Issue 2, 2023

Complex ligand adsorption on 3D atomic surfaces of synthesized nanoparticles investigated by machine-learning accelerated ab initio calculation

Abstract

Nanoparticle surfaces are passivated by surface-bound ligands, and their adsorption on synthesized nanoparticles is complicated because of the intricate and low-symmetry surface structures. Thus, it is challenging to precisely investigate ligand adsorption on synthesized nanoparticles. Here, we applied machine-learning-accelerated ab initio calculation to experimentally resolved 3D atomic structures of Pt nanoparticles to analyze the complex adsorption behavior of polyvinylpyrrolidone (PVP) ligands on synthesized nanoparticles. Different angular configurations of large-sized ligands are thoroughly investigated to understand the adsorption behavior on various surface-exposed atoms with intrinsic low-symmetry. It is revealed that the ligand binding energy (Eads) of the large-sized ligand shows a weak positive relationship with the generalized coordination number Image ID:d2nr05294f-t1.gif. This is because the strong positive relationship of short-range direct bonding (Ebind) is attenuated by the negative relationship of long-range van der Waals interaction (EvdW). In addition, it is demonstrated that the PVP ligands prefer to adsorb where the long-range vdW interaction with the surrounding surface structure is maximized. Our results highlight the significant contribution of vdW interactions and the importance of the local geometry of surface atoms to the adsorption behavior of large-sized ligands on synthesized nanoparticle surfaces.

Graphical abstract: Complex ligand adsorption on 3D atomic surfaces of synthesized nanoparticles investigated by machine-learning accelerated ab initio calculation

Supplementary files

Article information

Article type
Paper
Submitted
26 Sep 2022
Accepted
05 Dec 2022
First published
06 Dec 2022

Nanoscale, 2023,15, 532-539

Complex ligand adsorption on 3D atomic surfaces of synthesized nanoparticles investigated by machine-learning accelerated ab initio calculation

D. Kang, S. Kim, J. Heo, D. Kim, H. Bae, S. Kang, S. Shim, H. Lee and J. Park, Nanoscale, 2023, 15, 532 DOI: 10.1039/D2NR05294F

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