Properties of emulsions highly depend on the interdroplet interactions and, thus, engineering interdroplet interactions at molecular scale are essential to achieve desired emulsion systems. Here, attractive Pickering emulsion gels (APEGs) are designed and prepared by bridging neighboring particle-stabilized droplets via telechelic polymers. In the APEGs, each telechelic molecule with two amino end groups can simultaneously bind to two carboxyl functionalized nanoparticles in two neighboring droplets, forming a bridged network. The APEG systems show typical shear-thinning behaviors and their viscoelastic properties are tunable by temperature, pH, and molecular weight of the telechelic polymers, making them ideal for direct 3D printing. The APEGs can be photopolymerized to prepare APEG-templated porous materials and their microstructures can be tailored to optimize their performances, making the APEG systems promising for a wide range of applications.
Pickering emulsions are emulsions stabilized by colloidal surfactants, i.e. solid particles. Compared with traditional molecular surfactant-stabilized emulsions, Pickering emulsions show many advantages, such as high resistance to coalescence, long-term stability, good biocompatibility and tunable properties. In recent years, Pickering emulsions are widely applied in scientific researches and industrial applications. In this review, we focus on the influences of particle properties on Pickering emulsions, including particle amphiphilicity, concentration, size and shape, and summarize the strategies developed for the preparation of amphiphilic Janus particles. The applications of Pickering emulsions in food industry, cosmetic industry, material science, drug delivery, biomedical research and vaccine adjuvant will also be covered. Pickering emulsions are a unique system for multi-disciplinary studies and will become more and more important in the future.
Fluorosurfactants have expanded the landscape of high-value biochemical assays in microfluidic droplets, but little is known about how the spatial geometries and polarity of the head group contribute to the performance of fluorosurfactants. To decouple this, we design, synthesize, and characterize two linear and two dendritic glycerol- or tris-based surfactants with a common perfluoropolyether tail. To reveal the influence of spatial geometry, we choose inter-droplet cargo transport as a stringent test case. Using surfactants with linear di- and triglycerol, we show that the inter-droplet cargo transport is minimal compared with their dendritic counterparts. When we encapsulated a less-leaky sodium fluorescent dye into the droplets, quantitatively, we find that the mean fluorescence intensity of the PFPE-dTG stabilized PBS-only droplets after 72 h was ∼3 times that of the signal detected in PBS-only droplets stabilized by PFPE-lTG. We also demonstrate that the post-functionalization of PFPE-lTG having a linear geometry and four hydroxy groups enables the ‘from-Droplet’ fishing of the biotin–streptavidin protein complex without the trade-off between fishing efficiency and droplet stability. Thus, our approach to design user-friendly surfactants reveals the aspects of spatial geometry and facile tunability of the polar head groups that have not been captured or exploited before.
Quantifying the viscosity of a fluid is of great importance in determining its properties and can even be used to identify what the fluid is. While many techniques exist for measuring the viscosity of either gases or liquids, it is very challenging to probe both gases and liquids with a single approach because of the significant difference in their nature, and the vast difference in the values of their viscosities. We introduce a facile approach to measuring the viscosity of a Newtonian fluid, either a gas or a liquid, by flowing it through a deformable microchannel where the deformation depends on the pressure required to induce the flow, which, in turn, depends on the fluid viscosity. A strain gauge embedded just above and across the microchannel transduces the flow-induced deformation into strain. The strain is proportional to the square of the flow-induced deformation enabling us to precisely discriminate not only gases but also liquids based on their viscosities with the same device.
A general strategy to carry out cell uptake and biodistribution studies is to label nanoparticles (NPs) with a fluorescent dye. However, the comparative study of different dye‐loaded NPs remains difficult owing to uncontrolled dye quenching and de‐quenching. Here we compared two types of dye‐labeled NPs and demonstrated their distinct properties. NPs with dye molecules at a solid state suffer from dye quenching, so the dye release and/or NP degradation in biological environments leads to a several‐fold increase of fluorescence intensity despite the same amount of NPs, owing to the state switch from quenching to de‐quenching. In contrast, NPs with dye molecules at a soluble state exhibit no quenching effect. To standardize the comparative study, we propose two possible solutions: using lower dye loading or using medium analysis for quantifying cell uptake of NPs. This work provides valuable insights into selecting valid quantification methods for bio‐nano studies.
Circulating extracellular vesicles (EVs)—biological nanomaterials shed from most mammalian cells—have emerged as promising biomarkers, drug delivery vesicles, and treatment modulators. While different types of vesicles are being explored for these applications, it is becoming clear that human EVs are quite heterogeneous even in homogeneous or monoclonal cell populations. Since it is the surface EV protein composition that will largely dictate their biological behavior, high-throughput single EV profiling methods are needed to better define EV subpopulations. Here, we present an antibody-based immunosequencing method that allows multiplexed measurement of protein molecules from individual nanometer-sized EVs. We use droplet microfluidics to compartmentalize and barcode individual EVs. The barcodes/antibody-DNA are then sequenced to determine protein composition. Using this highly sensitive technology, we detected specific proteins at the single EV level. We expect that this technology can be further adapted for multiplexed protein analysis of any nanoparticle.
Fibrin is the main component of blood clots. The mechanical properties of fibrin are therefore of critical importance in successful hemostasis. One of the divalent cations released by platelets during hemostasis is Zn2+; however, its effect on the network structure of fibrin gels and on the resultant mechanical properties remains poorly understood. Here, by combining mechanical measurements with three-dimensional confocal microscopy imaging, we show that Zn2+ can tune the fibrin network structure and alter its mechanical properties. In the presence of Zn2+, fibrin protofibrils form large bundles that cause a coarsening of the fibrin network due to an increase in fiber diameter and reduction of the total fiber length. We further show that the protofibrils in these bundles are loosely coupled to one another, which results in a decrease of the elastic modulus with increasing Zn2+ concentrations. We explore the elastic properties of these networks at both low and high stress: At low stress, the elasticity originates from pulling the thermal slack out of the network, and this is consistent with the thermal bending of the fibers. By contrast, at high stress, the elasticity exhibits a common master curve consistent with the stretching of individual protofibrils. These results show that the mechanics of a fibrin network are closely correlated with its microscopic structure and inform our understanding of the structure and physical mechanisms leading to defective or excessive clot stiffness.
BK virus (BKV) is a human polyomavirus that is generally harmless but can cause devastating disease in immunosuppressed individuals. BKV infection of renal cells is a common problem for kidney transplant patients undergoing immunosuppressive therapy. In cultured primary human renal proximal tubule epithelial (RPTE) cells, BKV undergoes a productive infection. The BKV-encoded large T antigen (LT) induces cell cycle entry, resulting in the upregulation of numerous genes associated with cell proliferation. Consistently, microarray and transcriptome sequencing (RNA-seq) experiments performed on bulk infected cell populations identified several proliferation-related pathways that are upregulated by BKV. These studies revealed few genes that are downregulated. In this study, we analyzed viral and cellular transcripts in single mock- or BKV-infected cells. We found that the levels of viral mRNAs vary widely among infected cells, resulting in different levels of LT and viral capsid protein expression. Cells expressing the highest levels of viral transcripts account for approximately 20% of the culture and have a gene expression pattern that is distinct from that of cells expressing lower levels of viral mRNAs. Surprisingly, cells expressing low levels of viral mRNA do not progress with time to high expression, suggesting that the two cellular responses are determined prior to or shortly following infection. Finally, comparison of cellular gene expression patterns of cells expressing high levels of viral mRNA with those of mock-infected cells or cells expressing low levels of viral mRNA revealed previously unidentified pathways that are downregulated by BKV. Among these are pathways associated with drug metabolism and detoxification, tumor necrosis factor (TNF) signaling, energy metabolism, and translation.
Knowing the location of sweet spots benefits the horizontal well drilling and the selection of perforation clusters. Generally, geoscientists determine sweet spots from the well-logging interpretation. In this paper, a group of prevalent classifiers [extreme gradient boosting (XGBoost), unbiased boosting with categorical features (CatBoost), and light gradient boosting machine (LightGBM)] based on gradient-boosting decision trees (GBDTs) are introduced to automatically determine sweet spots based on well-log data sets. Compared with linear support vector machines (SVMs), these robust algorithms can deal with comparative scales of features and learn nonlinear decision boundaries. Moreover, they are less influenced by the presence of outliers. Another prevailing approach, named generative adversarial networks (GANs), is implemented to augment the training data set by using a small number of training samples. An extensive application has been built for the field cases in a certain oilfield. We randomly select 73 horizontal wells for training, and 13 features are chosen from well-log data sets. Compared with conventional SVMs, the agreement rates of interpretation by XGBoost and CatBoost are significantly improved. Without special preprocessing of the input data sets and conditional tabular GAN (CTGAN) model fine tuning, the fake data set could still bring a relatively low agreement rate for all detections. Finally, we propose an ensemble-learning framework concatenating multilevels of classifiers and improve agreement rate. In this paper, we illustrate a new tool for categorizing the reservoir quality by using GBDTs and ensemble models, which further helps search and identify sweet spots automatically. This tool enables us to integrate experts’ knowledge to the developed model, identify logging curves more efficiently, and cover more sweet spots during the drilling and completion treatment, which immensely decrease the cost of log interpretation.
Polymeric microcapsules with shells containing homogeneous pores with uniform diameter on the nanometer scale are reported. The mesoporous microcapsules are obtained from confined self-assembly of amphiphilic block copolymers with a selective porogen in the shell of water-in-oil-in-water double emulsion drops. The use of double emulsion drops as a liquid template enables the formation of homogeneous capsules of 100s of microns in diameter, with aqueous cores encapsulated in a shell membrane with a tunable thickness of 100s of nanometers to 10s of microns. Microcapsules with shells that exhibit an ordered gyroidal morphology and three-dimensionally connected mesopores are obtained from the triblock terpolymer poly(isoprene)-block-poly(styrene)-block-poly(4-vinylpyridine) coassembled with pentadecylphenol as a porogen. The bicontinuous shell morphology yields nanoporous paths connecting the inside to the outside of the microcapsule after porogen removal; by contrast, one-dimensional hexagonally packed cylindrical pores, obtained from a traditional diblock copolymer system with parallel alignment to the surface, would block transport through the shell. To enable the mesoporous microcapsules to withstand harsh conditions, such as exposure to organic solvents, without rupture of the shell, we develop a cross-linking method of the nanostructured triblock terpolymer shell after its self-assembly. The microcapsules exhibit pH-responsive permeability to polymeric solutes, demonstrating their potential as a filtration medium for actively tunable macromolecular separation and purification. Furthermore, we report a tunable dual-phase separation method to fabricate microcapsules with hierarchically porous shells that exhibit ordered mesoporous membrane walls within sponge-like micron-sized macropores to further control shell permeability.
Understanding the fluid-structure interaction is crucial for an optimal design and manufacturing of soft mesoscale materials. Multi-core emulsions are a class of soft fluids assembled from cluster configurations of deformable oil-water double droplets (cores), often employed as building-blocks for the realisation of devices of interest in bio-technology, such as drug-delivery, tissue engineering and regenerative medicine. Here, we study the physics of multi-core emulsions flowing in microfluidic channels and report numerical evidence of a surprisingly rich variety of driven non-equilibrium states (NES), whose formation is caused by a dipolar fluid vortex triggered by the sheared structure of the flow carrier within the microchannel. The observed dynamic regimes range from long-lived NES at low core-area fraction, characterised by a planetary-like motion of the internal drops, to short-lived ones at high core-area fraction, in which a pre-chaotic motion results from multi-body collisions of inner drops, as combined with self-consistent hydrodynamic interactions. The onset of pre-chaotic behavior is marked by transitions of the cores from one vortex to another, a process that we interpret as manifestations of the system to maximize its entropy by filling voids, as they arise dynamically within the capsule.