Critical micelle concentration of SDS through DPD simulations using COSMO-RS–based interaction parameters, the thermal effects

Many-body dissipative particle dynamics simulations of micellization of sodium alkyl sulfates

We present a study of micelle formation in alkyl sulfate surfactants using the simulation method of many-body dissipative particle dynamics (MDPD). We parametrise our model by tuning the intermolecular interactions in order to reproduce experimental values for the chemical potential and density at room temperature. Using this approach, we find that our model shows good agreement with experimental values for the critical micelle concentration (CMC). Furthermore, we show that our model can accurately predict CMC trends, which result from varying properties such as surfactant tail length and the salt concentration. We apply our model to investigate the effect of aggregation number on various micellar properties, such as the shape of individual micelles and the fraction of bound counterions. We show that micelles become aspherical at large aggregation numbers, in line with experimental predictions, and that longer tail surfactants are generally more spherical at all aggregation numbers compared to those which are shorter. We find excellent agreement between our simulations and experimental values for the degree of counterion binding, a factor that is crucial to accurately studying micellar shape, but one that is typically overlooked in the existing literature.

Analyzing the Accuracy of Critical Micelle Concentration Predictions Using Deep Learning

This paper presents a novel approach to predicting critical micelle concentrations (CMCs) by using graph neural networks (GNNs) augmented with Gaussian processes (GPs). The proposed model uses learned latent space representations of molecules to predict CMCs and estimate uncertainties. The performance of the model on a data set containing nonionic, cationic, anionic, and zwitterionic molecules is compared against a linear model that works with extended connectivity fingerprints (ECFPs). The GNN-based model performs slightly better than the linear ECFP model when there is enough well-balanced training data and achieves predictive accuracy that is comparable to published models that were evaluated on a smaller range of surfactant chemistries. We illustrate the applicability domain of our model using a molecular cartogram to visualize the latent space, which helps to identify molecules for which predictions are likely to be erroneous. In addition to accurately predicting CMCs for some surfactant classes, the proposed approach can provide valuable insights into the molecular properties that influence CMCs.

Dioctyl sodium sulfosuccinate surfactant self-assembly dependency of solvent hydrophilicity: a modelling study

The self-assembly of dioctyl sodium sulfosuccinate (AOT) model surfactant in solvent environments of differing polarity is examined by means of dissipative particle dynamics (DPD) bead model parametrized against Hildebrand solubility parameters from atomistic molecular dynamics (MD) simulations. The model predicts that in hydrophobic solvents (e.g. dodecane) the surfactant forms small (Nagg ∼ 8) reverse micellar aggregates, while in a solvent corresponding to water lamellar assembly takes place, in good agreement with literature structural parameters. Interestingly, solvents of intermediate polarity lead to formation of large, internally structured aggregates. In these, the surfactant headgroups cluster within the aggregate, surrounded by a continuous phase formed by the hydrocarbon tails. We show that the partitioning of the headgroups between the aggregate surface layer and the inner clustered phase depends primarily on solvent polarity, and can be controlled by the solvent, but also system composition. Finally, we compare the DPD assembly response to simplified effective interaction potentials derived at dilute concentration limit for the interactions. The comparison reveals that the simplified effective potential descriptions provide good level of insight on the assembly morphologies, despite drastic, isotropic interactions simplification involved.

Comprehensive review of the interfacial behavior of water/oil/surfactant systems using dissipative particle dynamics simulation

A comprehensive understanding of interfacial behavior in water/oil/surfactant systems is critical to evaluating the performance of emulsions in various industries, specifically in the oil and gas industry. To gain fundamental knowledge regarding this interfacial behavior, atomistic methods, e.g., molecular dynamics (MD) simulation, can be employed; however, MD simulation cannot handle phenomena that require more than a million atoms. The coarse-grained mesoscale methods were introduced to resolve this issue. One of the most effective mesoscale coarse-grained approaches for simulating colloidal systems is dissipative particle dynamics (DPD), which bridges the gap between macroscopic time and length scales and molecular-scale simulation. This work reviews the fundamentals of DPD simulation and its progress on colloids and interface systems, especially surfactant/water/oil mixtures. The effects of temperature, salt content, a water/oil ratio, a shear rate, and a type of surfactant on the interfacial behavior in water/oil/surfactant systems using DPD simulation are evaluated. In addition, the obtained results are also investigated through the lens of the chemistry of surfactants and emulsions. The outcome of this comprehensive review demonstrates the importance of DPD simulation in various processes with a focus on the colloidal and interfacial behavior of surfactants at water-oil interfaces.