Current advances in liquid–liquid mixing in static mixers: A review

Chaotic Transport of Solutes in Unsaturated Porous Media

Unsaturated porous media, characterized by the combined presence of several immiscible fluid phases in the pore space, are highly relevant systems in nature, because they control the fate of contaminants and the availability of nutrients in the subsoil. However, a full understanding of the mechanisms controlling solute mixing in such systems is still missing. In particular, the role of saturation in the development of chaotic solute mixing has remained unexplored. Using three-dimensional numerical simulations of flow and transport at the pore scale, built upon X-ray tomograms of a porous medium at different degrees of liquid (wetting)-phase saturation, we show the occurrence of chaotic dynamics in both the deformation of the solute plume, as characterized by computed chaos metrics (Lyapunov exponents), and the mixing of the injected solute. Our results show an enhancement of these chaotic dynamics at lower saturation and their occurrence even under diffusion-relevant conditions over the medium's length, also being strengthened by larger flow velocities. These findings highlight the dominant role of the pore-scale spatial heterogeneity of the system, enhanced by the presence of an immiscible phase (e.g., air), on the mixing efficiency. This represents a stepping stone for the assessment of mixing and reactions in unsaturated porous media.

Measuring the Shape, Stiffness, and Interface Tension of Droplets with the Scanning Ion Conductance Microscope

Imaging and probing liquid-liquid interfaces at the micro- and nanoscale are of high relevance, for example, in materials science, surface chemistry, and microfluidics. However, existing imaging techniques are limited in resolution, average over large sample areas, or interact with the sample. Here, we present a method to quantify the shape, stiffness, and interface tension of liquid droplets with the scanning ion conductance microscope (SICM), providing submicrometer resolution and the ability to perform noncontact mechanical measurements. We show that we can accurately image the three-dimensional shape of micrometer-sized liquid droplets made of, for example, decane, hexane, or different oils. We then introduce numerical models to quantitatively obtain their stiffness and interface tension from SICM data. We verified our method by measuring the interface tension of decane droplets changing under the influence of surfactants at different concentrations. Finally, we use SICM to resolve the dissolution dynamics of decane droplets, showing that droplet shape exhibits different dissolution modes and stiffness continuously increases while the interface tension remains constant. We thereby demonstrate that SICM is a useful method to investigate liquid-liquid interfaces on the microscale with applications in materials or life sciences.

Liquid-Liquid Dispersion Performance Prediction and Uncertainty Quantification Using Recurrent Neural Networks

We demonstrate the application of a recurrent neural network (RNN) to perform multistep and multivariate time-series performance predictions for stirred and static mixers as exemplars of complex multiphase systems. We employ two network architectures in this study, fitted with either long short-term memory and gated recurrent unit cells, which are trained on high-fidelity, three-dimensional, computational fluid dynamics simulations of the mixer performance, in the presence and absence of surfactants, in terms of drop size distributions and interfacial areas as a function of system parameters; these include physicochemical properties, mixer geometry, and operating conditions. Our results demonstrate that while it is possible to train RNNs with a single fully connected layer more efficiently than with an encoder-decoder structure, the latter is shown to be more capable of learning long-term dynamics underlying dispersion metrics. Details of the methodology are presented, which include data preprocessing, RNN model exploration, and methods for model performance visualization; an ensemble-based procedure is also introduced to provide a measure of the model uncertainty. The workflow is designed to be generic and can be deployed to make predictions in other industrial applications with similar time-series data.