Structure and dynamics of nanoemulsions: Insights from combining dynamic and static neutron scattering

Abstract

Despite their lack of thermodynamical stability, nanoemulsions can show a remarkable degree of kinetic stability. Among the various different preparation methods the phase-inversion concentration method is particularly interesting as it occurs spontaneously. Here we investigate such a system composed of a surfactant, cosurfactant, and oil that upon dilution with water forms long time metastable oil-in-water nanoemulsion droplets. The dynamics of the amphiphilic monolayers and its elastic properties is important for their stability and therefore the monolayer dynamics have been investigated by neutron spin echo (NSE). Despite the difficulties arising from the inherently polydisperse nature and the large number of different components necessarily contained in commercial nanoemulsion formulations, information concerning the membrane rigidity was extracted from the combination of small angle neutron scattering and NSE and several different formulations are compared. These results show that small amounts of different admixed ionic surfactants can modify the monolayer rigidity substantially and similarly effects of surface bound polyelectrolytes have been evaluated.

Softening of phospholipid membranes by the adhesion of silica nanoparticles – as seen by neutron spin-echo (NSE)

“…The absolute values for k are known to be wrong in many cases [59][60][61] however using an effective viscosity h eff ¼ h  3 instead of the bulk viscosity to account for dissipation between membrane and solvent has proven to be successful in the past to correct the values accordingly, mostly on microemulsions 60,61 but also on other systems, 62 including phospholipid bilayers. 63 In a somewhat more elaborate approach Watson and Brown 64 adapted a theory by Seifert and Langer, 65 in which contributions from lateral ow and interbilayer friction are considered, to the framework of the Zilman-Granek model.…”

Random walk centrality for temporal networks

“…To realize that, we have to formulate and characterize random walks on temporal networks. Previous studies have addressed diverse properties of the dynamics of random walks on temporal networks, for instance, the cover time [25], mean firstpassage time [26,27], the stationary density [27], mixing time [28,29], conditions for stationarity and ergodicity [30], and properties of the so-called active random walk [31,32].However, to apply a random walk centrality measure to real data, we have to understand random walks on real temporal network data. This is non-trivial for at least two reasons.…”

Dynamics of small unilamellar vesicles

“…The values obtained for κ with the Zilman-Granek model, using an effective solvent viscosity of η eff = 3η to account for additional dissipation in the membrane, [57][58][59] are shown in Fig. 7.…”

“…While the scaling of Γ with κ is correctly predicted, the absolute values of κ obtained from NSE measurements have often shown to be wrong and a number of approaches exist to correct them. 25,[57][58][59][60][61][62][63] For example, Seifert and Langer 60 took into account contributions from lateral flow in the membrane and interbilayer friction. 64 Watson and Brown 62 adapted that theory to the framework of the Zilman-Granek theory, and the measured, effective bending rigidity is given by κ eff = κ + 2d 2 k, where k is the monolayer compressibility modulus and d is the height of the monolayer neutral surface.…”

“…(12) to account for the finite size of the film and the molecules which comprise it. As here, we are mainly interested in relative changes in κ, we will limit ourselves to the simplest correction, which consists of using an effective solvent viscosity η eff = 3η, [57][58][59] which previously led to realistic values of κ.…”