Steering the Selectivity of Single Atom Electrocatalysts—Static vs Pulsed CO₂RR

The re-utilization of CO₂via its electrocatalytic reduction (CO₂RR) into value-added chemicals and fuels is a promising avenue to minimize the impact of existing technologies on the climate change. This requires the development of low cost, efficient, selective and durable electrocatalysts based of their rational understanding. Moreover, it should be considered that even morphologically and chemically well-defined pre-catalysts are susceptible to drastic modifications under operation, especially when the reaction conditions themselves change dynamically.<br/><br/>This talk will address the transformations that Metal-N-C catalysts (M=Cu, Ni, Co, Fe, Sn, Zn) experience during static and pulsed CO₂RR using <i>operando quick</i> X-ray absorption spectroscopy (XAS) and Raman spectroscopy, combined with machine learning based data analysis. In particular, I will illustrate the astonishing behavior displayed by Cu-N-C catalysts during CO₂RR, featuring reversible transformations from single atom sites towards Cu nanoparticles. The latter reflects the intricate interplay between the relative strengths of metal-metal atomic interactions, metal-adsorbate and metal-support interactions. The switchable nature of these Cu species that can be achieved by applying different potential pulses holds the key for the on-demand control of the distribution of the CO₂RR products and thus, a wide-spread adoption of this process.<br/><br/>Moreover, I will discuss the structural evolution and intermediate states of Ni and Co single atoms during static CO₂RR. In particular, I will unveil the nature of the ligands forming and present under CO₂RR at singly dispersed Ni and Co sites in Ni-N-C and Co-N-C catalysts, which are currently drawing great attention for their high performances in the CO formation. This will be achieved by a synergistic combination of conventional XAS, high energy resolution fluorescence detected X-ray absorption near edge structure (HERFD-XANES) spectroscopy, and X-ray emission spectroscopy (XES) coupled with unsupervised and supervised machine learning methodologies and density functional theory.<br/><br/>Overall, my lecture will feature the importance of operando characterization of electrocatalysts in order to unveil structure/composition-reactivity correlations during CO₂RR and ultimately optimize their electrocatalytic performance.