(121a) Coarse-Grained Molecular Dynamics Simulations of Self-Assembled Structures of Cylindrical Micelles and Charged Nanoparticles
Wormlike micelles in solutions are ‘living polymers’ which exhibit reversible self-assembly and rich rheological behavior due to shear-induced conformational and phase changes. In addition, entangled micelles are excellent templates for creating stable and 3-D quasi-ordered active nanostructures suitable for a variety of practical applications ranging from plasmonics to medicine. In this work, equilibrium and non-equilibrium coarse-grained molecular dynamics (CG MD) simulations are employed to decipher the fundamental mechanisms of self-assembly between nanoparticles and cylindrical micelles and how such hybrid structures are perturbed by flow shear. MARTINI force fields are employed to describe micelle structures as done in our recent work . Specifically, we investigate the interaction between cylindrical micelles of cationic cetyltrimethylammonium chloride (CTAC) surfactant and negatively charged nanoparticles. Surfactant molecules were observed to form self-assembled structures such as vesicles or surfactant coronas around the nanoparticles at zeta potentials of -40 mV or larger in magnitude. Potential of mean force (PMF) between the nanoparticle and cylindrical micelles evaluated using umbrella sampling suggests that the end caps of cylindrical micelles are more susceptible to opening up and forming junctions with nanoparticles compared to relatively stiff central domains. This is due to the fact that higher curvature and less ordering of surfactants at the end caps result in higher free energy, which can be reduced by the formation of a micelle-particle bridge. Effects of governing parameters such as the zeta potential of the nanoparticle, added salt concentration, and flow shear on the association and phase behavior of micelle-nanoparticle structures will be also discussed.
Acknowledgement: We acknowledge National Science Foundation grant CBET-1049454 and the New York Center for Computational Sciences at Stony Brook University/Brookhaven National Laboratory for support of this research.
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