Yao Yang1
Cornell University1
In an era of shifting the energy paradigm from fossil fuels to renewable energy, CO<sub>2</sub> reduction reaction (CO<sub>2</sub>RR) emerges as a promising approach to convert greenhouse gas into valuable chemical fuels and close the carbon cycle for a sustainable energy supply. Since Cu remains the sole element for CO<sub>2</sub>RR to multicarbon products (C<sub>2+</sub>), significant efforts have been devoted to developing Cu electrocatalysts with higher selectivity and activity. However, the complex nature of active sites and the intrinsic structures under reaction conditions have remained largely elusive due to the lack of <i>operando</i>/<i>in situ</i> methods. In this work, we present a comprehensive <i>operando</i> correlative study of size- and potential-dependent dynamic evolution of Cu nanoparticle electrocatalysts under CO<sub>2</sub>RR conditions. <i>Operando</i> electrochemical liquid-cell scanning transmission electron microscopy (EC-STEM) and 4D-STEM, driven by machine learning, resolve microscopic dynamic morphological and structural evolution at the nm scale. Correlated <i>operando</i> synchrotron-based high-energy-resolution fluorescence-detected (HERFD) X-ray absorption spectroscopy (XAS) reveals dynamic macroscopic changes in valence states and coordination environment. The methodology described herein can serve as a general strategy to resolve the electrocatalytic interface of nanoparticle catalysts under real-time operating conditions across multiple time and length scales.