Publication date: 8th January 2019
The fast-growing atmospheric CO2 level, caused by combustion of fossil fuels, is threatening the future life of humankind today. The solar conversion of CO2 into value-added hydrocarbons, through artificial photosynthesis, is a propitious route towards atmospheric CO2 recycling and simultaneously holds a great promise for storing sunlight in form of chemical fuel. Thus far, three different systems have been developed to convert CO2 into hydrocarbons with employing sunlight: photoelectrochemical cell (PEC), photocatalysis, and photovoltaic-electrochemical cell (PV-EC). However, the PEC and photocatalysis systems have shown high solar-to-fuel conversion efficiency, but their widespread implementation is hampered owing to utilization of costly apparatus. PV-EC system, due to manufacturing ease in large scale, is regarded to be a perfect candidate for industrial application. High-efficiency PV-EC systems can be achieved by combining the following two superior pre-existing infrastructures: efficient PVs as the power generator and an EC operated at low overpotential for the CO2 electroreduction into hydrocarbons. The advent of high-efficiency perovskite solar cells has been provided an unrivaled chance for development of low-cost PV-EC systems with high solar-to-fuel conversation efficiency by combining efficient perovskite solar cells and effective electrocatalyst. Although great efforts have been done in the past few years to develop high-efficiency electrocatalyst for this purpose, there are still fundamental scientific challenges remaining to obtain high-performance electrocatalyst to selectively and effectively convert CO2 to hydrocarbons. A wide variety of metal nanoclusters has been examined as catalysts for CO2 electroreduction with a focus on Cu, Au, Ag, Cd, Hg, Sn, along with several others metals. Specifically, Cu nanocluster has gained the most interest whereas none of the developed catalysts have found to be more effective than copper. The Cu-based catalysts have demonstrated high (> 50%) faradaic efficiency at reasonable current densities, furthermore, the high selectivity of Cu nanoclusters for CO2 electroreduction into hydrocarbons has sparked an intense interest in the field. However, the overpotential necessary to achieve CO2 electroreduction reaction on Cu nanoclusters is prohibitively high, at the same time, there are physical limits for the density of Cu nanoclusters can be loaded onto a substrate without affecting other important processes, such as charge and mass transport. An appropriate substrate that interacts strongly with the nanoclusters in order to prevent their aggregation remains a significant experimental challenge. Recently, cuprous oxide-derived Cu nanoparticles (OD-Cu NPs) have shown vastly improved the CO2 electroreduction efficiency at low overpotentials. The proposed electro-catalyst model system potentially could overcome to the high overpotential and substrate concerns for the CO2 electroreduction, however, the catalytic active sites still exhibit poor stability. Here, we report a stabilization of the catalytic active sites by the formation of a mixed metal oxide CuInO2 nanoparticles. Our result shows the incorporation of nanoporous Sn:In2O3 interlayer to Cu2O pre-catalyst system leads to the formation of CuInO2 nanoparticles with a remarkably higher activity for CO2 electroreduction at lower overpotential in comparison to the OD-Cu NPs derived from sole Cu2O. Operando Raman spectroelectrochemistry is employed to in-situ monitor the process of catalyst deformation during the electrocatalytic process. The experimental data are collaborated with DFT calculation to provide insight into the electro-formation of the Cu-based mixed metal oxide catalyst during the CO2 electroreduction, where a formation mechanism via copper ion diffusion across the substrate is suggested. Finally developed Cu-based mixed metal oxide catalyst electrode has been combined with mixed-halide perovskite solar cells to build perovskite PV-EC system for solar driven CO2 electroreduction into hydrocarbons.
Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX) is acknowledged for providing computational resources under projects snic 2017-1-57 and snic 2016-10-23.