ABSTRACT
The transportation and control of microfluidics have an important influence on the fields of biology, chemistry, and medicine. Pump systems based on the electrocapillary effect and room-temperature liquid metal droplets have attracted extensive attention. Flow rate is an important parameter that reflects the delivery performance of the pump systems. In the systems of previous studies, cylindrical structures are mostly used to constrain the droplet. The analysis and quantitative description of the influence of voltage frequency, alternating voltage, direct current voltage bias, and solution concentration on the flow rate are not yet comprehensive. Furthermore, the systems are driven by only one droplet, which limits the increase in flow rate. Therefore, a pump with a cuboid structure is designed and the droplet is bound by pillars, and the flow rate of the pump is increased by more than 200% compared with the cylindrical pump. For this structure, the mechanism of various factors on the flow rate is analyzed. To further enhance the flow rate, a pump system with multi-droplets is proposed. Moreover, the expression of flow velocity of the solution on the surface of each droplet and the relationship between the flow rate, alternating voltage, and the number of droplets are deduced. Finally, the potential of applying the multi-droplet cuboid pump system in drug delivery and analytical chemistry is demonstrated. Additionally, the core of the pump, the droplet area, is modularized, which breaks the overall structural limitations of the liquid metal pump and provides ideas for pump design.
ABSTRACT
Gallium-based liquid metal is a new class of material that has attracted extensive attention due to its excellent deformation characteristics and great potential in applications. Based on the deformation characteristics of liquid metal droplets, researchers have developed many oscillation systems composed of gallium indium tin alloy (GaInSn) droplet and graphite, or aluminum-doped gallium indium alloy (Al-GaIn24.5) droplet and iron, and so on. Rather than the oxidation and deoxidation mechanisms used in previous systems, an oscillation system that can achieve gallium indium alloy (EGaIn) droplet oscillation with the frequency of 0-29 Hz is designed depending on the interactions between the electric field, pillars, sodium hydroxide, and the droplet. The forces on the droplet are analyzed specifically, which have a great influence on droplet deformation. Additionally, the effects of factors such as voltage, the concentration of sodium hydroxide (NaOH) solution, and droplet size on the droplet oscillation are elucidated based on the force analysis, enabling the flexible control of the oscillation frequency and amplitude of the droplet. This work provides a new perspective on the design of oscillation systems and further enhances our understanding of the deformation of gallium-based liquid metal droplets.
ABSTRACT
HYPOTHESIS: Most droplets on high-efficiency condensing surfaces have radii of less than 100 µm, but conventional droplet transport methods (such as wettability-gradient surfaces and structural-curvature-gradient surfaces) that rely on the unbalanced force of three-phase lines can only transport millimeter-sized droplets efficiently. Regulating high-speed directional transport of condensate droplets is still challenging. Therefore, we present a method for condensate droplet transportation, based on the reaction force of the superhydrophobic saw-tooth surfaces to the liquid bridge, the condensate droplets could be transported at high speed and over long distances. EXPERIMENTS: The superhydrophobic saw-tooth surfaces are fabricated by femtosecond laser ablation and chemical etching. Condensation experiments and luminescent particle characterization experiments on different surfaces are conducted. Aided by the theoretical analysis, we illustrate the remarkable performance of condensate droplet transportation on saw-tooth surfaces. FINDINGS: Compared with conventional methods, our method improves the transport velocity and relative transport distance by 1-2 orders of magnitude and achieves directional transport of the smallest condensate droplet of about 2 µm. Furthermore, the superhydrophobic saw-tooth surfaces enable multi-hop directional jumping of condensate droplets, leading to cross-scale increases in transport distances from microns to decimeters.
ABSTRACT
Different demodulation methods affect the efficiency and accuracy of spatial frequency domain imaging (SFDI). A simple and effective method of sum-to-product identities (STPI) demodulation was proposed in this study. STPI requires one fewer image than conventional three-phase demodulation (TPD) at a spatial frequency. Numerical simulation and phantom experiments were performed. The result proved the feasibility of STPI and showed that STPI combined with subtraction can achieve high-precision demodulation in the low spatial frequency domain. Through extraction of phantom optical properties, STPI had similar accuracy compared with other demodulation methods in extracting optical properties in phantoms. STPI was also used to extract the optical properties of milk, and it had highly consistent results with TPD, which can distinguish milk with different fat content. The demodulation effect of this method in the low spatial frequencies is better than other fast demodulation methods.
ABSTRACT
HYPOTHESIS: Jumping of coalesced droplets on superhydrophobic surfaces (SHSs) is widely used for enhanced condensation, anti-icing/frosting, and self-cleaning due to its superior droplet transport capability. However, because only a tiny fraction (about 5%) of the released excess surface energy during coalescence can be transformed into jumping kinetic energy, the jumping is very weak, limiting its application. METHODS: We experimentally propose enhanced jumping methods, use machine learning to design structures that achieve ultimate jumping, and finally combine experiments and simulations to investigate the mechanism of the enhanced jumping. FINDING: We find that a more orderly flow inside the droplets through the structure is the key to improve energy transfer efficiency and that the egg tray-like structure enables the droplet to jump with an energy transfer efficiency 10.6 times higher than that of jumping on flat surfaces. This energy transfer efficiency is very close to the theoretical limit, i.e., almost all the released excess surface energy is transformed into jumping kinetic energy after overcoming viscous dissipation. The ultimate jumping enhances the application of water droplet jumping and enables other low surface energy fluid such as R22, R134a, Gasoline, and Ethanol, which cannot jump on a flat surface, to jump.
ABSTRACT
The jumping direction is an essential characteristic of jumping droplets, but it is poorly understood and uncontrollable at present. In this work, we present a method to control the jumping direction by surface structures, where the jumping direction is controlled by changing the inclination angle of the structure. The underlying mechanism is analyzed experimentally, with numerical simulations, and using a theoretical model developed to relate the jumping direction and the inclination angle for a few cases with a specific distribution. Because random droplet distributions are more common on actual condensation surfaces, a more comprehensive prediction model was developed based on a convolution neural network (CNN) to predict the jumping direction for more general cases. The input to the CNN is an image of droplets with various distribution features, which are detected by the neural network and used to predict the jumping angle. SHapley Additive exPlanations methods were then used to analyze the feature importance and to give human-understandable insights from the prediction model. This work offers avenues for improving cooling rates, anti-icing/freezing characteristics, and self-cleaning attributes and contributes to a better understanding of the jumping direction.
