4.8 Article

Phosphine-Stabilized Hidden Ground States in Gold Clusters Investigated via a Aun(PH3)m Database

Journal

ACS NANO
Volume 17, Issue 2, Pages 1012-1021

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/acsnano.2c07223

Keywords

nanoclusters; gold; phosphine; ligands; DFT; high throughput; synthesis

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Nanoclusters are promising materials due to their large surface areas and unique electronic structures. This study investigates the influence of ligands on the stability, bonding, and electronic structure of phosphine-stabilized gold nanoclusters. The addition of phosphine affects the morphology and functionality of the nanoclusters, highlighting the importance of considering the ligand environment.
Nanoclusters are promising materials for catalysis and sensing due to their large surface areas and unique electronic structures which can be tailored through composi-tion, geometry, and chemistry. However, relationships correlat-ing synthesis parameters directly to outcomes are limited. While previous computational studies have mapped the potential energy surface of specific systems of bare nanoclusters by generating and calculating the energies of reasonable structures, it is known that environmental ions and ligands crucially impact the final shape and size. In this work, phosphine-stabilized gold is considered as a test system and DFT calculations are performed for clusters with and without ligands, producing a database containing >10000 structures for Aun(PH3)m (n <= 12). We find that the ligation of phosphines affects the thermodynamic stability, bonding, and electronic structure of Au nanoclusters, specifically such that hidden ground state cluster geometries are stabilized that are dynamically unstable in the pure gold system. Further, the addition of phosphine introduces steric effects that induce a transition from planar to nonplanar structures at 4-5 Au atoms rather than up to 13-14 Au atoms, as previously predicted for bare clusters. This work highlights the importance of considering the ligand environment in the prediction of nanocluster morphology and functionality, which adds complexity as well as a rich opportunity for tunability.

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