Unfolding adsorption on metal nanoparticles: Connecting stability with catalysis

Metal nanoparticles have received substantial attention due to their applications in diverse fields from medicine, catalysis, energy and the environment. However, the fundamental properties of nanoparticle adsorption on a surface remain to be understood. James Dean and an interdisciplinary research team in the department of Chemical Engineering, in the U.S. introduced a universal adsorption model to account for the structural characteristics, metal composition and different adsorbates of nanoparticles via machine learning (ML). The model fit a large number of data to accurately predict adsorption trends on monometallic and alloy-based nanoparticles. The template was simple and provided rapidly calculated data for metals and adsorbates. The research team connected the adsorption with stability behavior to advance the design of optimal nanoparticles for applications of interest. The research is now published on Science Advances.