The Effect of Anisotropy Dimensions on the Self-Assembly of Nanoparticles.

Abstract

The incredible control over nanomaterials available to scientists argues for a conceptual framework to understand how subtle changes to the chemistry of a nanomaterial effects its bulk properties. Anisotropy dimensions provide a conceptual framework to understand and categorize different nanomaterials. Surface coverage, aspect ratio, and faceting are examples of anisotropy dimensions. The major question addressed in this thesis is what is the effect of systematically varying anisotropy dimensions on self-­‐assembly. The self-­‐assembly of lock and key colloids, Archimedean tilings, and deformed nanoplates are the nanomaterials whose anisotropy dimensions are varied. By tuning the reconfigurability and bond distance anisotropy dimensions, lock and key colloids are shown to assemble interesting ordered structures, such as random square triangle tilings, that would be inaccessible with additive non-­‐reconfigurable but similar building blocks. By tuning the patchiness and faceting anisotropy dimensions, a set of design rules for the targeted self-­‐assembly of the Archimedean tilings was developed. By altering the faceting and anisotropy dimensions, general conclusions about the necessary conditions for forming lattice, complex, and porous tilings were discovered for nanoplates. Clearly, the choice of anisotropy dimension is essential to the successful self-­‐assembly of different crystalline structures. The careful optimization of anisotropy dimensions has general applicability to multifunctional materials and dynamic materials. Also, the systematic transformations of anisotropy dimensions is an essential stepping stone to the creation of machine learning systems to predict the optimal building block for the self-­‐assembly for a wide variety of materials.