Viscosity-control and prediction of microemulsions

Viscosity is a fundamental property of complex fluids that is still difficult to predict quantitatively. This macroscopic property originates from molecular as well as mesoscopic properties. The understanding and prediction of the evolution of the viscosity with changing parameters is crucial for several applications, e.g. for liquid-liquid extraction processes or the formulation of aqueous surfactant systems. In this work, a new “minimal” thermodynamic model taking into account the relevant free energies was developed that helps to understand, control and predict the evolution of the viscosity of microemulsions in presence of solutes. The term “minimal” means in that context that this model is based on a minimal set of parameters that are all measurable and have a physical meaning, thus minimizing the number of adjustable parameters. This model that considers the chemical terms at the molecular scale, the physical terms at the mesoscale as well as the flow characteristics at the macroscale was applied on water-poor extracting microemulsions as well as on aqueous anionic surfactant systems. Liquid-liquid extraction is the central technology in metal recycling. The extraction is always driven by oil-soluble extracting surfactants. TBP (Tributyl phosphate) is one of those widely used extractants. This molecule allows selective extraction of actinides in the framework of the nuclear fuel cycle as well as lanthanides for the life-cycle of electronic devices. Emulsification and demulsification processes in extraction devices are only efficient when the difference in viscosity between organic and aqueous phase is small. Although the processes using TBP as selective extractant are known since World-War II, economic and technical reasons motivate the research for alternative extractants. One promising approach is the use of N,N-dialkylamides which also have good extraction properties and significant advantages over TBP. The main disadvantage of N,N-dialkylamides is the viscosity of the organic phase, which increases exponentially when processes are intensified by increasing heavy metal and extractant concentration. By combining scattering and phase behavior analysis, we show that the viscosity increase with heavy metal concentration can be rationalized by the formation of a three-dimensional living network with different topologies. The mesoscopic structure consists of a one-dimensional ionic liquid of alternating metal-nitrate chains at well-defined distances embodied in a “bottlebrush” microstructure. The main component of the network are W/O cylinders in equilibrium with endcaps and junction units. The corresponding microphase distribution of endcaps, cylinders and junctions could be calculated and reasonably correlated with the viscosity for given systems. Moreover, the model was used to propose general formulation approaches for extractant systems. Giant wormlike micelles formed by anionic surfactants in aqueous solution exhibit interesting rheological properties with high impact on industrial applications, especially in home and personal care products. Their structural and consequently their viscoelastic properties change during salt addition. Usually, the addition of a few percent of salt leads to an increase of the zero-shear viscosity by several orders of magnitude up to a viscosity peak. Further salt addition leads to a decrease in viscosity which was explained by the presence of more and more branching points that serve as stress relaxation points allowing micellar chains to slide along the micellar contour. The position and intensity of the viscosity peak changes by addition of solutes or fragrances and varies also with the type of salt added. This work further shows that this general model developed can be adjusted for the aqueous case and allows to reproduce the viscosity curve of an anionic surfactant in the presence of salt with almost perfect agreement. Furthermore, the influence of the type of salt as well as of added fragrances was predicted successfully and understood.