(352h) Structure and Rheology of Micelle-Nanoparticle and Polymer-Nanoparticle Networks From Molecular Dynamics Simulations

Sureshkumar, R., Syracuse University
Sambasivam, A., Syracuse University
Dhakal, S., Syracuse University

Self-assembly of nanoparticles (NPs) with cylindrical micelles or polymers in an aqueous solution has generated much interest recently [1-3]. Such “nanofluids” can be tailored to respond to specific optical, magnetic, thermal or electrical stimulus depending on the NP characteristics. For instance, we have shown previously that by utilizing noble metal NPs, plasmonic fluids with tunable optical properties could be synthesized by controlling the particle shape, size, and surface functionalities [4]. Although these experiments have shed light into the plausible mechanisms of self-assembly, a quantitative description of the role of NP shape and size, ratio of the surfactant to the salt concentrations, and surfaces charge on the structure, stability and rheology of such fluids is still lacking. Extending our previous coarse grained molecular dynamics (MD) simulations for surfactant self-assemblies [5, 6] and their interactions with a single NP [7-8], we investigate the structure and rheological properties of particulate networks consisting of multiple (O(10)) NPs. In these simulations, explicit solvent/salt mediated interactions among surfactants are considered while the NP-surfactant interactions are represented through a mean field potential inspired by the predictions of fully explicit MD simulations of NP-micelle interactions. We find that the stability and structure of the network are highly dependent on the ratio of surfactant/salt concentration. Effects of NP size, shape, volume fraction and surface charge density on the structure and rheological properties of the network will be discussed. Extension of the simulation framework to study polymer-colloidal particle networks as a mechanical model for bacterial biofilms will also be presented.

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Acknowledgements: We acknowledge National Science Foundation grants 1049489 and 1049454 for the support of this research.