Selective CO2 Hydrogenation to Formic Acid on Cu55 and Cu13@Ni42 Nanoclusters: A DFT and Artificial Bee Colony Optimization Study

Abstract

The selective hydrogenation of CO2 to formic acid is a promising route for carbon utilization and green hydrogen storage, yet the lack of highly active and selective nanocatalysts limits its practical deployment. In this work, we combine global structural optimization using the artificial bee colony (ABC) algorithm with density functional theory (DFT) simulations to systematically investigate the catalytic behavior of monometallic Cu55 and bimetallic core–shell Cu13@Ni42 nanoparticles. Global minima identified via the ABC-DFT framework reveal that both clusters adopt icosahedral-derived geometries but exhibit markedly different electronic structures and binding characteristics. Comprehensive adsorption analyses of key intermediates (CO2*, H*, HCOO*, COOH*, and HCOOH*) show that Ni incorporation substantially strengthens CO2 activation, H2 dissociation, and intermediate stabilization. The Cu13@Ni42 cluster exhibits significantly more exothermic adsorption energies—most notably for CO2 (-0.924 eV) and CO (-3.745 eV), along with a thermodynamically more favorable formate pathway compared to Cu55. Thermochemical profiling confirms that the rate-determining hydrogenation step (HCOO* → HCOOH*) is energetically more accessible on Cu13@Ni42 (-0.709 eV) than on Cu55 (-0.470 eV), indicating higher catalytic efficiency. Density of states (DOS) analysis reveals strong 3d–3d orbital hybridization between Cu and Ni, which shifts the d-band center and enhances reactivity. Overall, the results establish the Cu13@Ni42 core-shell nanocluster as a superior candidate for selective CO2-to-formic acid conversion, offering improved thermodynamics, stronger CO2 activation, and more favorable electronic properties compared with monometallic Cu55.