Controlled Ionomer Deposition into the Cathode Catalyst Layer By Inkjet Printer for PEM Fuel Cells
Notice bibliographique
Résumé
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.
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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 | 0,001 |
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.
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 ».