Session J19: Microscale and Nanoscale Flows: Theory, Particles, Drops, Bubbles

Three-Dimensional Stress-Measurement Using Flow Birefringence: Revisiting the Stress-Optic Law

We have developed a novel three-dimensional stress-measurement system. This study systematically investigates the flow birefringence of cellulose nanocrystal (CNC) suspensions to clarify the importance of the stress component along the camera's optical axis in the stress-optic law (SOL). SOL describes the relationship between birefringence, the retardation of transmitted polarized light, and the stress field. Over 100 datasets on the retardation of CNC suspensions (concentrations of 0.1, 0.3, 0.5, and 1.0 wt%) in a laminar flow within a rectangular channel (aspect ratios of 0.1, 1, and 3) were systematically obtained. The measured retardation data were compared with predictions from the conventional SOL excluding the stress component along the camera's optical axis and from the SOL including these components as second-order terms (2nd-order SOL). The results show that the 2nd-order SOL agrees significantly better with the measurements. Based on the 2nd-order SOL, the retardation at the center of the channel, where the effect of the stress component along the camera's optical axis is most pronounced, is predicted to be proportional to the square of the flow rate, matching the experimental data. These results confirm the importance of considering the stress component along the camera's optical axis in the flow birefringence of CNC suspensions at high flow rates, even for quasi-two-dimensional channel flow.   

Dynamics of a Quincke-rotating colloid near a planar electrode

The Quincke effect is an electrohydrodynamic instability that gives rise to an electric torque on a dielectric particle in a uniform DC electric field above a critical field. In free space, a Hopf bifurcation at higher field strength results in unsteady, chaotic rotation due to particle inertia.

The presence of a nearby electrode couples rotation and translation. After the first bifurcation, the torque on a spherical colloid results in the particle steady rolling. However, we find that at high field strengths the chaotic dynamics is suppressed, and instead periodic, oscillatory back-and-forth rolling takes place. We model the Quincke effect when  higher order multipoles (due to the images) are present, and analyze the effect of particle-electrode gap and particle inertia. The bifurcation diagram quantifies the transition from the steady rolling state to chaotic and periodic oscillations, and illustratse the effect of the particle-electrode gap on the periodic orbits.    

Convolutional Neural Networks for Particle Diffusometry Measurements in the Presence of Flow and with Defocused Particles

Diffusion is a natural phenomenon in fluids. Its measurement can be done optically by seeding an otherwise featureless fluid with tracer particles and observing their motion using a microscope. However, existing particle-based diffusion coefficient measurement algorithms have multiple failure modes, especially when the fluid has a flow, or the particles are defocused. This work uses Convolutional Neural Networks (CNNs) as an alternative for predicting diffusion coefficients using PIV-styled crops in the presence of these real-world effects. The networks were trained, validated, and tested on datasets with Gaussian-shaped or defocused particles under an arbitrary flow condition. The results show that the CNNs have a low Mean Absolute Error (MAE) of 0.09 um²/s and 0.07 um²/s between the true and predicted diffusion coefficient values for the dataset with Gaussian-shaped and defocused particles respectively. The performance of the CNNs was also benchmarked against four conventional algorithms on the simulated datasets. The results show that the CNNs outperform conventional methods when the particles are defocused. Finally, the outputs of CNNs were compared against the outputs of conventional algorithms on experimental datasets, leading to uncertainty in the range of 0.19 um²/s - 0.47 um²/s. Hence, the study utilized CNNs to reliably predict diffusion coefficients from complex particle datasets where the conventional algorithms fail.   

Thermoviscous flows for microfluidic manipulation

Recent microfluidic experiments have explored the precise positioning of micron-sized particles via laser-induced thermoviscous flow. From micro-robotics to biology at the subcellular scale, applications of this versatile technique have been realised in a wide range of disciplines. Through the interplay between thermal expansion and thermal viscosity changes, the repeated scanning of the laser along a scan path results in fluid flow and hence net transport. In microfluidic settings, geometry has a significant influence on the flow induced by the focused light. Achieving high-precision microfluidic manipulation of particles in complex environments therefore requires innovative design of laser scan patterns, along with quantitative theoretical understanding. Here we present an analytical, theoretical model for the flow induced by arbitrary scan patterns in complex geometries, showing excellent agreement with new experiments. Our results will enable refined control over particles at the microscale in complex geometries, as well as new studies probing the role of physical transport in living cells experimentally.   

Out of Equilibrium, But Not Out of Mind: Rapid Estimation of Fluid Transport Coefficients in Molecular Simulations Away from Thermodynamic Equilibrium

Molecular-dynamics (MD) simulation is a widely used method for the study of fluids at the molecular scale. MD simulations are frequently used to predict, and understand fundamental mechanisms underlying, important fluid transport properties. But MD simulations never directly supply these (or in fact any) material properties; the only data directly supplied by an MD simulation are particle kinematics. A significant body of theory has been developed to convert particle kinematics into material properties of interest; as one example (of many), Green-Kubo theory can be used to convert particle velocities into a diffusion coefficient. All this well-established theory is formally valid in the infinite-time/infinite-statistics limit; in practice, one can only simulate a finite number of particles for a finite amount of time. In this talk, we investigate optimal strategies for estimating fluid transport coefficients in molecular simulations in this pre-asymptotic regime, with a focus on systems out of thermodynamic equilibrium. We show that a class of estimators, based upon excess entropy scaling, perform well in a range of practical (and even in some cases exotic) out-of-equilibrium conditions. We validate our results with MD simulations of a variety of simple and not-so-simple liquids.