(70d) Predictive Design of Next-Generation Nanomaterials and Devices Via Bottom-up Approaches

Nanomaterials and devices that resemble biological matter in their ability to reconfigure and adapt on demand have captured increasingly growing interest over the past decades. Towards this end, bottom-up approaches including self-and directed-assembly techniques have been shown as a promising means for engineering the underlying nanostructure of this exciting class of materials and devices. In these approaches, the underlying nanostructures are obtained from the self- or directed-organization of nano building blocks such as block copolymers, nanoparticles and colloids. The fundamental challenges to the bottom-up techniques are to design the optimal assembling units, to tailor their effective interactions and to find efficient assembly pathways. In this contribution, I will address the design rules for the building blocks’ geometry and their interactions targeting several hierarchically assembled structures including terminal supraparticles, helical ribbons and various columnar morphologies. I will also demonstrate numerous unconventional pathways to assemble these bio-mimicking and reconfigurable nanostructures including interaction tuning to shape shifting. The ultimate goals of my research program are to design of adaptive, programmable nanomaterials that are of potential use in a host of nanotechnology applications including, but not limited to, optical devices, drug delivery, biosensing and energy storage and conversion. Also presented are the tools and methods I have been developing to improve the efficiency of the computational studies of interest, ranging from large-scale GPU-accelerated codes to enhanced sampling methods.