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Microfluidic droplet generation enables the rapid and efficient production of large quantities of droplets to be used in various fields such as medical science and biology. While polydisperse droplets are inherent in bulk emulsion production, which can be potentially used for combinatorial experimentation in addition to monodisperse droplets, microfluidic chip platforms offer superior control for post‐processing applications and are better suited for integration within miniaturized systems. In this study, we present a simple yet robust method for generating droplet aggregation, which could be used for applications where the polydisperse droplets are advantageous in the context of microfluidics. This approach offers a significantly shorter timescale (in a fraction of a second) compared to existing methods in the literature. The generated droplets rely on the hydrodynamic instability of an aqueous interface within the framework of the pressure barrier principle. This approach requires adjustments to geometry and surface wettability properties, resulting in a distinct mode of droplet generation. This approach not only leads to a platform for the water‐in‐air microfluidics systems but also facilitates the integration of water‐in‐oil emulsion into microfluidic devices as a subsequent step. It was also observed that the polydisperse droplets are only generated in Glass‐PDMS chips, not PDMS‐PDMS chips, and the main channel height should be critically narrow (below 10 μm in our method) to allow the system to generate droplets. The generated droplets have diameters between 1 and 7 μm, with the majority concentrated in the 2–3 μm size band. We present a droplet generation method in microfluidic devices based on hydrodynamic instabilities at air–water interfaces enabled by pressure barrier mechanism, resulting in rapid formation of polydisperse droplets without external fields or additional manipulation. By adjusting channel geometry and surface wettability, multiple jets form and coalesce into larger droplets. The system allows transition from water‐in‐air to water‐in‐oil emulsions, offering a versatile platform for high‐throughput applications.

Foam is a medium-stable system composed of gas and liquid phases, which has the advantages of low density at the gas phase and high viscosity at the liquid phase, and has a wide application in oil and gas field development and mineral flotation, but its special medium-stable system also brings many problems in industry applications. Scientists have carried out extensive analyses and research on the foam stability and bubble-bursting mechanism, which initially clarified the rules of bubble breakage caused by environmental factors such as temperature and pressure, but the mechanism of bubble bursting under the action of internal factors such as liquid mineralization and oil concentration of the films is still not clearly defined. In this paper, we propose a compound salt-resistant foaming agent, investigated the influence of the aggregation and adsorption behavior of oil droplets on the liquid films and boundaries, and established a relevant aggregation and adsorption model with the population balance equation. We put forward a liquid film drainage mechanism based on the distribution, aggregation, and transport of oil droplets in the liquid films, so as to explain the changes in foam stability under the action of oil droplets. On the other hand, the viscoelastic analysis of foam fluid is performed with a rheometer, and the results show that in comparison with conventional power-law fluid, foam fluid has a complex rheological behavior for low shear thickening, but high shear thinning.

This study aimed to investigate gelation in glycerol monooleate (GMO)-stabilized water-in-canola oil (W/CO) emulsions by increasing water content (20–50 wt.%) and the addition of low methoxyl pectin (LMP) in the aqueous phase. A constant ratio of GMO to water was used to keep a similar droplet size in all emulsions. Hydrogenated soybean oil (7 wt.%) was used to provide network stabilization in the continuous phase. All fresh emulsions with LMP in the aqueous phase formed a stable and self-supported matrix with higher viscosity and gel strength than emulsions without LMP. Emulsion viscosity and gel strength increased with an increase in water content. All emulsions showed gel-like properties (storage moduli (G’) > loss moduli (G’’)) related to the presence of LMP in the aqueous phase and increased water content. Freeze/thaw analysis using a differential scanning calorimeter showed improved stability of the water droplets in the presence of LMP in the aqueous phase. This study demonstrated the presence of LMP in the aqueous phase, its interaction with GMO at the interface, and fat crystals in the continuous phase that could support the water droplets’ aggregation to obtain stable elastic W/CO emulsions that could be used as low-fat table spreads.

During the flow of an emulsion, droplets of the dispersed phase can deform, break up, coalesce or migrate to other regions within the flow field. Understanding these different processes is relevant to morphology development in immiscible polymer blends. Here, emulsions of castor oil in silicone oil were employed to study shear-induced coalescence alone; the conditions chosen were such that drop breakup and drop migration did not occur. A cone-and-plate device and tubes of varying length were used to examine the influence of the average shear rate, the time of shearing, concentration of the dispersed phase, and temperature on the average droplet size. It was found that the extent of “demixing” was not influenced by the spatially non-homogeneous nature of flow in a tube; results correlated very well with the average shear rate. On the other hand, coalescence was significant even when the concentration of the dispersed phase was as low as 0.5%, and it became more important as the concentration was increased. Other results were that the extent of coalescence could be promoted by lowering the shear rate. In quantitative terms, it was found that available coalescence theory gave the correct order of magnitude for the average steady-state droplet size as a function of the imposed shear rate, but the actual variation of drop size with shear rate was gentler than that predicted by theory. An unusual observation was that, under some circumstances, the droplets did not coalesce but simply stuck to each other and maintained their separate identity.

For pure ice clouds in the cold-temperature regime (T<235 K), two major formation pathways are possible. Liquid origin ice clouds stem from droplets that freeze close to water saturation. In-situ-formed ice clouds form directly from the vapor phase below water saturation. For a better investigation of these pathways, we developed a novel microphysics scheme. The new two-moment scheme distinguishes between five ice classes (“ice modes”) each with their own unique formation mechanism: homogeneous freezing of solution droplets, deposition nucleation, homogeneous freezing of cloud droplets and raindrops, immersion freezing, and secondary ice from rime splintering. The ice modes interact with each other, e.g., in competition for growth by deposition of water vapor and aggregation, but also with the other cloud particle classes, i.e., cloud droplets, rain, snow, graupel, and hail. This scheme was employed to investigate the liquid origin vs. in situ formation in the fully glaciated parts of an idealized convective cloud. The majority of the cloud ice in the deep convection cloud consisted of frozen droplets (liquid origin). This was caused by the high number concentration of cloud droplets available for freezing. In-situ-formed ice was only relevant for the overshoot where ice from both formation pathways mixed. The new scheme is also useful for investigation of the ice formation in the mixed-phase parts of the convective cloud. We find a vertical layering of ice modes in the cloud. The lowermost layer consists of secondary ice from rime splintering and occurs near the updraft core at temperatures around the Hallett–Mossop zone. At altitudes between 6 and 9 km, ice mostly stems from immersion freezing. We find a correlation between the abundance of ice from immersion freezing and snow. The majority of ice crystals above 9 km stems from homogeneously frozen cloud droplets since ice-nucleating particles (INPs) required for immersion freezing were quickly depleted.

Membraneless compartments can form in cells through liquidliquid phase separation (see the Perspective by Polymenidou). But what prevents these cellular condensates from randomly fusing together? Using the RNA-binding protein (RBP) Whi3, Langdon et al. demonstrated that the secondary structure of different RNA components determines the distinct biophysical and biological properties of the two types of condensates that Whi3 forms. Several RBPs, such as FUS and TDP43, contain prion-like domains and are linked to neurodegenerative diseases. These RBPs are usually soluble in the nucleus but can form pathological aggregates in the cytoplasm. Maharana et al. showed that local RNA concentrations determine distinct phase separation behaviors in different subcellular locations. The higher RNA concentrations in the nucleus act as a buffer to prevent phase separation of RBPs; when mislocalized to the cytoplasm, lower RNA concentrations trigger aggregation. Science , this issue p. 922 , p. 918 ; see also p. 859 High concentrations of RNA prevent pathological aggregation of RNA binding proteins in the nucleus. Prion-like RNA binding proteins (RBPs) such as TDP43 and FUS are largely soluble in the nucleus but form solid pathological aggregates when mislocalized to the cytoplasm. What keeps these proteins soluble in the nucleus and promotes aggregation in the cytoplasm is still unknown. We report here that RNA critically regulates the phase behavior of prion-like RBPs. Low RNA/protein ratios promote phase separation into liquid droplets, whereas high ratios prevent droplet formation in vitro. Reduction of nuclear RNA levels or genetic ablation of RNA binding causes excessive phase separation and the formation of cytotoxic solid-like assemblies in cells. We propose that the nucleus is a buffered system in which high RNA concentrations keep RBPs soluble. Changes in RNA levels or RNA binding abilities of RBPs cause aberrant phase transitions.

Cells are compartmentalized to allow distinct processes to occur in membrane-delimited organelles. But similar spatial restriction of cellular components in membrane-less intracellular assemblies or condensates also appears to occur—much like oil droplets in water. These compartments contribute to multiple biological processes and regulatory mechanisms. Shin and Brangwynne review the protein-protein and protein-RNA interactions that result in formation of these structures. They explain known and potential functions of such structures in a range of examples, from signaling and local control of biochemical reactants to spatial segregation. In disease, such aggregation may go awry and contribute to neurodegenerative syndromes associated with inappropriate protein aggregation. Science , this issue p. eaaf4382 Phase transitions are ubiquitous in nonliving matter, and recent discoveries have shown that they also play a key role within living cells. Intracellular liquid-liquid phase separation is thought to drive the formation of condensed liquid-like droplets of protein, RNA, and other biomolecules, which form in the absence of a delimiting membrane. Recent studies have elucidated many aspects of the molecular interactions underlying the formation of these remarkable and ubiquitous droplets and the way in which such interactions dictate their material properties, composition, and phase behavior. Here, we review these exciting developments and highlight key remaining challenges, particularly the ability of liquid condensates to both facilitate and respond to biological function and how their metastability may underlie devastating protein aggregation diseases.

Sound waves have proven to be effective in promoting the interaction and aggregation of droplets. It is necessary to theoretically study the motion of particles in a sound field to develop new acoustic technology for precipitation enhancement. In this paper, the motion of cloud droplets due to a traveling sound wave field emitted from the ground to the air is simulated using the motion equation of point particles. The force condition of the particles in the oscillating flow field is analyzed. Meanwhile, the effects of droplet size, sound frequency, and sound pressure level (SPL) on the velocity and displacement of the droplets are also investigated. The results show that Stokes force and gravity play a dominant role in the falling process of cloud droplets, and the effect of the sound wave is mainly reflected in the fluctuation of velocity and displacement, which also promotes the displacement of cloud droplets to a certain extent. The maximum displacement increments of cloud droplets of 10 µ m can reach 9200 µ m due to the action of sound waves of 50 Hz and 143.4 dB. The SPL required for a noticeable velocity fluctuation for droplets of 10 µ m with frequency of 50 Hz is 88.2 dB. When SPL < 100 dB and frequency > 500 Hz, the effect is negligible. The cloud droplet size plays a significant role in the motion, and the sound action is weaker for larger particles. For a smaller sound frequency and higher SPL, the effect of the sound wave is more prominent.

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by alpha-synuclein (αSyn) aggregation and associated with abnormalities in lipid metabolism. The accumulation of lipids in cytoplasmic organelles called lipid droplets (LDs) was observed in cellular models of PD. To investigate the pathophysiological consequences of interactions between αSyn and proteins that regulate the homeostasis of LDs, we used a transgenic Drosophila model of PD, in which human αSyn is specifically expressed in photoreceptor neurons. We first found that overexpression of the LD-coating proteins Perilipin 1 or 2 (dPlin1/2), which limit the access of lipases to LDs, markedly increased triacylglyclerol (TG) loaded LDs in neurons. However, dPlin-induced-LDs in neurons are independent of lipid anabolic (diacylglycerol acyltransferase 1/midway, fatty acid transport protein/dFatp) and catabolic (brummer TG lipase) enzymes, indicating that alternative mechanisms regulate neuronal LD homeostasis. Interestingly, the accumulation of LDs induced by various LD proteins (dPlin1, dPlin2, CG7900 or Klarsicht LD-BD ) was synergistically amplified by the co-expression of αSyn, which localized to LDs in both Drosophila photoreceptor neurons and in human neuroblastoma cells. Finally, the accumulation of LDs increased the resistance of αSyn to proteolytic digestion, a characteristic of αSyn aggregation in human neurons. We propose that αSyn cooperates with LD proteins to inhibit lipolysis and that binding of αSyn to LDs contributes to the pathogenic misfolding and aggregation of αSyn in neurons.

It was previously shown that the superdroplet algorithm for modeling the collision–coalescence process can faithfully represent mean droplet growth in turbulent clouds. An open question is how accurately the superdroplet algorithm accounts for fluctuations in the collisional aggregation process. Such fluctuations are particularly important in dilute suspensions. Even in the absence of turbulence, Poisson fluctuations of collision times in dilute suspensions may result in substantial variations in the growth process, resulting in a broad distribution of growth times to reach a certain droplet size. We quantify the accuracy of the superdroplet algorithm in describing the fluctuating growth history of a larger droplet that settles under the effect of gravity in a quiescent fluid and collides with a dilute suspension of smaller droplets that were initially randomly distributed in space (“lucky droplet model”). We assess the effect of fluctuations upon the growth history of the lucky droplet and compute the distribution of cumulative collision times. The latter is shown to be sensitive enough to detect the subtle increase of fluctuations associated with collisions between multiple lucky droplets. The superdroplet algorithm incorporates fluctuations in two distinct ways: through the random spatial distribution of superdroplets and through the Monte Carlo collision algorithm involved. Using specifically designed numerical experiments, we show that both on their own give an accurate representation of fluctuations. We conclude that the superdroplet algorithm can faithfully represent fluctuations in the coagulation of droplets driven by gravity.