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Understanding the Stabilization of Single-Walled Carbon Nanotubes and Graphene in Ionic Surfactant Aqueous Solutions: Large-Scale Coarse-Grained Molecular Dynamics Simulation-Assisted DLVO Theory
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文摘
Understanding the dispersion of single-walled carbon nanotubes (SWCNTs) and graphene in surfactant aqueous solutions is essential for processing of these materials. Herein, we develop the first theoretical framework, which combines large-scale coarse-grained (CG) molecular dynamics (MD) simulations with the Derjaguin鈥揕andau鈥揤erwey鈥揙verbeek (DLVO) theory and Langmuir isotherm, to inform the mechanisms of surfactant adsorption and induced colloidal stability. By carrying out large-scale CG-MD simulations (鈭? million CPU h), we successfully calculated the surface coverage of the widely used ionic surfactant sodium cholate (SC) on different carbon nanomaterials at experimentally realistic SC concentrations. This was accomplished by simultaneously simulating SC micellization and adsorption in one simulation box. Our theoretical framework further allows us to quantify the surface electric potential and the potential energy barrier height that maintains colloidal stability of dispersed SWCNTs or graphene sheets as a function of SC concentration, C, and radius of the SWCNT, r. We found that, for a specific carbon nanomaterial, there exists an optimal surfactant concentration, resulting from a trade-off between the increase in surfactant adsorption (i.e., surface charge density) and the increase in ionic strength (i.e., inverse Debye length) in the bulk aqueous phase. For the first time, we predict that small-radius SWCNTs promote higher surfactant adsorption than large-radius SWCNTs in the high SC concentration regime and, surprisingly, that the opposite behavior is exhibited in the low SC concentration regime. These findings suggest new experimental implications for the separation of SWCNTs based on their radius by tuning the SC concentration. In addition, we also predict that monolayer graphene exhibits better colloidal stability than multilayer graphene.

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