Nanoparticles are materials that have dimensions on the nanometer scale, and they can be assembled into larger materials. The shapes and surface chemistries of nanoparticles can be tailored in ways that atoms or molecules cannot. While spherical nanoparticles with one type of surface coverage or patchy coverage have been studied extensively, other particle shapes that can be prepared, like polyhedra and frames, have been much less explored. This research project aims to devise and validate general computational methods to guide investigators in selecting the key characteristics of arbitrarily-shaped components that lead to their efficient self-assembly into target materials. The outcomes of this research could impact the advanced materials and semiconductor industries by providing principles to design and prepare materials for photonics, photovoltaics, and catalysis applications. The results of this research will be disseminated through presentations at professional meetings, an industrial outreach program organized by Cornell, and a podcast created by graduate students of the Chemical and Biomolecular Engineering (CBE) Department. The research project serves as a training ground for graduate students to perform cutting edge research. An undergraduate student will be recruited to create a multimedia module about engineering self-assembly at the nanoscale to be disseminated through the CBE Women's group outreach initiatives that target rural High School girls. Results from this investigation will also be used in the curriculum of two graduate statistical mechanics courses at Cornell.
This research project involves formulating and validating variational principles for enhancing thermodynamic and kinetic assembly behavior. The researchers will implement efficient simulation methods to test and improve the variational principles by applying them to systems containing: (i) hard-core components, (ii) nanoparticles grafted with complementary DNA strands that favor inter-species bonding, (iii) nonconvex shapes where non-additive mixing can occur, and (iv) building blocks that form open lattices. The particle shapes to be investigated include spheres, polyhedra, and frames. The research is based on the hypothesis that the minimization of a free-energy figure of merit can identify how particle-particle interactions should be tuned to enhance their productive assembly into target superlattices. In terms of thermodynamic behavior, it is hypothesized that the free-energy at the order-disorder phase transition should be minimized for optimal stability of pure-component crystals and substitutionally ordered compounds. In terms of kinetic behavior, it is hypothesized that inter-particle interactions that lead to a reduced free-energy barrier height for the disorder-to-order transition, at a fixed degree of supersaturation, correlate with optimal kinetic ability to self-assemble. Advanced methods to map such free-energies as a function of parameters of the system's Hamiltonian will be used to identify optimal values of such parameters. In testing these variational principles, the phase and kinetic behavior of many systems of scientific interest will be mapped out, potentially unveiling new phases and nucleation mechanisms.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.