Autonomous Size-Targeting for Block Copolymer Nanoparticles

Using the combination of flow synthesis with online dynamic light scattering (DLS) analysis for particle size characterization and fully autonomous computer control allows for reproducible and targetable synthesis of nanoparticles from block copolymer (BCP) solutions by rapid mixing with water in a defined micromixer environment. Using Bayesian optimization (BO), nanoparticle sizes become programmable and preselectable, and awide range of sizes can be obtained per used BCP. Specifically, we show for a series of polystyrene-b-poly(N,N- dimethyl acrylamide) and polystyrene-b-poly(poly(ethylene glycol) methyl ether acrylate) BCPs, how particles spanning from 130 to 280 nm can be systematically targeted, with sizes between 100 and 1000 nm being at least in principle also achievable. Further, Pareto fronts for the individual synthesis parameters overall flow rate, water volume fraction and polymer concentration are obtained from the established routines and presented. This BO approach highlights the efficacy of autonomous flow platforms in achieving precise control over polymer self-assembly processes, offering an optimal production window for the development and optimization of polymeric nanostructures in diverse fields such as drug delivery and materials science.