The Structure of Adamantane Clusters: Atomistic vs. Coarse-Grained Predictions From Global Optimization

Effects of oxidative adsorbates and cluster formation on the electronic structure of nanodiamonds

Nanodiamonds (NDs) are modern high-potential materials relevant for applications in biomedicine, photocatalysis, and various other fields. Their electronic surface properties, especially in the liquid phase, are key to their function in the applications, but we show that they are sensitively modified by their interactions with the environment. Two important interaction modes are those with oxidative aqueous adsorbates as well as ND self-aggregation towards the formation of ND clusters. For planar diamond surfaces it is known that the electron density migrates from the diamond towards oxidative adsorbates, which is known as transfer doping. Here, we quantify this effect for highly curved NDs of varying sizes (35-147 C atoms) and surface terminations (H, OH, F), focusing on their interactions with the most abundant aqueous oxidative adsorbates (H₃ O⁺ , O₂ , O₃ ). We prove that the concept of transfer doping stays valid for the case of the high-curvature NDs and can be tuned via the ND's specific properties. Secondly, we investigate the electronic structures of clusters of NDs which are known to form in particular in aqueous dispersions. Upon cluster formation, we find that the optical gaps of the structures are significantly reduced, which explains why different experimental values were obtained for the optical gap of the same structures, and the cluster's LUMO shapes resemble atom-type orbitals, as in the case of isolated spherical NDs. Our findings have implications for ND applications as photocatalysts or electronic devices, where the specific electronic properties are key to the functionality of the ND material.

Towards elucidating structure of ligand-protected nanoclusters

Developing a centralized database for ligand-protected nanoclusters can fuel machine learning and data-science-based approaches towards theoretical structure prediction.