual component characteristics, optimization of the component interactions to obtain enhanced morphology and microstructure, as well as processing techniques are currently being developed. Strategies to lower catalyst loadings and move to higher current operation and power densities include incorporating high activity catalysts and improving proton conductivity and oxygen transport by integrating advanced ionomers. Advanced cathode catalysts3 such as Pt alloy (de-alloy), core-shell and shape controlled nanocrystals show more promise in achieving higher mass activity than conventional carbon supported platinum catalyst, although durability or technology maturity challenges remain. Alternatively, while performance gains through ohmic improvements can be achieved by utilizing high proton conductivity ionomers4, additional opportunities such as incorporating novel ionomer materials5, with increased oxygen permeability to allow for reduction of the oxygen thin film transport resistance are of interest. In addition to maximizing the catalyst and ionomer attributes, catalyst-ionomer interaction understanding at the molecular scale and its influence on catalyst layer morphology and transport properties is crucial. Earlier studies of ionomer thin films6, molecular dynamics modeling7 as well as catalyst layer characterization by XPS8, indicate that the ionomer morphology within confined thin films, as well as the resulting film properties, are not only dependent on the surface properties of the substrate / catalyst material and water content, but can be altered in situ as those parameters change. Therefore, the catalyst-ionomer interaction and morphology must be better understood and controlled. Although it can be optimized through design choices, processing parameters during catalyst layer fabrication can also be effective. For example, Figure 1a) shows changes to the cathode catalyst layer pore size distribution with four different processes, incorporating changes to the ink preparation, coating and drying parameters, while keeping the same catalyst layer composition and loading. The optimized process resulted in improved catalyst and ionomer dispersion, lower ionic resistivity and improved performance. Moreover, Figure 1b) shows a clear relationship of improved performance with larger catalyst layer pore size. In addition to microstructural characteristics, macroscopic non-uniformities of the catalyst layer, such as cracks and roughness variations, can also affect the performance and durability of the MEA and are an area of focus for quality control tools9. Recent four dimensional X-ray computed tomography studies have shown how pre-existing cracks within the catalyst layer grow with accelerated stress testing and can be detrimental to both the voltage and membrane durability of the MEA10-11. All in all, a multiscale research approach to gain understanding focussed on nanoscale, microscale and macroscale features of the catalyst layer for next generation designs is recommended and will be the focus of the presentation. Acknowledgement The authors would like to acknowledge Natural Sciences and Engineering Research Council of Canada (NSERC), Mitacs, Automotive Partnership Canada (APC), and the US Department of Energy (DOE) for funding various aspects of this work. References Deloitte and Ballard. (2020). Fueling the Future of Mobility: Hydrogen and fuel cell solutions for transportation. Volume 1 [White paper]. Deloitte China. https://www2.deloitte.com/content/dam/Deloitte/cn/Documents/finance/deloitte-cn-fueling-the-future-of-mobility-en-200101.pdf Hydrogen Council. (2020). Path to hydrogen competitiveness: A cost perspective. https://hydrogencouncil.com/wp-content/uploads/2020/01/Path-to-Hydrogen-Competitiveness_Full-Study-1.pdf Banham, D. and Ye, S., ACS Energy Letters 2(3): 629-638 (2017) Young, A.P. et al. 230th ECS Meeting in Honolulu, Hawaii (October 2-7, 2016) Rolfi et al. J. Power Sources, 396 (2018) 95 - 101 Kusoglu, A. and Weber, A. Z. Chem. Rev. 2017, 117, 3, 987–1104 Borges, D. D. et al. J. Phys. Chem. C 2015, 119, 2, 1201–1216 Artyushkova, K. et al. J. Power Sources 284 (2015) 631-641 Philips, A. et al. Int. J. Hydrog. Energy 2018, 43, 12, 6390-6399 White, R. T. et al. J. Power Sources 350 (2017) 94-102 Singh, Y. et al. J. Power Sources 412(2019) 224-237 Figure 1

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 machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,047
Score d'incertitude au seuil0,156

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0020,002
Science ouverte0,0010,001
Intégrité de la recherche0,0020,002
Charge utile insuffisante (le modèle a refusé de juger)0,0470,016

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.

Tête enseignante Opus0,031
Tête enseignante GPT0,209
Écart entre enseignants0,179 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2020
Routes d'admission1
Résumé présentoui

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