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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. 

Polymer infiltration is studied in a bicontinuous, nanoporous gold (NPG) scaffold. For poly(2-vinylpyridine) (P2VP) with molecular weights (M_w) from 51k to 940k Da, infiltration is investigated in a NPG with fixed pore radius (R_p= 34 nm) under moderate confinement (Γ = R_g/R_p ) 0.18 to 0.78. The time for 80% infiltration (τ_(80%)) scales as M_w^1.43, similar to PS, but weaker than bulk behavior. Infiltration of P2VP is slower than PS due to stronger P2VP-wall interactions resulting in a physisorbed P2VP layer. This interpretation is supported by the similar scaling of τ_(80%) for P2VP and PS, as well as Molecular Dynamics (MD) simulations. Simulations show that infiltration time scales as M_w^1.43and that infiltration slows as the polymer-wall attraction increases. As M_w increases, the effective viscosity transitions from greater than to less than the bulk viscosity due to pore narrowing and a reduction entanglement density. These studies provide new insight for polymer behavior under confinement and a new route for preparing nanocomposites at high filler loadings. 

Abstract Bi‐continuous jammed emulsion (bijel) membrane reactors, integrating simultaneous reaction and separation, offer a promising avenue for enhancing membrane reactor processes. In this study, we present a comprehensive macroscopic‐scale physicochemical model for tubular bijel membrane reactors and a numerical solution strategy for solving the governing partial differential equations. The model captures the co‐continuous network of two immiscible phases stabilized by nanoparticles at the liquid–liquid interface. We present the derivation of model equations and an efficient numerical solution strategy. The model is validated with experimental results from a conventional enzymatic biphasic membrane reactor for oleuropein hydrolysis, already reported in the literature. Simulation results indicate accurate prediction of reactor behavior, highlighting the potential superiority of bijel membrane reactors over current technologies. This research contributes a valuable tool for scale‐up, design, and optimization of bijel membrane reactors, filling a critical gap in this emerging field.