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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. 

This study investigates Marangoni effect-induced structural changes in spin-coated polymer nanocomposite (PNC) films composed of poly(methyl methacrylate)-grafted silica nanoparticles (NPs) and poly(styrene-ran-acrylonitrile). Films cast from methyl isobutyl ketone (MIBK) solvent exhibit distinct hexagonal honeycomb cells with thickness gradients driven by surface tension variations. Atomic force microscopy reveals protruded ridges and junctions at cell intersections, where NP concentration is the highest. Upon annealing at 155 degrees C, NPs segregate to the surface due to their lower surface energy, and the initially protruding features flatten and eventually form depressed channels while maintaining higher NP density than surrounding areas. Time-of-flight secondary ion mass spectrometry corroborated these findings, highlighting enhanced surface segregation of NPs in MIBK films. These defects can be eliminated using methyl isoamyl ketone (MIAK) as a solvent that produces homogeneous films of uniform thickness. This study highlights the impact of the Marangoni effect on the microstructure and surface properties of PNC films, providing insights for enhancing film quality and performance. 

This study examines nanoparticle diffusion in crowded polymer nanocomposites by diffusing small Al2O3 nanoparticles (NPs) in SiO2-loaded P2VP matrices. Time-of-flight secondary ion mass spectroscopy (ToF-SIMS) measures Al2O3 NP diffusion coefficients within a homogeneous PNC background of larger, immobile SiO2 NPs. By developing a geometric model for the average interparticle distance in a system with two NP sizes, we quantify nanocomposite confinement relative to the Al2O3 NP size with a bound layer. At low SiO2 concentrations, Al2O3 NP diffusion aligns with the neat polymer results. In more crowded nanocomposites with higher SiO2 concentrations where the interparticle distance approaches the size of the mobile Al2O3 NP, the 6.5 nm Al2O3 NPs diffuse faster than predicted by both core–shell and vehicular diffusion models. Relative to our previous studies of NPs diffusing into polymers, these findings demonstrate that the local environment in crowded systems significantly complicates NP diffusion behavior and the bound layer lifetimes.