Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Springer Handbook of Electrochemical Energy |
| Seiten | 259-285 |
| Seitenumfang | 27 |
| Publikationsstatus | Veröffentlicht - 2017 |
Publikationsreihe
| Name | Springer Handbooks |
|---|---|
| ISSN (Print) | 2522-8692 |
| ISSN (elektronisch) | 2522-8706 |
Abstract
The overall performance of a fuel cell or an electrochemical reactor depends greatly on properties of catalyst layers, where electrochemical reactions take place. Optimization of these structures in the past was mainly guided by experimental methods. For substantial progress in this field, combination of experiments with modeling is highly desirable. In this chapter focus is on macroscale models, since at the moment they provide more straightforward relationship to experimentally measurable quantities. After introducing the physical structure of a catalyst layer, we discuss typical macroscale modeling approaches such as interface, porous, and agglomerate models. We show how governing equations for the state fields, like potential or concentration can be derived and which typical simplifications can be made. For derivations, a porous electrode model has been chosen as a reference case. We prove that the interface model is a simplification of a porous model, where all gradients can be neglected. Furthermore, we demonstrate that the agglomerate model is an extension of the porous model, where in addition to macroscale, additional length scale is considered. Finally some selected examples regarding different macroscale models have been shown. Interface model has low capability to describe the structure of the catalyst layer, but it can be utilized to resolve complex reaction mechanisms, providing reaction kinetic parameters for distributed models. It was shown that the agglomerate models, having more structural parameters of the catalyst layer, are more suitable for catalyst layer optimization than the porous models.
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Springer Handbook of Electrochemical Energy. 2017. S. 259-285 (Springer Handbooks).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Beitrag in Buch/Sammelwerk › Forschung › Peer-Review
}
TY - CHAP
T1 - Catalyst layer modeling
AU - Vidaković-Koch, Tanja
AU - Hanke-Rauschenbach, Richard
AU - Gonzalez Martínez, Isaí
AU - Sundmacher, Kai
N1 - Publisher Copyright: © 2017, Springer-Verlag Berlin Heidelberg. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2017
Y1 - 2017
N2 - The overall performance of a fuel cell or an electrochemical reactor depends greatly on properties of catalyst layers, where electrochemical reactions take place. Optimization of these structures in the past was mainly guided by experimental methods. For substantial progress in this field, combination of experiments with modeling is highly desirable. In this chapter focus is on macroscale models, since at the moment they provide more straightforward relationship to experimentally measurable quantities. After introducing the physical structure of a catalyst layer, we discuss typical macroscale modeling approaches such as interface, porous, and agglomerate models. We show how governing equations for the state fields, like potential or concentration can be derived and which typical simplifications can be made. For derivations, a porous electrode model has been chosen as a reference case. We prove that the interface model is a simplification of a porous model, where all gradients can be neglected. Furthermore, we demonstrate that the agglomerate model is an extension of the porous model, where in addition to macroscale, additional length scale is considered. Finally some selected examples regarding different macroscale models have been shown. Interface model has low capability to describe the structure of the catalyst layer, but it can be utilized to resolve complex reaction mechanisms, providing reaction kinetic parameters for distributed models. It was shown that the agglomerate models, having more structural parameters of the catalyst layer, are more suitable for catalyst layer optimization than the porous models.
AB - The overall performance of a fuel cell or an electrochemical reactor depends greatly on properties of catalyst layers, where electrochemical reactions take place. Optimization of these structures in the past was mainly guided by experimental methods. For substantial progress in this field, combination of experiments with modeling is highly desirable. In this chapter focus is on macroscale models, since at the moment they provide more straightforward relationship to experimentally measurable quantities. After introducing the physical structure of a catalyst layer, we discuss typical macroscale modeling approaches such as interface, porous, and agglomerate models. We show how governing equations for the state fields, like potential or concentration can be derived and which typical simplifications can be made. For derivations, a porous electrode model has been chosen as a reference case. We prove that the interface model is a simplification of a porous model, where all gradients can be neglected. Furthermore, we demonstrate that the agglomerate model is an extension of the porous model, where in addition to macroscale, additional length scale is considered. Finally some selected examples regarding different macroscale models have been shown. Interface model has low capability to describe the structure of the catalyst layer, but it can be utilized to resolve complex reaction mechanisms, providing reaction kinetic parameters for distributed models. It was shown that the agglomerate models, having more structural parameters of the catalyst layer, are more suitable for catalyst layer optimization than the porous models.
UR - http://www.scopus.com/inward/record.url?scp=85041778588&partnerID=8YFLogxK
U2 - 10.1007/978-3-662-46657-5_9
DO - 10.1007/978-3-662-46657-5_9
M3 - Contribution to book/anthology
AN - SCOPUS:85041778588
T3 - Springer Handbooks
SP - 259
EP - 285
BT - Springer Handbook of Electrochemical Energy
ER -