Abstract
We explore the use of coarse-grained dissipative particle dynamics simulations to predict critical micelle concentrations (CMCs) in polydisperse surfactant mixtures and blends. By fitting pseudo-phase separation models (PSMs) to aqueous solutions of binary surfactant mixtures at selected compositions above the CMC, we avoid the need for expensive simulations of more complex multicomponent mixtures performed as a function of dilution. The approach is demonstrated for sodium laureth sulfate (SLES) surfactants with polydispersity in the ethoxylate spacer. For this system, we find a modest degree of cooperativity in micelle formation, which we attribute to the reduced repulsion between charged headgroups for surfactants with dissimilar ethoxylate spacer lengths. However, this is insufficient to explain the lowered CMC often observed in commercial SLES samples, which we attribute to the presence of small amounts of unsulfated alkyl ethoxylates and/or traces of salt.
Highlights
Surfactants readily self-assemble in aqueous solution into supramolecular aggregates known as micelles.[1]
The above considerations have motivated a significant renewed interest in modeling surfactants.[7−17] In our work, we have developed coarse-grained dissipative particle dynamics (DPD) models with explicit chemical specificity, which we have shown can accurately reproduce the critical micelle concentrations (CMCs) of pure nonionic[8] and anionic[10] materials
As described in the Methods section, we block-average the total concentration of surfactants in the former class to obtain estimates of the equilibrium notional “monomer” surfactant concentration cm at a given state point (SI Table S1)
Summary
Surfactants readily self-assemble in aqueous solution into supramolecular aggregates known as micelles.[1]. The CMC has become a key design target because it is correlated with a multitude of physicochemical properties such as a minimum in interfacial tension[2] and a maximum in detergency.[3] As surfactants are widely used in applications ranging from manufacturing processes through to home and personal care products,[4] a significant effort has been expended to develop thermodynamic models that can be used to predict and optimize the CMC.[5] In this, we expect that the adoption of computer-aided formulation (CAF) design practices can play a key role, complementing traditional experimental approaches, rapid screening methods, and data-driven models. Rapid decarbonization of the current multi-billiondollar surfactant market (moving away from petrochemicals and traditional plant-based feedstocks toward sustainably sourced raw materials) is driving an urgent need to update these models. In addition to screening new surfactants and aiding rapid prototyping, CAF tools and methodologies can be used to hone raw materials specifications, quantify the effects of impurities, de-risk scale-up, and shorten time to market
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