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

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


Nguyen, T. - Presenter, Northwestern University
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.