Understanding metal–adsorbate interactions is key to controlling and improving the functionality of metal nanoparticles (NPs) in energy and biomedical application areas. However, adsorption is dependent on the morphological characteristics of the NPs, such as their size and shape, and in turn, the NP morphology is dependent on the chemical environment (presence of adsorbates). In this work, we introduce a novel and computationally tractable framework that is able to capture adsorption trends as a function of NP size and shape, including the impact of chemical environment on NP morphology. Our methodology is tested in the area of catalysis and specifically on the CO oxidation behavior on gold (Au), a highly structure-sensitive reaction. Our results reveal a strong correlation between the experimentally observed CO oxidation activity and the average CO adsorption (binding) energy on Au NPs enabling catalytic behavior prediction as a function of NP morphology. We demonstrate that the Au NP size plays a pivotal role on CO adsorption, whereas the Au NP shape appears to be less significant. Most importantly, the developed methodology introduces NP morphology effects on adsorption that are key for the rational design of materials with fine-tuned properties in applications ranging from catalysis to targeted medical imaging to drug delivery.
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