(Digital Presentation) Micro-Scale Simulation of PEFC Catalyst Layer with Dynamic Structure Change

In order to develop high performance Polymer Electrolyte Fuel Cells (PEFCs), the innovative electrode materials are needed and utilized in optimal catalyst structure from the viewpoint of high output density, low amount of Pt and durability. In present PEFC, the resistance of the oxygen reduction reaction (ORR) at the cathode are dominant factors, and a key requirement is the smooth, rapid, and uniform supply of protons, electrons, and oxygen to the Pt surface. Moreover, an improved mass transport flux in the catalyst layer offers the possibility of increasing current densities, which in turn allows the reduction of cell areas, enabling further cost reductions. In our past studies, we focused on the actual heterogeneous porous structure of catalyst layer. From these results, it was found that mass transport performance of oxygen and proton in heterogeneous porous catalyst layer, which consists of carbon support, ionomer and void space, is much lower than that of theoretical prediction in homogeneous porous media [1-5]. In addition, it depends on the structure of carbon black, the coating structure of ionomer and the local distribution of these materials. Accordingly, optimization of porous structure of catalyst layer is needed, and the mechanism of mass transport resistance and reaction distribution have to be understood in detail. The numerical simulation is effective to understand it because the difficulty of experimental measurement directly. In this study, to understand the relationship between heterogeneous porous structure and cell performance, a three-dimensional heterogeneous CL structure consisting of a simulated CB aggregate and an ionomer coating model was developed using numerical analysis. Moreover, this simulation was applied to actual experimental cell test condition. Firstly, we simulated CB aggregate structure. Previously, our research group examined a numerical model for actual CB aggregate structures (Vulcan and Ketjen black) [6, 7]. These structures were reconstructed with a probability density distribution that was a function of the distance between particles. And various structural properties, such as aggregate size, anisotropy, surface volume and surface weight were evaluated with experimental data. With this model, actual CB aggregate structure was simulated which was same as that in experimental cell test. The structure of catalyst layer was simulated by random packing these CB structure and ionomer coating model [8]. Pt particles were placed on the CB surface and in CB pore. In the calculation of cathode electrochemical reaction, the kinetic activity (exchange current density) of exterior and interior Pt surface were set different value by considering effect of ionomer coating [9]. The electrode reaction was calculated using the Butler–Volmer equation for the formation of Pt oxide on the Pt surface. This oxide layer inhibits the absorption of oxygen, and the coverage depends on the local potential. The resistance of oxygen diffusion and dissolution in ionomer was included. The mass balance equations for oxygen, vapor, protons, and electrons in catalyst layer was coupled with local reaction [10]. The current density distribution was found to depend on the carbon black structure and ionomer adhesion shape. From the viewpoint of increasing both Pt utilization and mass transport performance, an adequate heterogeneous pore structure is necessary in the catalyst layer. Furthermore, this simulation was applied the evaluation of structure change of catalyst layer after long-time durability test. The information of the diameter of CB primary particle and Pt particle of BOL and EOL that obtained by 3D TEM was used. The effect of this dynamic structure change on cell output performance will be reported in this presentation. Acknowledgements This research was supported by the New Energy and Industrial Technology Development Organization (NEDO), Japan, grant number P20003-20001327-0. References [1] G. Inoue et al., J. Power Sources , 327 (2016) 610. [2] G. Inoue et al., J. Power Sources , 327 (2016) 1. [3] H. Ishikawa et al., J. Power Sources , 374 (2018) 196. [4] M. So et al., Int. J. Hydrogen Energy 44 (60) (2019) 32170. [5] M. So et al., J. Electrochm. Soc. , 167 (2020) 013544. [6] G. Inoue et al., J. Power Sources , 439 (2019) 227060. [7] K. Park et al., J. Power Sources Advances , (2022) in press. [8] G. Inoue et al., Int. J. Hydrogen Energy , 41 (46) (2016) 21352. [9] R. Kotoi et al., ECS Transaction , 14 (75) (2016) 386. [10] G. Inoue et al., Int. J. Hydrogen Energy , 47 (25) (2022) 12665. Figure 1