Particle actuation by rotating magnetic fields in microchannels: a numerical study.

Magnetic particles confined in microchannels can be actuated to perform translation motion using a rotating magnetic field, but their actuation in such a situation is not yet well understood. Here, the actuation of a ferromagnetic particle confined in square microchannels is studied using immersed-boundary lattice Boltzmann simulations. In wide channels, when a sphere is positioned close to a side wall but away from channel corners, it experiences a modest hydrodynamic actuation force parallel to the channel walls. This force decreases as the sphere is shifted toward the bottom wall but the opposite trend is found when the channel is narrow. When the sphere is positioned midway between the top and bottom channel walls, the actuation force decreases as the channel width decreases and can reverse its direction. These phenomena are elucidated by studying the flow and pressure fields in the channel-particle system and by analyzing the viscous and pressure components of the hydrodynamic force acting on different parts of the sphere.

Magnetic Actuation of Surface Walkers: The Effects of Confinement and Inertia.

Driven by a magnetic field, the rotation of a particle near a wall can be rectified into a net translation. The particles thus actuated, or surface walkers, are a kind of active colloid that finds application in biology and microfluidics. Here, we investigate the motion of spherical surface walkers confined between two walls using simulations based on the immersed-boundary lattice Boltzmann method. The degree of confinement and the nature of the confining walls (slip vs no-slip) significantly affect a particle's translational speed and can even reverse its translational direction. When the rotational Reynolds number Reω is larger than 1, inertia effects reduce the critical frequency of the magnetic field, beyond which the sphere can no longer follow the external rotating field. The reduction of the critical frequency is especially pronounced when the sphere is confined near a no-slip wall. As Reω increases beyond 1, even when the sphere can still rotate in the synchronous regime, its translational Reynolds number ReT no longer increases linearly with Reω and even decreases when Reω exceeds ∼10.

Data-Driven Intelligent Manipulation of Particles in Microfluidics.

Automated manipulation of small particles using external (e.g., magnetic, electric and acoustic) fields has been an emerging technique widely used in different areas. The manipulation typically necessitates a reduced-order physical model characterizing the field-driven motion of particles in a complex environment. Such models are available only for highly idealized settings but are absent for a general scenario of particle manipulation typically involving complex nonlinear processes, which has limited its application. In this work, the authors present a data-driven architecture for controlling particles in microfluidics based on hydrodynamic manipulation. The architecture replaces the difficult-to-derive model by a generally trainable artificial neural network to describe the kinematics of particles, and subsequently identifies the optimal operations to manipulate particles. The authors successfully demonstrate a diverse set of particle manipulations in a numerically emulated microfluidic chamber, including targeted assembly of particles and subsequent navigation of the assembled cluster, simultaneous path planning for multiple particles, and steering one particle through obstacles. The approach achieves both spatial and temporal controllability of high precision for these settings. This achievement revolutionizes automated particle manipulation, showing the potential of data-driven approaches and machine learning in improving microfluidic technologies for enhanced flexibility and intelligence.

How different freezing morphologies of impacting droplets form.

HYPOTHESIS: Freezing morphologies of impacting water droplets depend on the interaction between droplet spreading and solidification. The existing studies showed that the shape of frozen droplets mostly is of spherical cap with a singular tip, because of much shorter timescale of the droplet spreading than that of the solidification. Here, we create the experimental conditions of extended droplet spreading and greatly enhanced heat transfer for fast solidification, thereby allowing to study such droplet freezing process under the strong coupling of the droplet spreading and solidification. EXPERIMENTS: We design experiments that a room-temperature water droplet impacts on a subcooled superhydrophilic surface in an enclosure chamber filled with nitrogen gas. We thoroughly investigate the freezing processes of impacting droplets under the effects of impact velocity and substrate temperature. Both the droplet impact dynamics and solidification are studied with a high-speed camera. FINDINGS: We observed five different freezing morphologies which depend on the droplet impact velocity and substrate temperature. We found that the formation of diverse morphologies results from the competitive timescales related to droplet solidification and impact hydrodynamics. We also develop a phase diagram based on scaling analysis and show how freezing morphologies are controlled by droplet impact and freezing related timescales.

Predicting Effective Diffusivity of Porous Media from Images by Deep Learning.

We report the application of machine learning methods for predicting the effective diffusivity (De) of two-dimensional porous media from images of their structures. Pore structures are built using reconstruction methods and represented as images, and their effective diffusivity is computed by lattice Boltzmann (LBM) simulations. The datasets thus generated are used to train convolutional neural network (CNN) models and evaluate their performance. The trained model predicts the effective diffusivity of porous structures with computational cost orders of magnitude lower than LBM simulations. The optimized model performs well on porous media with realistic topology, large variation of porosity (0.28-0.98), and effective diffusivity spanning more than one order of magnitude (0.1 ≲ De < 1), e.g., >95% of predicted De have truncated relative error of <10% when the true De is larger than 0.2. The CNN model provides better prediction than the empirical Bruggeman equation, especially for porous structure with small diffusivity. The relative error of CNN predictions, however, is rather high for structures with De < 0.1. To address this issue, the porosity of porous structures is encoded directly into the neural network but the performance is enhanced marginally. Further improvement, i.e., 70% of the CNN predictions for structures with true De < 0.1 have relative error <30%, is achieved by removing trapped regions and dead-end pathways using a simple algorithm. These results suggest that deep learning augmented by field knowledge can be a powerful technique for predicting the transport properties of porous media. Directions for future research of machine learning in porous media are discussed based on detailed analysis of the performance of CNN models in the present work.

Influence of Environmental Factors on the Adsorption Capacity and Thermal Conductivity of Silica Nano-Porous Materials.

In this work, the influence of temperature and humidity environment on the water vapor adsorption capacity and effective thermal conductivity of silica nano-porous material is conducted within a relative humidity range from 15% to 90% at 25 °C, 40 °C and 55 °C, respectively. The experiment results show that both the temperature and relative humidity have significant influence on the adsorption capacity and effective thermal conductivity of silica nano-porous materials. The adsorption capacity and effective thermal conductivity increase with humidity because of the increases of water vapor concentration. The effective thermal conductivity increases linearly with adsorption saturation capacity at constant temperature. Because adsorption process is exothermic reaction, the increasing temperature is not conducive to the adsorption. But the effective thermal conductivity increases with the increment of temperature at the same water uptake because of the increment of water thermal conductivity with temperature Geometric models and unit cell structure are adopted to predict the effective thermal conductivity and comparisons with the experimental result are made, and for the case of moist silica nano-porous materials with high porosity no quantitative agreement is found. It is believed that the adsorbed water will fill in the nano-pores and gap and form lots of short cuts, leading to a significant reduction of the thermal resistance.