Metal nanoparticles exhibit superior carbon dioxide (CO2) reduction performance due to the existence of unique surface active sites. For example, gold (Au) nanoparticles (NPs) show high efficiency in reducing CO2 to carbon monoxide (CO), and copper (Cu) NPs efficiently convert CO to C2 productions. However, due to the difficulties in operando measurements, the atomic details of these actives site have never been revealed. In this work, we employed multi-scale simulation technologies to “computationally synthesized” the Au NPs and Cu NPs on carbon nanotube (CNT) support by closely simulating the chemical vapor deposition (CVD) experiments. The large-scale reactive force field simulations with up to million atoms enable the direct simulation of Au NPs and Cu NPs with a size of 10 to 20 nm, which is consistent with the realistic catalysis using in the experiment condition. The simulated NPs well reproduce the experiment X-ray Powder Diffraction (XRD) patterns and transmission electron microscopy (TEM) images. These NPs all-atom models allow us to scan the active site on realistic catalysis surface. By using quantum mechanics based high-throughput screening, we have located the active surface site on Au NPs for CO2 reduction to CO and the active sites on Cu NPs for CO reduction to C2. We find that: at Au NPs, the surface sites at twin boundary are responsible for promoting CO2 reduction to CO; at Cu NPs, the unsaturated square site is responsible for C-C coupling. Armed with this atomic information of active sites, we applied these mulita-scale high-throughput screening technologies to explore the alloy catalysis searching for better catalysis. We predict Au-Fe alloy NPs as a better CO2 reduction catalysis than pure Au NPs, and experimentally confirm the superior performance by collaborating with experiment group. We believe the work-flow in this work will profoundly accelerate the searching for improved CO2 reduction catalysis.

Nature of the active sites for carbon dioxide reduction on metal nanoparticles: suggestions for optimizing performance

255th National Meeting (2018)

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