The morphology of mesostructured (photo)electrodes significantly affects the performance of (photo)electrochemical devices. Complex anisotropic morphologies are important for overcoming performance limiting bulk transport properties of semiconductor materials or enhancing the selectivity of CO₂ reduction electrodes, but are often also an unintended outcome of the fabrication process. A better understanding of morphology-induced transport limitations of (photo)electrodes is needed to better understand the kinetics, degradation, and transport limitations, and to subsequently guide mesostructured design and performance optimization.

We use direct pore-level simulations for the coupled transport characterization of mesostructured (photo)electrodes. I will introduce computational approaches that use the exact mesostructure as input into the calculations. Two examples will be discussed: stochastic mesostructures and ordered / lattice-like mesostructures.

For the former, we utilized a 3D-microscopy method, FIB-SEM tomography with a high resolution of 8x8x8 nm³, to obtain a grey value array representing the photoelectrode morphology. The digital structure was segmented based on trainable machine-learning algorithms to subsequently quantify performance-related morphological parameters. The digitalized morphology was used to quantify specific surface, mean feature dimensions, and film homogeneity. Further, the structural characteristics in the meso and nano-scale, including the shape and orientation of these structural details, were quantified. The digitalized photoelectrode morphology was subsequently used in direct pore-level simulations to understand transport around and in the semiconductor. Charge carrier generation rates in the semiconductor phase were calculated by an electromagnetic wave propagation simulation based on spatially resolved material density profiles. The generation rates were mapped onto the semiconductor-electrolyte interface and limitations in the diffusive ion transport in the electrolyte were investigated with a finite volume solver [1].

For the ordered / lattice-like structures, we developed a 3D mass transport model on a repeatable mesostructured unit to calculate local concentration distributions of CO₂(aq), OH^-, HCO₃^-, and CO₃^2- by considering the buffer reactions in the electrolyte and modeling local catalytic surface reaction rates based on Butler-Volmer correlations. Validated with experimental data from the literature [2], the model predicted the suppression of the hydrogen evolution reaction with an inverse dependency on the hydroxide concentration and the promotion of the CO evolution reaction with a proportional dependency on the carbonate concentration [3]. In order to increase the CO selectivity, we developed design guidelines that suggest high electrode roughness per film thickness, which translates to smaller pore size in practice. Further, the shallow pores of the electrode strongly reduce the overall CO selectivity as the mass transport to the bulk is too high. We demonstrate that the introduction of an additional diffusion layer on top of the silver electrode can promote the CO selectivity from as low as 39% to more than 90%.

Direct pore-level numerical simulations (with nanometer-scale resolution) allow for direct linking of the multi-physical transport to the morphological parameters of the mesoporous electrodes. These investigations lay the ground for the optimization of performance and selectivity of mesostructured (photo)electrodes by tailored meso-pore engineering.