Novel viscoelastic smart suspensions based on cationic wormlike micelles (WLMs) of erucylbis(hydroxyethyl)methylammonium chloride and oppositely charged submicron magnetite particles in the presence of added low molecular weight salt were prepared and investigated. The suspensions demonstrate remarkable stability against sedimentation, which can be due to the incorporation of particles into the network of entangled WLMs by linking to energetically unfavorable micellar end-caps. Added particles enhance significantly the viscosity, the plateau modulus, and the relaxation time of the system, acting as additional multifunctional physical cross-links in the micellar network. The increase of plateau modulus stops when the concentration of particles reaches ca. 1.5 wt %, indicating that all micellar end-caps available in the system are linked to the particles. Further addition of particles may lead just to the redistribution of micellar ends between the particles without creation of new elastically active chains. The increase of rheological characteristics by added particles is more pronounced in suspensions with a smaller content of low molecular weight salt KCl when the WLMs are shorter in length and therefore contain a larger amount of end-caps responsible for the interaction with the particles. Magnetite particles not only enhance the rheological characteristics but also impart magnetoresponsive properties to the suspension. Upon application of magnetic field, the liquidlike system transforms into a solidlike one, demonstrating a constant value of storage modulus in all frequency range and the appearance of yield stress, which is due to the formation of field-aligned chainlike aggregates of particles opposing the flow. A combination of responsive properties inherent to both the matrix and the particles makes these smart fluids very competitive with other magnetic soft matter materials for various applications.

Adsorption properties of chain fluids are of interest from both fundamental and industrial points of view. Density Functional Theory (DFT) based models are among the most appropriate techniques allowing to describe surface phenomena. At the same time Statistical Associating Fluid Theory (SAFT) successfully describes bulk PVT properties of chain-fluids. In this publication we have developed novel version of SAFT-DFT approach entitled RS-SAFT which is capable to describe adsorption of short hydrocarbons on geometrically rough surface. Major advantage of our theory is application to adsorption on natural roughs surfaces with normal and lateral heterogeneity. For this reason we have proposed workflow where surface of real solid sample is analyzed using theoretical approach developed in our previous work [1] and experimentally by means of low temperature adsorption isotherm measurements for simple fluids. As result RS-SAFT can predict adsorption properties of chain fluids taking into account geometry of the surface sample under the consideration. In order to test our workflow we have investigated hexane adsorption on carbon black with initially unknown geometry. Theoretical predictions for hexane adsorption at 303K and 293K fit corresponding experimental data well.

We propose a new theoretical approach to obtain the nanoscale morphology of rough surfaces from low-temperature adsorption experiments. Our method is based on one of the most realistic models of rough surfaces formulated in terms of random correlated processes. In our study, the adsorption on the rough surfaces is theoretically described by random surface density functional theory (RS-DFT), which allows us to take into account both the roughness in the normal direction and the correlation length of the lateral surface. Varying geometrical parameters of RS-DFT, we fit the experimental data in the low-pressure range, where the influence of the surface geometry is the most crucial. From this procedure, we obtained best-fit detailed geometry of rough surfaces, which provides full information for further atomistic modeling. Also, the developed approach allows the calculation of the surface fractal dimension from the experimental isotherms. It demonstrates that the surface fractal dimension observed in many experiments is natural for the correlated random surface model. We investigated the surface geometry of popular silica materials synthesized at different conditions. The obtained roughness parameters and fractal dimensions coincide well with the published experimental data and correctly reflects how the nanoroughness of silica materials depends on the synthesis conditions. Analysis of the best-fit specific surface area reveals the mechanism of adsorption on rough surfaces and provides a new strategy for the search of optimal storage materials.

A network of wormlike surfactant micelles with embedded magnetic particles demonstrates high magnetoresponsive linear viscoelastic properties due to tunable matrix.