Computational and Experimental Investigations of Phase-Separated Monolayers on Ultrasmall Noble Metal Nanoparticles

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Metal nanoparticles contain a small metallic core that might consist of just a few thousand atoms, surrounded by a layer of organic molecules, which determines its interaction with the environment. Layers made from two different types of molecules can form complex patterns such as strips or spots as they separate on the surface, like oil and water. The ability to manipulate these patterns could lead to new technologies that require precise control of the nanoparticle interaction with its surroundings. However, characterizing existing patterns is difficult due to the small size and curved shape of the metallic core. With support from the Macromolecular, Supramolecular and Nanochemistry Program in the Division of Chemistry, Professors David Green and Kateri DuBay at the University of Virginia are using a combination of experimental and computational techniques to study pattern formation on the nanoparticle surface. Their discoveries could enable innovations in optoelectronics, sensing, and medical applications. The project is also training future scientists and engineers. In addition, by engaging K-12 students in the Charlottesville area, as well as undergraduates at the University of Virginia and at historically black colleges and universities and minority-serving institutions in the southeast, they are providing educational and research opportunities to underrepresented minority students in STEM fields.This project aims to develop a tightly integrated program of theory, computation, and experiment to study self-assembled monolayers (SAMs) on ultra-small noble metal nanoparticles (NPs), where the monolayer is composed of a mixture of organic ligands that can move about on the surface and self-organize to form equilibrated nanophases. The work combines multiple analytic tools and algorithms in an integrated strategy to elucidate the driving forces that govern SAM morphology in NP-SAM systems. In particular, the computational tools enable comprehensive examination of the effects of length and chemical mismatches between ligands as well as NP size, facilitating the de novo design of SAM morphologies by tuning these characteristics. To determine how ligand type and NP size affect SAM morphology, single NP statistics are required. The research plans include: (1) synthesizing well-defined monolayer protected NPs; (2) mapping ligand distributions on NP surfaces with laser desorption ionization mass spectroscopy using NP fragments; and (3) predicting and visualizing SAM morphology with atomic simulation anchored by comparisons to experimental data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.