Electrochemical CO2 reduction (CO2RR) is a leading sustainable approach for transforming atmospheric CO2 into valuable chemicals. This electrocatalytic process relies on a delicate balance of electron and proton transfer steps, resulting in the formation of diverse 1-carbon and some 2-carbon products, contingent on the surface properties. Extensive exploration of various transition metals for CO2RR has revealed that the binding strength of reaction intermediates on these metals significantly influences product selectivity. Notably, metals such as Au, Ag, Pd, Zn, Cd, Bi, Sn, and In, recognized for their activity in CO2RR, exhibit lower binding strengths for CO2 intermediates, leading to the predominant production of either HCOOH or CO as sole products. In contrast, Cu, with its moderate ability to bind CO2RR intermediates, yields multiple products during CO2RR, albeit with reduced selectivity [1]. Moreover, the overall efficiency of CO2 conversion is hampered by the simultaneous occurrence of the competing hydrogen evolution reaction (HER) at the potentials required for producing desired products. Thus, it is imperative to develop new, effective catalysts. Catalysts capable of suppressing HER while enhancing selectivity are essential for advancing the overall efficiency of CO2 conversion [2].Single-atom catalysts (SACs) are a relatively new class of materials that hold great promise due to their high specific activity and maximum atom utilization [3]. These catalysts often provide singular types of active sites, enhancing selectivity in reactions. However, the vast array of possible SACs, deployable on various supports, makes it impractical, if not possible, to use the traditional trial-and-error studies to yield high performing catalysts within reasonable timeframes. To navigate this complexity, theoretical investigations, especially DFT computations and machine learning techniques, have emerged as powerful tools. They can facilitate the identification of suitable catalyst structures and enable the precise engineering of atomic arrangement, therefore achieving targeted product formation from CO2 with enhanced conversion efficiency. Additionally, conventional synthesis methods suffer from various drawbacks, involving multiple steps, harsh conditions, and a lack of control over the metal site distribution in the carbon matrix, limiting the desired coordination structure [4, 5].Therefore, the work presented in this talk represents our combined experimental-theoretical effort. Specifically, Cu single atoms were strategically deposited onto defective carbon supports and systematically investigated for their performance as CO2RR electrocatalysts. These catalysts were inspired by DFT results showing how the product profile could be manipulated on catalyst surfaces, along with a prediction of the most likely products that would arise from the reaction.These catalysts were synthesized using a novel method denoted as switched solvent synthesis. This approach involves initially impregnating metals onto a carbon support, followed by switching the solvent from aqueous to an organic solvent with a higher dipole moment, and subsequently reducing the resulting mixture under an H2 environment. This innovative method offers notable advantages, enabling the efficient production of SACs in less time, at lower temperatures (~100-500 °C), and with high yields. In this study, Cu SACs were synthesized, and the influence of reaction parameters on the distribution of Cu metal atoms was systematically examined. The confirmation of SAC formation was achieved through various characterization techniques, including aberration-corrected STEM, XRD, and XPS.To assess the electrochemical activity and CO2 reduction abilities of the Cu SACs, electrochemical characterization techniques were employed under both He and CO2-saturated 0.1 M KHCO3 electrolytes. Furthermore, the product distribution and CO2 conversion efficiencies, including activity, stability, selectivity, and faradaic efficiency, of the Cu SACs were rigorously evaluated through electrolysis in a flow cell setup. Quantification of the results was performed using an integrated GC/MS instrument.
Read full abstract
Nov 2, 2024
Yushan Li + 4
Just Published
Open Access