Interface-Mediated Assembly of Nanoparticles into Tunable Anisotropic Architectures

Polymer nanocomposites have attracted considerable scientific and technological interest, as such composites combine desirable material properties of both the polymer and the nanoparticles (NPs). New applications of composites often require higher-order, low-dimensional (anisotropic) organization of NPs in polymers, e.g., 1d strings, percolating networks, or 2d sheets. While self-assembly provides a powerful bottom-up approach for fabricating higher-order nanostructures, achieving unique low-dimensional assemblies of NPs in polymers is challenging since NPs tend to self-assemble into three-dimensional close-packed aggregates to minimize their total free energy. In this dissertation, I tackle this challenge of achieving anisotropic NP assembly in polymers through molecular dynamics (MD) simulations along with global optimization and machine learning techniques. First, I present a new strategy for assembling NPs into anisotropic architectures in polymer matrices, which takes advantage of the interfacial tension between two mutually immiscible polymers forming a bilayer and differences in the relative miscibility of polymer grafts with the two polymer layers to trap NPs within 2d planes parallel to the interface. Coarse-grained MD simulations are used to demonstrate this strategy, where I illustrate the assembly of NP clusters, such as trimers with tunable bending angle and anisotropic macroscopic phases, including serpentine and branched structures, ridged hexagonal monolayers, and square-ordered bilayers. The above MD simulations are however inefficient for determining the equilibrium structures of NP assemblies, especially those with many particles or complex unit cells. I adapt the efficient Basin-hopping Monte Carlo algorithm to locate the global minimum-energy configurations of NPs at interface, which allows us to explore the full breadth of NP structures possible at interface and discover many unique NP, such as binary superlattices, several of which are yet to be experimentally realized. While exploring the assembly of polymer-grafted NPs at polymer interfaces using explicit coarse-grained MD simulations, we observe that multi-body effects play an important role in the formation of quasi-1d structures. Motivated by this observation, and by similar observations in bulk polymer, I introduce a general machine learning (ML) approach to develop an analytical potential that can describe many-body interactions between polymer-grafted NPs in a polymer matrix, where the high-dimensional energy landscape of NPs is fitted by permutationally invariant polynomials as a function of their interparticle distances. The developed potential reduces the computational cost by several orders of magnitude and thus allows us to explore NP assembly at large length and time scales. Lastly, I investigate the orientational behaviors of shaped NPs (cubic NPs) at interfaces. I demonstrate the possibility of tuning the orientations of nanocubes between all three orientation phases (face up, edge up and vertex up) through polymer grafts and then take advantage of their orientational effects to assemble them into unique clusters, such as rectilinear strings, close-packed sheets, bilayer ribbons, and perforated sheets. Furthermore, by using two species of grafts, where one is hydrophilic and the other is hydrophobic, I demonstrate that the interactions between nanocubes can be further manipulated by controlling the length and stoichiometry of the two grafts, leading to more open, reconfigurable NP assemblies. Overall, this dissertation suggests that interfacial assembly of NPs could be a promising approach for fabricating next-generation functional materials with potential applications in plasmonics, electronics, optics, and catalysis.