Modeling gas-diffusion electrodes for CO2 reduction
文献信息
Adam Z. Weber
CO2 reduction conducted in electrochemical cells with planar electrodes immersed in an aqueous electrolyte is severely limited by mass transport across the hydrodynamic boundary layer. This limitation can be minimized by use of vapor-fed, gas-diffusion electrodes (GDEs), enabling current densities that are almost two orders of magnitude greater at the same applied cathode overpotential than what is achievable with planar electrodes in an aqueous electrolyte. The addition of porous cathode layers, however, introduces a number of parameters that need to be tuned in order to optimize the performance of the GDE cell. In this work, we develop a multiphysics model for gas diffusion electrodes for CO2 reduction and used it to investigate the interplay between species transport and electrochemical reaction kinetics. The model demonstrates how the local environment near the catalyst layer, which is a function of the operating conditions, affects cell performance. We also examine the effects of catalyst layer hydrophobicity, loading, porosity, and electrolyte flowrate to help guide experimental design of vapor-fed CO2 reduction cells.
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Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.













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