(7ii) Predictive Bottom-up Design of Nanomaterials for Biomimicking Applications

Research Interests:  The 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. For these goals, I am interested in exploring design rules for a wide range of molecular and colloidal systems via bottom-up approaches such as self- and directed-assembly techniques. The design rules cover not only the assembling building block geometry and interactions, but also assembly pathways, at equilbrium and far-from-equilbrium. The target nanostructures are judiciously chosen for specific applications in optical devices, drug delivery, biosensing and energy storage and conversion. As such, the assembly processes may occur either in bulk or at the interfaces and the interactions between the assembling building blocks require mutiscale modeling. To efficiently address the fundamental challenges with bottom-up approaches, I am particularly intersted in employing and developing tools and methods that have been actively used across multidisciplines, ranging from large-scale GPU-accelerated simulation codes to enhanced sampling methods to data mining techniques.

Teaching Interests:  I am interested in teaching Chemical Engineering core courses including Thermodynamics and Transport, and developing my own courses on Polymer Physics and Computational Nanoscience at graduate level. I would like to facilitate cooperative learning in classroom and incorporate practical aspects with fundamental problems into lecture materials. My objectives are to explain to the students why the coursework benefit them with their career and how they can succeed with the courses.