Steven T. Wereley

This paper describes the effect of optical light on the generation and manipulation of microvortex flow named ‘twin opposing microvortex’ (TOMV) flow. This opto-electrohydrodynamic (OEHD) technique combines optical light, i.e. infrared (IR) laser (1064 nm), with non-uniform AC electric fields generated from a pair of indium tin oxide (ITO) electrodes. When the IR laser beam passes through the electric fields, a rapid and three-dimensional (3D) vortex flow is generated in a microchamber. When the laser beam passes through the electric fields, especially the exposed ITO electrode, the direction of the TOMV flow as well as its strength are controlled. With an AC signal of 107 kHz and various voltages below a peak-to-peak voltage of 10 V, laser power is varied up to 1.5 W and the path of a laser beam relative to the electrode (300 μm long and 16 μm wide) is manipulated. The maximum in-plane velocity outside the electrode region was obtained by micron-resolution particle image velocimetry (μPIV). When the laser beam passes through the left or right side of the lower electrode, the TOMV flow field rotates counterclockwise or clockwise, respectively. Applying optical light on an ITO electrode creates in situ and on-demand microvortex flow, which increases the feasibility of OEHD technique in various biological and chemical applications (e.g., mixing and delivering nanofluids in microfluidic devices).

This work presents a straightforward computational method to estimate the rotational diffusion coefficient (Dr) of cells and particles of various sizes using the continuum fluid mechanics theory. We calculate the torque (Γ) for cells and particles immersed in fluids to find the mobility coefficient μ and then obtain the Dr by substituting Γ in the Einstein relation. Geometries are constructed using triangular mesh, and the model is solved with computational fluid dynamics techniques. This method is less intensive and more efficient than the widely used models. We simulate eight different particle geometries and compare the results with previous literature.

Diffusion coefficient measurement is a helpful tool in revealing various properties of a fluid such as viscosity and temperature. However, determining the diffusion coefficient often requires specialized equipment. Particle-based techniques allow the use of conventional cameras to determine flow properties without any specialized measurement devices. However, the performance of existing methods such as single-particle and correlation-based measurements degrade drastically in the presence of real-world scenarios such as flow and thermal gradients. This work introduces a new method of estimating diffusion coefficient in the presence of flow and thermal gradients named deep particle diffusometry (DPD). The technique uses temporally averaged particle images as inputs and uses convolutional neural networks to predict the underlying diffusion coefficient. The results show that a high fit coefficient R ² value of 0.99 was achieved with no or known fluid flow conditions and an R ² value of 0.95 was achieved if the fluid had an arbitrary flow. Next, the generalization ability of the network was shown by training the DPD models on no gradient datasets and testing on datasets with a diffusion coefficient gradient. The networks maintained comparably high R ² values of 0.96. Next, the DPD models were tested against three conventional methods on various simulated datasets, showing their superior performance in situations where an arbitrary flow was present along with diffusion. Finally, the networks were tested on experimental data and the predictions were compared with conventional methods which resulted in R² values of 0.97 under the no-flow condition. The results show that the proposed method provides performance similar to existing methods on datasets with no flow or with a known flow and can surpass their performance on datasets that have an arbitrary flow.

Non-contact micro-manipulation tools have enabled invasion-free studies of fragile synthetic particles and biological cells. Rapid electrokinetic patterning (REP) traps target particles/cells, suspended in an electrolyte, on an electrode surface. This entrapment is electrokinetic in nature and thus depends strongly on the suspension medium’s properties. REP has been well characterized for manipulating synthetic particles suspended in low concentration salt solutions (~ 2 mS/m). However, it is not studied as extensively for manipulating biological cells, which introduces an additional level of complexity due to their limited viability in hypotonic media. In this work, we discuss challenges posed by isotonic electrolytes and suggest solutions to enable REP manipulation in bio-relevant media. Various formulations of isotonic media (salt and sugar-based) are tested for their compatibility with REP. REP manipulation is observed in low concentration salt-based media such as 0.1× phosphate buffered saline (PBS) when the device electrodes are passivated with a dielectric layer. We also show manipulation of murine pancreatic cancer cells suspended in a sugar-based (8.5% w/v sucrose and 0.3% w/v dextrose) isotonic medium. The ability to trap mammalian cells and deposit them in custom patterns enables high-impact applications such as determining their biomechanical properties and 3D bioprinting for tissue scaffolding.

Real-time viscosity measurement techniques have been used to analyze the transition of hydrogels from a liquid state to a gel state. As viscosity is inversely proportional to diffusion coefficient, measuring real-time changes in viscosity can be done through passive rheometry with the addition of tracer particles. Particle diffusometry (PD) quantifies Brownian motion of sub-micron sized fluorescent particles by computing diffusion coefficients via statistical averaging. Herein, we demonstrate a method to study changes in diffusion coefficient as a function of time using PD for a temporally and spatially resolved rheometry measurement technique. We refined the PD algorithm using synthetic images of particles suspended in a liquid undergoing a sigmoidally decreasing diffusion trend to simulate the viscosity change of the solution during gelation. Then, the technique is applied to visualize the temporal and spatial gradients of diffusion coefficient during polyacrylamide hydrogel formation experiments. This work establishes the groundwork for quantifying over time changes in Brownian motion.

Particle Diffusometry, a technology derived from particle image velocimetry, quantifies the Brownian motion of particles suspended in a quiescent solution by computing the diffusion coefficient. Particle Diffusometry has been used for pathogen detection by measuring the change in solution viscosity due to the presence of amplified DNA from a specific gene target. However, Particle Diffusometry fails to calculate accurate measurements at elevated temperatures and in the presence of fluid flow. Therefore, these two current limitations hinder the potential application where Particle Diffusometry can further be used.In this work, we expanded the usability of Particle Diffusometry to be applied to fluid samples with the simple shear flow and at various temperatures. A range of diffusion coefficient videos is created to simulate the Brownian motion of particles under flow and temperature conditions. From our updated Particle Diffusometry analysis, a correction equation is formed under three different polynomial degrees of shear flow with varying flow rates and temperatures between 25 and 65 degrees. A model experiment involving the syringe pump and rectangular channel geometry provided a uniform flow for analyzing the modified algorithm. In simulation analysis, the modified algorithm successfully computes the diffusion coefficients with ± 10% error for shear rates as large as 8 pixel/Δt on all three flow types. Complementary experiments confirm the simulation results.

Trapping, sorting, transportation, and manipulation of synthetic microparticles and biological cells enable investigations in their behavior and properties. Microfluidic techniques like rapid electrokinetic patterning (REP) provide a non‐invasive means to probe into the nature of these micro and nanoparticles. The opto‐electrically induced nature of a REP micro vortex allows tuning of the trap characteristics in real‐time. In this work, we studied the effects of transient optical heating on the induced electrothermal vortex using micro‐particle image velocimetry (μ‐PIV) and computational modeling. A near infra‐red (980 nm) laser beam was focused on a colloidal suspension of 1 μm polystyrene beads sandwiched between two parallel‐plate electrodes. The electrodes were subjected to an AC current. The laser spot was scanned back‐and‐forth in a line, at different frequencies, to create the transient vortex. This phenomenon was also studied with a computational model made using COMSOL Multiphysics. We visualize fluid flow in custom‐shaped REP traps by superposing multiple axisymmetric (spot) vortices and discuss the limitations of using superposition in dynamically changing traps.