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Cu is currently the most effective monometallic catalyst for producing valuable multi-carbon-based products, such as ethylene and ethanol, from the CO2 reduction reaction (CO2RR). One approach to optimize the activity and selectivity of the metal Cu catalyst is to functionalize the Cu electrode with a molecular modifier. We investigate from a data standpoint whether any reported functionalized Cu catalyst improves the intrinsic activity and/or multi-carbon product selectivity compared to the performance of bare Cu foil and the best single crystal Cu facets. Our analysis shows that the reported increases in activity are due to increased surface roughness and disappear once normalizing with respect to electrochemical surface area. The intrinsic activity generally falls below that of bare Cu foil, both for total and product-specific current, which we attribute to non-selective blocking of active sites by the modifier on the surface. Instead, we show that the modifier allows for easier diffusion of CO2 compared to H2O to the surface, leading to greater selectivity for CO2RR and C2+ products. As such, our analysis finds no catalyst for CO2RR that intrinsically outperforms bare Cu.

Cu is currently the most effective monometallic catalyst for producing valuable multi-carbon-based products, such as ethylene and ethanol, from the CO2 reduction reaction (CO2RR). One approach to optimize the activity and selectivity of the metal Cu catalyst is to functionalize the Cu electrode with a molecular modifier. We investigate from a data standpoint whether any reported functionalized Cu catalyst improves the intrinsic activity and/or multi-carbon product selectivity compared to the performance of bare Cu foil and the best single crystal Cu facets. Our analysis shows that the reported increases in activity are due to increased surface roughness and disappear once normalizing with respect to electrochemical surface area. The intrinsic activity generally falls below that of bare Cu foil, both for total and product-specific current, which we attribute to non-selective blocking of active sites by the modifier on the surface. Instead, we show that the modifier allows for easier diffusion of CO2 compared to H2O to the surface, leading to greater selectivity for CO2RR and C2+ products. As such, our analysis finds no catalyst for CO2RR that intrinsically outperforms bare Cu.

The dihydroazulene/vinylheptafulvene (DHA/VHF) photocouple is a promising candidate for molecular solar heat batteries, storing and releasing energy in a closed cycle. Much work has been done on improving the energy storage capacity and the half-life of the high-energy isomer via substituent functionalization, but similarly important is keeping these improved properties in common polar solvents, along with being soluble in these, which is tied to the dipole properties. However, the number of possible derivatives makes an overview of this combinatorial space impossible both for experimental work and traditional computational chemistry. Due to the time-consuming nature of running many thousands of computations, we look to machine learning, which bears the advantage that once a model has been trained, it can be used to rapidly estimate approximate values for the given system. Applying a convolutional neural network, we show that it is possible to reach good agreement with traditional computations on a scale that allows us to rapidly screen tens of thousands of the DHA/VHF photocouple, eliminating bad candidates and allowing computational resources to be directed toward meaningful compounds.

The catalytic reduction of CO2/CO is key to reducing the carbon footprint and producing the chemical building blocks needed for society. In this work, we performed a theoretical investigation of the differences and similarities of the CO2/CO catalytic reduction reactions in gas, aqueous solution, and aprotic solution. We demonstrate that the binding energy serves as a good descriptor for the gaseous and aqueous phases and allows catalysts to be categorized by reduction products. The CO* vs O* and CO* vs H* binding energies for these phases give a convenient mapping of catalysts regarding their main product for the CO2/CO reduction reactions. However, for the aprotic phase, descriptors alone are insufficient for the mapping. We show that a microkinetic model (including the CO* and H* binding energies) allows spanning and interpreting the reaction space for the aprotic phase.

The catalytic reduction of CO2/CO is key to reducing carbon footprint and producing the chemical building blocks needed for society. In this work, we performed a theoretical investigation of the differences and similarities of the CO2/CO catalytic reduction reactions in the gas, aqueous solution, and aprotic solution. We demonstrate that binding energy serves as a good descriptor for gaseous and aqueous phases and allows categorizing catalysts by reduction products. The CO vs. O and CO vs. H binding energies for these phases gives a convenient mapping of catalysts regarding their main product for the CO2/CO reduction reactions. However, for the aprotic phase, descriptors alone are insufficient for the mapping. We show that a microkinetic model (including the CO and H binding energies) allows spanning and interpreting the reaction space for the aprotic phase.