Deep-Learning Based Estimation of Dielectrophoretic Force

The ability to accurately quantify dielectrophoretic (DEP) force is critical in the development of high-efficiency microfluidic systems. This is the first reported work that combines a textile electrode-based DEP sensing system with deep learning in order to estimate the DEP forces invoked on microparticles. We demonstrate how our deep learning model can process micrographs of pearl chains of polystyrene (PS) microbeads to estimate the DEP forces experienced. Numerous images obtained from our experiments at varying input voltages were preprocessed and used to train three deep convolutional neural networks, namely AlexNet, MobileNetV2, and VGG19. The performances of all the models was tested for their validation accuracies. Models were also tested with adversarial images to evaluate performance in terms of classification accuracy and resilience as a result of noise, image blur, and contrast changes. The results indicated that our method is robust under unfavorable real-world settings, demonstrating that it can be used for the direct estimation of dielectrophoretic force in point-of-care settings.

Mediation of lubricated air films using spatially periodic dielectrophoretic effect

A stone thrown in a lake captures air as it collides with water and sinks; likewise a rain drop falling on a flat surface traps air bubbles underneath and creates a spectacular splash. These natural occurrences, from bubble entrapment to liquid ejection, happen as air fails to escape from the closing gap between liquid and solid surfaces. Trapping of air is devastating for casting, coating, painting, and printing industries, or those intolerant of water entry noise. Attempts to eliminate the interfering air rely on either reducing the ambient pressure or modifying the solid surfaces. The former approach is inflexible in its implementation, while the latter one is inherently limited by the wetting speed of liquid or the draining capacity of air passages created on the solid. Here, we present a “divide and conquer” approach to split the thin air gap into tunnels and subsequently squeeze air out from the tunnels against its viscous resistance using spatially periodic dielectrophoretic force. We confirm the working principles by demonstrating suppression of both bubble entrapment and splash upon impacts of droplets on solid surfaces.

Heat transport in turbulent electro-hydrodynamics

<p>Electro-hydrodynamics (EHD) plays an important role in many industrial applications. Ink-jet printers, microwave drying facilities, and lab-on-a-chip devices utilize dielectric properties of the working fluids and their manipulation without moving parts. Another application is found in heat exchangers, where the dielectrophoretic force is used to increase heat transfer due to thermal buoyancy. In particular, the dielectrophoretic force has great advantages in low- and micro-gravity conditions since the force can be used to mimic a gravitational force field. Hence, convection in a rectangular cavity induced by EHD is a model system comparable to Rayleigh-Benard (RB) convection. However, the electric-driven buoyancy term and dielectric heating make the system more complex. The direction of the triggering dielectrophoretic force depends mainly on the temperature gradient which can be used to manipulate the heat flux or the entire structure of the convective flow. Layer formation, comparable to double-diffusive convection, and convective overshooting are two representative cases which can be established by EHD, too. We will present first results of turbulent convection induced by EHD in the rectangular cavity and the impact of volumetric heating. For this purpose, the Boussinesq approximation, as well as compressible models for EHD, are tested also for applicability in geophysical relevant regimes. This is a crucial point as the limitations by incompressible modeling are not well-understood for EHD. The main focus of the study is the analysis of the heat flux as a function of the thermo-electric feedback and the dielectric heating rate. Convective overshooting and layer formation are examined closely. Results of this study are used to estimate transport properties and time scales. Applications using EHD are the GeoFlow and AtmoFlow experiments. The GeoFlow experiment was conducted several years on the International Space Station and gained deep insight into EHD driven convection in the spherical shell geometry. The AtmoFlow experiment is under construction and is planned for operations on the ISS in 2024. This experiment is designed to study atmospheric like flows in the spherical shell.</p>

Screening for cerebral amyloid angiopathy based on serological biomarkers analysis using a dielectrophoretic force-driven biosensor platform

Screening of cerebral amyloid angiopathy and Alzheimer's disease by analyzing plasma amyloid-β using a highly sensitive dielectrophoretic force-driven biosensor platform.