(623e) Quantifying Anisotropic Properties Emerging from Uniformly Coated Gold Nanoparticles

Chew, A. K., University of Wisconsin
Van Lehn, R. C., University of Wisconsin-Madison
Monolayer-protected gold nanoparticles (GNPs) have attracted significant interest in biomedical applications because of their ability to deliver drugs, target specific receptor sites, and be detected in vivo. GNPs are typically protected by self-assembled monolayers (SAMs) consisting of ligands with a sulfur head group, alkane backbone, and terminal end groups that adsorb onto the GNP surface. SAM-protected GNPs are advantageous because of their ease of fabrication and tunable properties, such as the gold core size and shape or the ligand chain length, structure, and end group chemistry. These tuning parameters allow us to explore how small structural changes can lead to significant differences in overall monolayer structure. For instance, long chain alkanethiols have been experimentally found to form structurally asymmetric monolayers, even though the SAM composition is chemically homogenous. These changes in monolayer structure lead to anisotropic properties that influence overall GNP behavior in biological environments, which are difficult to characterize experimentally at the molecular level.

Recently, molecular simulations have emerged as a valuable tool to modeling GNPs as they are small enough that their behavior can be modeled at atomistic resolution. In this work, we use classical atomistic molecular dynamics simulations to model SAM-protected GNPs and understand how tuning either the gold core or the ligand can affect overall GNP characteristics. We have developed a generalized system preparation workflow for SAM-protected GNPs which we use to explore how gold core morphology influences the structure of uniformly nonpolar alkanethiol SAMs. We find that long alkanethiol ligands form quasi-crystalline domains, or bundles, in which ligands orient in the same direction and leads to heterogenous, anisotropic surface properties. We use a clustering algorithm to identify subpopulations of ligands that are bundled to interrogate the distinct properties of these subpopulations. For example, bundled ligands generally have higher ligand order and lower surface area accessible to solvent interactions. We then extend the ligand selection to include more complex ligands, such as end-functionalized hydrophilic or charged alkanethiols, to study how the selection of end group influences these spatially heterogenous characteristics. The computational tools developed here are crucial to understanding the influence of gold core and ligand selection on monolayer characteristics, which can inform the stability or self-assembly of GNPs in solution and the interaction of GNPs with other molecules.