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Abstract Double emulsions with core‐shell structures are versatile materials used in applications such as cell culture, drug delivery, and materials synthesis. A droplet library with precisely controlled dimensions and properties would streamline screening and optimization for specific applications. While microfluidic droplet generation offers high precision, it is typically labor‐intensive and sensitive to disturbances, requiring continuous operator intervention. To address these limitations, we present an artificial intelligence (AI)‐empowered automated double emulsion droplet library generator. This system integrates a convolutional neural network (CNN)‐based object detection model, decision‐making, and feedback control algorithms to automate droplet generation and collection. The system monitors droplet generation every 171 ms—faster than a Formula 1 driver's reaction time—ensuring rapid response to disturbances and consistent production of single‐core double emulsions. It autonomously generates libraries of 25 distinct monodisperse droplets with user‐defined properties. This automation reduces labor and waste, enhances precision, and supports rapid and reliable droplet library generation. We anticipate that this platform will accelerate discovery and optimization in biomedical, biological, and materials research. 

We developed ultra-high energy storage density capacitors using a new class of lead-free bismuth pyrochlorebased dielectric film material systems with high breakdown strength and reliability. The 2 μm-thick pyrochlore ceramic film capacitors have demonstrated ultra-high energy densities around 90 J/cm3 with very low energy loss below 3%, which is achieved by the combination of high permittivity, pseudo-linear dielectric characteristics, and high breakdown electric field over 4.5 MV/cm. Particularly, these pyrochlore ceramic films can endure voltage strength up to ~900 V. These noteworthy pyrochlore ceramic films are fabricated by the lowcost chemical solution deposition process which allows dielectric films to be processed on standard platinized silicon wafers. This new class of capacitors can satisfy the emergent needs for significant reduction in size and weight of capacitors with high energy storage capability in power electronics, electric vehicles, and energy storage in sustainable energy systems. Our research provides a unique and economical platform for the processing of this useful pyrochlore material in large volume for eco-friendly energy applications. 

Extensive research is focused on the development of highly sensitive, rapid on-site diagnostic devices. The lateral flow strip (LFS) is a paper-based point-of-care diagnostic device, which is highly promising because of its ease of use and low cost. Despite these advantages, LFS device is still less popular than other methods such as enzyme-linked immunosorbent assay (ELISA) or real-time polymerase chain reaction (qPCR) due to its low sensitivity. Here, we have developed a fluorescence-based lateral flow strip (f-LFS) device for DNA detection using a molecular beacon (MB), a short hairpin-forming DNA strand tagged with a fluorophore-quencher pair. Each paper and membrane component of f-LFS device was carefully selected based on their physicochemical properties including porosity, surface functionality, and autofluorescence. The limit of detection (LOD) of this device was substantially improved to 2.1 fg/mL by adding MgCl 2 to the reaction buffer and narrowing the test membrane dimension. Also, a portable fluorescence detection system for f-LFS was developed using a multi-pixel photon counter (MPPC), a sensitive detector detecting the signal on site. We anticipate that this highly sensitive paper-based diagnostic device can be utilized for on-site diagnosis of various diseases. 

We report an experimental investigation of pressure-driven flow of a viscous liquid across thin polydimethylsiloxane (PDMS) membranes. Our experiments revealed a nonlinear relation between the flow rate $$Q$$ and the applied pressure drop $$\unicode[STIX]{x0394}p$$ , in apparent disagreement with Darcy’s law, which dictates a linear relationship between flow rate, or average velocity, and pressure drop. These observations suggest that the effective permeability of the membrane decreases with pressure due to deformation of the nanochannels in the PDMS polymeric network. We propose a model that incorporates the effects of pressure-induced deformation of the hyperelastic porous membrane at three distinct scales: the membrane surface area, which increases with pressure, the membrane thickness, which decreases with pressure, and the structure of the porous material, which is deformed at the nanoscale. With this model, we are able to rationalize the deviation between Darcy’s law and the data. Our result represents a novel case in which macroscopic deformations can impact the microstructure and transport properties of soft materials.