Dynamics of Droplet Pinch-Off at Emulsified Oil-Water Interfaces: Interplay between Interfacial Viscoelasticity and Capillary Forces.

The presence of submicrometer structures at liquid-fluid interfaces modifies the properties of many science and technological systems by lowering the interfacial tension, creating tangential Marangoni stresses, and/or inducing surface viscoelasticity. Here we experimentally study the break-up of a liquid filament of a silica nanoparticle dispersion in a background oil phase that contains surfactant assemblies. Although self-similar power-law pinch-off is well documented for threads of Newtonian fluids, we report that when a viscoelastic layer is formed in situ at the interface, the pinch-off dynamics follows an exponential decay. Recently, such exponential neck thinning was found theoretically when surface viscous effects were taken into account. We introduce a simple approach to calculate the effective relaxation time of viscoelastic interfaces and estimate the thickness of the interfacial layer and the viscoelastic properties of liquid-fluid interfaces, where the direct measurement of interfacial rheology is not possible.

Spongy all-in-liquid materials by in-situ formation of emulsions at oil-water interfaces.

Printing a structured network of functionalized droplets in a liquid medium enables engineering collectives of living cells for functional purposes and promises enormous applications in processes ranging from energy storage to tissue engineering. Current approaches are limited to drop-by-drop printing or face limitations in reproducing the sophisticated internal features of a structured material and its interactions with the surrounding media. Here, we report a simple approach for creating stable liquid filaments of silica nanoparticle dispersions and use them as inks to print all-in-liquid materials that consist of a network of droplets. Silica nanoparticles stabilize liquid filaments at Weber numbers two orders of magnitude smaller than previously reported in liquid-liquid systems by rapidly producing a concentrated emulsion zone at the oil-water interface. We experimentally demonstrate the printed aqueous phase is emulsified in-situ; consequently, a 3D structure is achieved with flexible walls consisting of layered emulsions. The tube-like printed features have a spongy texture resembling miniaturized versions of "tube sponges" found in the oceans. A scaling analysis based on the interplay between hydrodynamics and emulsification kinetics reveals that filaments are formed when emulsions are generated and remain at the interface during the printing period. Stabilized filaments are utilized for printing liquid-based fluidic channels.

Self-assembly of highly ordered micro- and nanoparticle deposits.

The evaporation of particle-laden sessile droplets is associated with capillary-driven outward flow and leaves nonuniform coffee-ring-like particle patterns due to far-from-equilibrium effects. Traditionally, the surface energies of the drop and solid phases are tuned, or external forces are applied to suppress the coffee-ring; however, achieving a uniform and repeatable particle deposition is extremely challenging. Here, we report a simple, scalable, and noninvasive technique that yields uniform and exceptionally ordered particle deposits on a microscale surface area by placing the droplet on a near neutral-wet shadow mold attached to a hydrophilic substrate. The simplicity of the method, no external forces, and no tuning materials' physiochemical properties make the present generic approach an excellent candidate for a wide range of sensitive applications. We demonstrate the utility of this method for fabricating ordered mono- and multilayer patternable coatings, producing nanofilters with controlled pore size, and creating reproducible functionalized nanosensors.

An ensemble learning approach to digital corona virus preliminary screening from cough sounds.

This work develops a robust classifier for a COVID-19 pre-screening model from crowdsourced cough sound data. The crowdsourced cough recordings contain a variable number of coughs, with some input sound files more informative than the others. Accurate detection of COVID-19 from the sound datasets requires overcoming two main challenges (i) the variable number of coughs in each recording and (ii) the low number of COVID-positive cases compared to healthy coughs in the data. We use two open datasets of crowdsourced cough recordings and segment each cough recording into non-overlapping coughs. The segmentation enriches the original data without oversampling by splitting the original cough sound files into non-overlapping segments. Splitting the sound files enables us to increase the samples of the minority class (COVID-19) without changing the feature distribution of the COVID-19 samples resulted from applying oversampling techniques. Each cough sound segment is transformed into six image representations for further analyses. We conduct extensive experiments with shallow machine learning, Convolutional Neural Network (CNN), and pre-trained CNN models. The results of our models were compared to other recently published papers that apply machine learning to cough sound data for COVID-19 detection. Our method demonstrated a high performance using an ensemble model on the testing dataset with area under receiver operating characteristics curve = 0.77, precision = 0.80, recall = 0.71, F1 measure = 0.75, and Kappa = 0.53. The results show an improvement in the prediction accuracy of our COVID-19 pre-screening model compared to the other models.

Spontaneous Formation of Double Emulsions at Particle-Laden Interfaces.

HYPOTHESIS: Traditionally, double emulsions are produced in the presence of both oil-soluble and water-soluble surfactants in sequential droplet formation settings or unique fluidic designs. Micelles, assemblies of surfactants in liquid mediums, can generate single emulsion droplets without requiring input energy. We hypothesize that the synergy between nanoparticles in one phase, and micelles in the other phase can spontaneously generate double emulsions. Nanoparticles can become surface-activated by adsorbing surfactants and form the second type of emulsions from the initially emulsified phase by micelles. EXPERIMENTS: We design a thermodynamically-driven emulsification platform where double emulsions are spontaneously formed as soon an aqueous nanoparticle dispersion is placed in contact with an oleic micellar solution. Confocal and cryogenic-scanning electron microscopies are utilized to characterize structure and intensity of emulsions at various concentrations of silica nanoparticle and Span micelles. The rate of particle surface activation and emulsification and the amount of water intake are quantified using dynamic light scattering, dynamic interfacial tension, and density measurements. FINDINGS: Nanoscale water droplets nucleate in the oil in form of swollen micelles. Over time, nanoparticles form a water-shell encapsulating the swollen-micelle rich oil phase. The gradual surfaceactivation of nanoparticles is key in self-double emulsification and controlling the emulsion intensity. We build on this new discovery and design a novel system for double emulsification. Incorporating nanoparticles into spontaneous emulsification systems opens novel routes for designing emulsion-based materials.

Functionalized multiscale visual models to unravel flow and transport physics in porous structures.

The fluid flow, species transport, and chemical reactions in geological formations are the chief mechanisms in engineering the exploitation of fossil fuels and geothermal energy, the geological storage of carbon dioxide (CO2), and the disposal of hazardous materials. Porous rock is characterized by a wide surface area, where the physicochemical fluid-solid interactions dominate the multiphase flow behavior. A variety of visual models with differences in dimensions, patterns, surface properties, and fabrication techniques have been widely utilized to simulate and directly visualize such interactions in porous media. This review discusses the six categories of visual models used in geological flow applications, including packed beds, Hele-Shaw cells, synthesized microchips (also known as microfluidic chips or micromodels), geomaterial-dominated microchips, three-dimensional (3D) microchips, and nanofluidics. For each category, critical technical points (such as surface chemistry and geometry) and practical applications are summarized. Finally, we discuss opportunities and provide a framework for the development of custom-built visual models.

Noncontact and Nonintrusive Microwave-Microfluidic Flow Sensor for Energy and Biomedical Engineering.

A novel flow sensor is presented to measure the flow rate within microchannels in a real-time, noncontact and nonintrusive manner. The microfluidic device is made of a fluidic microchannel sealed with a thin polymer layer interfacing the fluidics and microwave electronics. Deformation of the thin circular membrane alters the permittivity and conductivity over the sensitive zone of the microwave resonator device and enables high-resolution detection of flow rate in microfluidic channels using non-contact microwave as a standalone system. The flow sensor has the linear response in the range of 0-150 µl/min for the optimal sensor performance. The highest sensitivity is detected to be 0.5 µl/min for the membrane with the diameter of 3 mm and the thickness of 100 µm. The sensor is reproducible with the error of 0.1% for the flow rate of 10 µl/min. Furthermore, the sensor functioned very stable for 20 hrs performance within the cell culture incubator in 37 °C and 5% CO2 environment for detecting the flow rate of the culture medium. This sensor does not need any contact with the liquid and is highly compatible with several applications in energy and biomedical engineering, and particularly for microfluidic-based lab-on-chips, micro-bioreactors and organ-on-chips platforms.