Title: Hierarchical Modeling and Design of Corona Driven DNA-Mediated Self-Assembly
Description: Nanoscale colloids and nanoparticles (NPs), have recently emerged as a new class of materials that possess photonic, plasmonic, and/or catalytic emergent properties. However, methods for their rational fabrication into materials with designed structural organization remain to be established. Self-assembly -- the idea that NPs can find each other and spontaneously form a targeted macroscale structure with prescribed microscale organization -- is attractive in this context, particularly because it potentially lends itself to facile, large-scale manufacturing. Such a process, however, relies on a combination of interactions and shape effects for the formation of ordered long-range morphologies and a detailed molecular understanding of the governing physics for these systems remains an open question. In an attempt to reduce the complexity of such systems, a major thrust has been to use two NP sub-populations grafted with complementary single stranded DNA (DNA-NPs). The base pairing of these strands drives their spontaneous organization into crystalline arrays.

DNA-mediated self-assembly provides a powerful tool to experimentally realize three dimensional crystalline ordering of NP networks; however, the majority of works within this field have focused on an isotropic, purely attractive interaction motif. While successful in controlling NP ordering, the usage of such symmetric designs severely restricts the range of accessible morphologies. This thesis systematically addresses the various limitations imposed by such a design strategy through both theoretical modeling of DNA-NPs interactions and inverse design of optimized self-assembly building blocks. We first relax the assumption that enthalpy completely dominates self-assembly by directly accounting for the effects of chain-chain repulsion as well as entropic frustrations that results from varying the mixing stoichiometry. We then build in the effect of utilizing anisotropy as a structural motif through the development of a scaling theory that captures the interplay between the chain dynamics and local curvature that results in the formation of non-trivial anisotropic coronas.

The effects of anisotropy on both the local morphology and long-range crystalline ordering can then be model through the usage of mean-field and perturbation theories. The resulting composite model enables us to directly study how nano-scale phenomena drive micron-scale self-assembly. Lastly, theoretical developments are combined with a genetic algorithm optimization process into an inverse design framework that allows for an a priori design of molecular building blocks such that they spontaneously pack into any desired lattice morphologies. This strategy serves to address the long-standing challenge of nanomaterials design where one can take arbitrary nano-scale objects and arrange them into desired three-dimensional lattices that posses interesting, emergent properties.