Using dissipative particle dynamics (DPD) simulations, we explore the specifics of micellization in the solutions of anionic and cationic surfactants and their mixtures. Anionic surfactant sodium dodecyl sulfate (SDS) and cationic surfactant cetyltrimethylammonium bromide (CTAB) are chosen as characteristic examples. Coarse-grained models of the surfactants are constructed and parameterized using a combination of atomistic molecular simulation and infinite dilution activity coefficient calibration. Electrostatic interactions of charged beads are treated using a smeared charge approximation: the surfactant heads and dissociated counterions are modeled as beads with charges distributed around the bead center in an implicit dielectric medium. The proposed models semiquantitatively describe self-assembly in solutions of SDS and CTAB at various surfactant concentrations and molarities of added electrolyte. In particular, the model predicts a decline in the free surfactant concentration with the increase of the total surfactant loading, as well as characteristic aggregation transitions in single-component surfactant solutions caused by the addition of salt. The calculated values of the critical micelle concentration reasonably agree with experimental observations. Modeling of catanionic SDS-CTAB mixtures show consecutive transitions to worm-like micelles and then to vesicles caused by the addition of CTAB to micellar solution of SDS.
This paper presents a consistent strategy for parametrization of coarse-grained models of chain molecules in dissipative particle dynamics (DPD), where the soft-core DPD interaction parameters are fitted to the activities in solutions of reference compounds that represent different fragments of target molecules. The intercomponent parameters are matched either to the infinite dilution activity coefficients in binary solutions or to the solvent activity in polymer solutions. The respective calibration relationships between activity and intercomponent interaction parameter are constructed from the results of Monte Carlo simulation of the coarse-grained solutions of reference compounds. The chain conformation is controlled by the near neighbor and second neighbor bond potentials, which are parametrized by fitting the intramolecular radial distribution functions of the coarse-grained chains to the respective atomistic molecular dynamics simulations. The consistency, accuracy, and transferability of the proposed parametrization strategy is demonstrated drawing on the example of nonionic surfactants of the poly(ethylene oxide) alkyl ether (CnEm) family. The lengths of tail and head sequences are varied (n = 8-12 and m = 3-9), so that the critical micelle concentration ranges from 10 to 0.1 mM. The surfactants are modeled at different coarse-graining levels using DPD beads of different diameters. We found consistent agreement with experimental data for the critical micelle concentration and aggregation number, especially for surfactants with relatively long hydrophilic segments. Depending on the system, we observed surfactant aggregation into spheroidal, elongated, or core-shell micelles, as well as into irregular agglomerates. Using the models at different coarse-graining levels for the same molecules, we found that the smaller the bead size the better is agreement with experimental data.
We present a coarse-grained model of the acid form of Nafion membrane that explicitly includes proton transport. This model is based on a soft-core bead representation of the polymer implemented into the dissipative particle dynamics (DPD) simulation framework. The proton is introduced as a separate charged bead that forms dissociable Morse bonds with water beads. Morse bond formation and breakup artificially mimics the Grotthuss hopping mechanism of proton transport. The proposed DPD model is parameterized to account for the specifics of the conformations and flexibility of the Nafion backbone and sidechains; it treats electrostatic interactions in the smeared charge approximation. The simulation results qualitatively, and in many respects quantitatively, predict the specifics of nanoscale segregation in the hydrated Nafion membrane into hydrophobic and hydrophilic subphases, water diffusion, and proton mobility. As the hydration level increases, the hydrophilic subphase exhibits a percolation transition from a collection of isolated water clusters to a 3D network of pores filled with water embedded in the hydrophobic matrix. The segregated morphology is characterized in terms of the pore size distribution with the average size growing with hydration from ∼1 to ∼4 nm. Comparison of the predicted water diffusivity with the experimental data taken from different sources shows good agreement at high and moderate hydration and substantial deviation at low hydration, around and below the percolation threshold. This discrepancy is attributed to the dynamic percolation effects of formation and rupture of merging bridges between the water clusters, which become progressively important at low hydration, when the coarse-grained model is unable to mimic the fine structure of water network that includes singe molecule bridges. Selected simulations of water diffusion are performed for the alkali metal substituted membrane which demonstrate the effects of the counter-ions on membrane self-assembly and transport. The hydration dependence of the proton diffusivity reproduces semi-qualitatively the trend of the diverse experimental data, showing a sharp decrease around the percolation threshold. Overall, the proposed model opens up an opportunity to study self-assembly and water and proton transport in polyelectrolytes using computationally efficient DPD simulations, and, with further refinement, it may become a practical tool for theory informed design and optimization of perm-selective and ion-conducting membranes with improved properties.
Creating a systematic framework to characterize the structural states of colloidal self-assembly systems is crucial for unraveling the fundamental understanding of these systems’ stochastic and non- linear behavior. The most...
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