(Invited) Multiscale Modeling of Transport Phenomena in Ion-Conducting Membranes and Aqueous CO₂ Reduction Cells

Transport phenomena is a critical component of electrochemical engineering of energy-conversion devices including fuel cells and solar-fuel generators. In fuel cells, ion-conducting polymers, such as perflurosulfonic-acid (PFSA) membranes, typically nanophase separate into conducting and nonconducting or structural domains that arrange together on the mesoscale and result in macroscopically observable properties. To examine these interactions, a multiscale model has been developed that includes a mean-field local-density theory at the nanoscale, a physically derived resistor-network for the mesoscale that leads up to macroscale. The nanoscale includes cation and water chemical potentials to model transport in the aqueous domains including cation-polymer electrostatic interactions, solvation energy, and a concentrated-solution transport theory to model transport and account for dielectric friction induced by the strong electrostatic fields of charged polymer groups. For the mesoscale, the model accounts for transport along the segments of the mesoscale network of the aqueous domains where mass and charge are conserved at each node (i.e. branching point) of the network. The difference in ionic potential and species chemical potentials between connected network nodes are driving forces for transport. The resistance of each segment of the network is determined from the nanoscale model. The results reveal nonlinear and nonintuitive transport pathways, where the overall properties agree with experimental property measurements. In solar-fuel generators, including CO 2 reduction cells, transport phenomena play a critical, yet not fully realized, role in controlling the overall reaction rate. To understand this interplay, multiscale modeling is used to examine the reaction mechanism of CO 2 reduction to CO over a planar silver catalyst. Traditional Nernst-Planck expressions for ion and gas transport are coupled to a microkinetics model, where the reaction rates and barriers for the various reaction steps are taken from DFT calculations. The modeling approach helps to resolve the reaction mechanism and demonstrates good agreement with experimental data and trends without fitting parameters. Acknowledgements The fuel-cell work was funded under the Fuel Cell Performance and Durability Consortium (FC PAD) funded by the Energy Efficiency and Renewable Energy, Fuel Cell Technologies Office, of the U. S. Department of Energy under contract DE-AC02-05CH11231. The CO2 reduction was funded under the Joint Center for Artificial Photosynthesis (JCAP), a BES Energy Innovation Hub.