MétaCan
Menu
Retour à la cohorte
Enregistrement W3117828838 · doi:10.1149/ma2020-02372370mtgabs

Impact of Solvent on Catalyst Layer for Anion Exchange Membrane Fuel Cell

2020· article· en· W3117828838 sur OpenAlexaff
Jonghyun Hyun, Hee-Tak Kim

Notice bibliographique

RevueECS Meeting Abstracts · 2020
Typearticle
Langueen
DomaineEngineering
ThématiqueFuel Cells and Related Materials
Établissements canadiensKootenay Association for Science & Technology
Organismes subventionnairesnon disponible
Mots-clésAnodePlatinumProton exchange membrane fuel cellCathodeCatalysisMembrane electrode assemblyChemical engineeringChemistryInorganic chemistryConductivityIon exchangeMembraneWater transportHydrogenIonomerMaterials scienceElectrodeIonWater flowEnvironmental engineeringOrganic chemistry

Résumé

récupéré en direct d'OpenAlex

Anion exchange membrane fuel cell (AEMFC) is attractive energy conversion device that is an alternative to proton exchange membrane fuel cell (PEMFC) which required expensive materials, including platinum-based catalysts and perfluorosulfonic acid (PFSA) ionomers. Operating under the alkaline environment, enables the use of various types of low cost platinum-free-catalysts and the inexpensive metal stack hardware, allowing the system to be configured at a much lower cost. Over the past several years, the development of anion exchange membrane (AEM) and ionomer (AEI) with high OH - conductivity and kinetically fast water diffusivity has resulted in significantly improved power performance of AEMFC. However, the problem of water imbalance during AEMFC operation becomes a hindrance to achieving more higher performance. In the anode, one water per electron is generated by hydrogen oxidation reaction (HOR) and water is consumed by the oxygen reduction reaction (ORR) as a reactant in the cathode. Also, since the OH - migration from the cathode to the anode causes electro-osmotic drag and water molecules are transport to same direction, the difference of water content in both electrodes becomes larger. This water imbalance causes flooding in the anode and making it difficult to diffuse H 2 gas to the Pt surface, moreover, water dry-out in the cathode inhibit the OH - conduction of AEIs. These series of processes, finally, reduce the Pt utilization due to the depletion of reactants. Considering the previous studies, despite using AEM and AEI with sufficient OH - conductivity, high-loading of platinum (0.4 mgPt cm -2 or more) was used to achieve power performance. Compared to practical PEMFC (0.2 mgPt cm -2 or less), the amount of platinum is too large, and this low Pt utilization problem is fatal drawback to AEMFC commercialization, which is aimed at lower prices. To improve the Pt utilization of AEMFC, the redox-active site, called the triple-phase-boundary, where the contact region of catalyst, H 2 or O 2 reactant gas, and OH - conducting ionomer, should be maximized in catalyst layer. In manufacturing a catalyst layer having a plenty of triple-phase-boundary, inducing a homogeneous ionomer distribution is one of the most important components. As a similar perspective, the research on the ionomer distribution has recently been conducted intensively at PEMFC field. The most representative way to control the ionomer distribution is tuning the interaction between the ionomer, solvent, and catalyst. In our previous study, the size of the ionomer aggregates was controlled by changing interaction between the solvent and ionomer using different solvents. As a result, the more homogeneous ionomer distribution in the cathode catalyst layer lead to the better performance due to the facile proton migration, however, nanoscale ionomer aggregates can induce the pore clogging with a severe mass transport resistance. However, AEMFC has not yet reported any papers related to ionomer distribution, even only a few papers studied about pore structure of the catalyst layer. Thus, in-depth study of the catalyst layer needs to be required and constructing optimized catalyst layer that containing homogeneous ionomer distribution with high porosity is absolutely necessary for efficient use of the catalyst. Herein, we presented a rational design of the catalyst layer by controlling the ionomer distribution and pore structure via solvent selection of the catalyst slurry. The analytical methods introduced in this study can be universally used for any AEIs and catalysts, and a strategy for preparing a catalyst layer with high Pt utilization considering the size of AEI and catalyst will greatly contribute to the cost reduction of AEMFC for commercialization. As the solvent of this study, dimethyl sulfoxide (DMSO), methanol (MeOH), and isopropanol (IPA) were selected, which have highly different dielectric constant and commonly used for making catalyst layer. Additionally, water was selected as a cosolvent to improve the dispersity of the catalyst slurry. By changing a solvent into higher dielectric constant, homogeneous ionomer distribution was constructed in catalyst layer, achieving high electrochemical surface area (ECSA) (72.1 m 2 gPt -1 ) in the cathode. The selection of solvent for inducing a low absolute zeta-potential on the catalyst surface enables the formation of highly porous catalyst layer, which can result in improved a power performance in anode. For the first time, we observed nanomorphology of AEI dispersed on the carbon surface by scanning transmission electron microscopy (STEM). The structure and performance of the catalyst layer different by solvents were analyzed using electron microscope and electrochemical methods. Finally, we discuss the optimized catalyst layer design for AEMFC to provide a guideline for future direction.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut 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: Empirique
Score de désaccord entre enseignants0,499
Score d'incertitude au seuil0,568

Scores Codex et Gemma par catégorie

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 savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,022
Tête enseignante GPT0,239
Écart entre enseignants0,217 · 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 tête enseignante, 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

Citations1
Publié2020
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueECS Meeting AbstractsMême sujetFuel Cells and Related MaterialsTravaux en français237 207