NER: Molecular Modeling of Self-Assembled Nanostructures on Surfaces and in Narrow Pores

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The self-assembly of nanostructures on surfaces and in porous media has been studied extensively by experiment, and is of interest in a wide range of applications, including optical and biological sensors, lithography, fabrication of nano-scale devices, and biomimetic materials. Despite this wide interest, the few attempts to develop theories and simulation methods for these systems give a poor account of the formation of the nano-structures and the effects of major variables (surfactant architecture, nature of surface, temperature, concentration, etc.) on them; some trends (e.g. temperature dependence) are predicted to be the opposite of those found experimentally.The aim of this project is to carry out a one year feasibility study to develop and evaluate a new multi-scale molecular simulation strategy to predict the equilibrium behavior of nano-structures formed from non-ionic surfactants on planar surfaces and in nano-pores. The simulations will cover size ranges from sub-Angstrom to hundreds of nanometers by using a combination of ab initio, atomistic simulation, and discretized lattice Monte Carlo simulation methods. By optimizing the lattice discretization and intermolecular potentials, the PI hopes to develop an approach that can predict not only the nanostructures that form, but the influence of temperature, composition, solvent, surfactant architecture, nature of the solid surface, and morphology of the pore structure, on the self-assembled structures. The challenge will be to include a sufficient level of molecular detail and sophistication in the model, while preserving sufficient simplicity that calculations can be made in a reasonable time on current computers.The feasibility of the method will be evaluated by comparison with experimental data. Criteria for success will include (a) correct prediction of qualitative trends of property behavior (adsorption, structures, aggregate size, heats) with variation in temperature, concentration, type of surface, etc., (b) quantitative agreement with experiment, and (c) computational burden of the calculations.