There is currently much interest in making crystals (assemblies) of nanoparticles (NPs), which can be considered 'superlattices' due to the potential of these superlattices to be used as unique optical switching devices or for sensing. While traditional nano-materials development is mostly a top-down approach, whereby novel nanomaterials and structures are first discovered and then their properties are determined and applications envisioned, the PIs propose a materials-by-design approach, whereby they will devise and experimentally validate a theoretical framework to rationally design ligand-capped NPs to self-assemble into specific targeted structures (superlattices) with low coordination. Their choice of spherical nanoparticles (NPs) as the model system to be tested, rather than the harder to make anisotropic NPs, will increase their chances of being successful. This is a well-thought, well-integrated proposal to test and validate a new theoretical framework for the inverse design of self-assembling nanocrystals. Results from the research will have broad applicability, opening up the possibilities of rationally designing interactions for novel self-assembling structures.
The PI proposes to extend the inverse statistic-mechanical (SM) optimization method developed in his recent CBET grant 0165357 and suggests ways to explore, in-silico, the assembly of various lattices (hexagonal, cubic, etc) in a simple one component system of spherical particles by just adjusting inter-particle potentials. He plans further to simulate the equilibrium phase diagrams and dynamics of cush NP formation. The experimental Co-PI will then use the results of the simulation to fabricate and characterize selected ligand-capped nanocrystals, which had been optimized in the in-silico simulations. Their plan comprises three major activities: 1) Use the XM-based inverse optimization to determine interaction parameters that favor targetted superlattices. 2) Use molecular simulations, together with the interaction potentials, to determine equilibrium and kinetic phase behaviors (discover design rules). 3) Synthesize and investigate the best candidate models and characterize the systems using techniques such as grazing-incidence small-angle x-ray scattering (GISAXS).
