(779h) Modeling Surfactant-Assisted Stabilization of Carbon Nanotubes and Graphene in Ionic Surfactant Aqueous Solutions: Coarse-Grained Molecular Dynamics Simulations and Modified DLVO Theory | AIChE

(779h) Modeling Surfactant-Assisted Stabilization of Carbon Nanotubes and Graphene in Ionic Surfactant Aqueous Solutions: Coarse-Grained Molecular Dynamics Simulations and Modified DLVO Theory

Authors 

Shih, C. J. - Presenter, Massachusetts Institute of Technology
Lin, S., Massachusetts Institute of Technology
Strano, M., Massachusetts Institute of Technology
Blankschtein, D., Massachusetts Institute of Technology



Dispersion of single-walled carbon nanotubes (SWCNTs) and graphene in ionic surfactant aqueous solutions is a very important route to further purify, self-assemble, and functionalize these nanomaterials for electronic, biological, and sensor applications. In order to optimize the yield and colloidal stability, it is essential to understand the mechanisms responsible for the concentration-dependent adsorption of surfactant molecules on carbon nanomaterials, as well as the effects of surfactant micellization and surface geometry of the carbon nanomaterials. Molecular dynamics (MD) simulations are a powerful tool to investigate the interactions between surfactants and carbon nanomaterials at the molecular level. However, the length and time scales associated with the systems considered here are far beyond current computational capabilities using conventional all-atomistic MD simulations. With this in mind, we have used “coarse-grained” MD simulations to successfully simulate micellization and adsorption on SWCNTs or graphene in one simulation box. Specifically, the adsorption density of surfactant molecules was quantified as a function of the bulk surfactant concentration. Based on the simulated results, we developed a model that describes the adsorption density as a function of surfactant concentration and the geometry of the SWCNTs and graphene. In combination with a modified DLVO theory of colloid stability, for the first time, we successfully modeled the surface electric potential, the intertube (or intersheet) potential energy profile, and the energy barrier height. These results allow us to predict the optimized surfactant concentration needed to disperse different SWCNTs and graphene in aqueous media. We believe that the development of advanced methods to separate SWCNTs and graphene in aqueous media will be greatly facilitated by the molecular-level insight and theoretical analyses presented here.