Dynamics of Surfactant-Suspended Single-Walled Carbon Nanotubes in a Centrifugal Field

Abstract

A hydrodynamic model is used to describe the motion of surfactant-suspended single-walled carbon nanotubes in a density gradient, while being subjected to a centrifugal field. The number of surfactant molecules adsorbed on each nanotube determines its effective density and, hence, its position in the gradient after centrifugation has been completed. Analysis of the spatial concentration distributions of CoMoCAT nanotubes suspended with 2 w/v% sodium cholate yielded 2.09, 2.14, and 2.08 surfactant molecules adsorbed per nanometer along the length of the (6,5), (7,5), and (8,7) nanotubes, respectively. The estimates are commensurate with experimental values reported in the literature and can be used to predict the fate of sodium cholate-suspended nanotubes in the separation process. Since the density of the surfactant-nanotube assembly is highly sensitive to the number of adsorbed molecules, a perturbation would cause it to be enriched at a different location in the gradient. The level of sensitivity is also reflected in the 95% confidence levels that are reported in this work.

Optimizing sonication parameters for dispersion of single-walled carbon nanotubes

“…Moreover, the surfactant-based functionalization facilitates modification of the buoyant density of SWCNTs in terms of their diameters or electronic properties [17]. Thus it is possible to sort these SWCNTs using density gradient ultracentrifugation (DGU) [18][19][20]. The dispersion mechanism has been explored and results indicate that the adsorption of surfactants on the surface of SWCNTs forms stronger p-p stacking interactions than those between the SWCNTs [21].…”

Determination of the Surfactant Density on SWCNTs by Analytical Ultracentrifugation

“…Nair et al used a hydrodynamic model to describe the motion of SWCNTs in a centrifugal field; [15] however, their study was based on buoyant densities, and the spreading of the nanotubes close to their isopycnic points. Since the buoyant density also includes a large hydration shell, it is not possible to draw conclusions on the mass and volume of the surfactant layer alone.…”

Combined inverse-forward artificial neural networks for fast and accurate estimation of the diffusion coefficients of cartilage based on multi-physics models

“…We assigned a neo-Hookean material model for cartilage with a Young's modulus of 10 MPa, Poisson's ratio of 0, hydraulic permeability of 10 -3 mm 4 /Ns, and effective solubility of 1. Diffusion coefficient of iodixanol was set to 0.00025 mm 2 /s (Nair et al, 2008) and the diffusion tensor was assumed isotropic. Since cartilage is a heterogeneous material, we used a previously proposed multi-zone model where water content varied from 0.8 in the superficial zone (20% of the cartilage thickness) to 0.7 in the middle zone (50% of the cartilage thickness) and 0.6 in the deep zone (30% of the cartilage thickness) (Sophia Fox et al, 2009) (Figure 1b).…”