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Enregistrement W2225342503 · doi:10.1149/ma2015-02/37/1436

Controlled Ionomer Deposition into the Cathode Catalyst Layer By Inkjet Printer for PEM Fuel Cells

2015· article· en· W2225342503 sur OpenAlexaff
Amin Aziznia, Madhu Sudan Saha, Mickey Tam, Scott McDermid, Darija Susac, Jürgen Stumper

Notice bibliographique

RevueECS Meeting Abstracts · 2015
Typearticle
Langueen
DomaineEngineering
ThématiqueFuel Cells and Related Materials
Établissements canadiensAutomotive Fuel Cell Cooperation (Canada)
Organismes subventionnairesnon disponible
Mots-clésIonomerProton exchange membrane fuel cellMaterials scienceCatalysisChemical engineeringCathodeLayer (electronics)MembraneMembrane electrode assemblyConductivityComposite materialIonic conductivityChemistryElectrodeElectrolyteOrganic chemistryPolymerEngineering

Résumé

récupéré en direct d'OpenAlex

Proton exchange membrane (PEM) fuel cells are being intensively investigated as alternative energy conversion systems for both residential and automotive applications. In order for this technology to become fully commercial, the reduction of cost and improvements in performance and durability of PEM fuel cells membrane electrode assemblies (MEAs) are still required. The ionomer plays several important roles in the catalyst layer (CL), as the binder, proton conductor as well as oxygen transport media. It has been shown that amount of ionomer and its morphology and interaction with catalyst particles play an important role in Pt utilization and cell performance. However, controlling the ionomer structure and morphology inside the CL with conventional method of MEA preparation has been a challenge. There are several reports describing the effect of ionomer loading in catalyst layers on the fuel cell performance, ionic conductivity, and microstructure of MEA but less attention has been paid to the role of the distribution of ionomer within the catalyst layer [1–4]. It is conjectured that incorporating a graded distribution of ionomer such that its content is higher at the CL/membrane interface and lower at the CL/carbon paper interface might be beneficial to proton migration and mass transport, respectively. That is, the region of the catalyst layer possessing the highest ionic current density would be designed to possess the higher proton conductivity, and the region associated with the highest flux of gaseous oxygen would be bestowed with the higher porosity. Some studies that examined novel methodologies for MEA preparation have specifically tried to achieve a controlled ionomer distribution inside the CL [1–4], with the aim of improving three-phase boundary and catalyst utilization. For instance, Xie et al. [1] prepared a gas diffusion electrode (GDE) that contains a graded distribution of ionomer. The performance was improved when the Nafion® content in the GDE was higher toward the CL/membrane interface and lower toward the CL/carbon paper interface. It was hypothesized that this maximizes proton transport in the GDE in the region of greatest ion flux and maximizes porosity in the region of greatest gaseous flux, respectively. Shin et al. [2] impregnated lower ionomer content into a catalyst layer and coated additional higher ionomer content on its surface in order to increase proton conductivity at the catalyst layer/membrane interface. Cheng et al. [3] also suggested that impregnating the catalyst layer with additional Nafion® ionomer improves proton conduction. A theoretical study of PEMFC cathodes [4] suggested that the catalyst layer possessing a graded distribution of ionomer in which the Nafion® content was larger toward the catalyst layer/membrane interface should exhibit a slightly higher performance in fuel cells. Recently, great interest has arisen with respect to inkjet printing technology for manufacturing CL’s [5–7]. Our recent publications on inkjet printing techniques show great potential for increasing Pt utilization by reducing amount of ionomer [6]. The high-precision of inkjet printing allows for controlled catalyst deposition, especially for low Pt loadings, as well as ionomer patterning and gradient structures inside the CL. Therefore, in this work, inkjet printing technology was used to study ionomer distribution inside the CL. An experimental strategy is devised and implemented to examine the influence of a gradient of ionomer printed by inkjet nozzles into the CL and to verify the theoretical prediction of Wang et al. [4]. The effect of a graded distribution of ionomer content on ionic conductivity, active catalyst area, and porosity as well as performance results under different simulated automotive conditions are examined. References [1] Z. Xie, T. Navessin, K. Shi, R. Chow, Q. Wang, D. Song, B. Andreaus, M. Eikerling, Z. Liu, S. Holdcroft, J. Electrochem. Soc., 152 (2005) A1171. [2] S.-J. Shin, J.-K. Lee, H.-Y. Ha, S.-A. Hong, H.-S. Chun, I.-H. Oh, J. Power Sources 106 (2002) 146–152. [3] X. Cheng, B. Yi, M. Han, J. Zhang, Y. Qiao, J. Yu, J. Power Sources 79 (1999) 75–81. [4] Q. Wang, M. Eikerling, D. Song, Z. Liu, T. Navessin, Z. Xie, S. Holdcroft, J. Electrochem. Soc. 151 (2004) A950. [5] S. Shukla, K. Domican, K. Karan, S. Bhattacharjee, M. Secanell, Electrochim. Acta 156 (2015) 289–300. [6] M.S. Saha, M. Tam, V. Berejnov, D. Susac, S. McDermid, A. P. Hitchcock, J. Stumper, ECS Trans. 58 (2013) 797–806. [7] M.S. Saha, D. Paul, D. Malevich, B. Peppley, K. Karan, ECS Trans. 25 (2009) 2049–2059. Acknowledgements The authors would like to thank Dorina Manolescu and Beniamin Zahiri for prototyping support.

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)0,000
score de la tête « metaresearch » (Gemma)0,000
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: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,001
Score d'incertitude au seuil0,003

Scores du classifieur distillé par catégorie (deux têtes)

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

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 Opus0,014
Tête enseignante GPT0,223
Écart entre enseignants0,210 · 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
Publié2015
Routes d'admission1
Résumé présentoui

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