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The controlled and continuous production of nanoparticles (NPs) with functionalized surfaces remains a technological challenge. We present a multistage synthetic platform, consisting of 3D-printed miniature continuous stirred-tank reactor (CSTR) cascades, for the continuous synthesis and functionalization of SiO 2 NPs. The use of the CSTR platform provides ideal and rapid mixing of precursor solutions, precise injection of additional reagents for multistep reactions, and facile operation when using viscous solutions and handling of syntheses with longer reaction times. To exemplify the use of such custom-designed CSTR cascades, amine- and carbohydrate-functionalized SiO 2 NPs are chosen as model reaction systems. In particular, the intensified flow reactor units allowed for the reproducible formation of SiO 2 NPs with diameters less than 100 nm and narrow size distributions (3-8%). Most importantly, by assembling various 3D-printed CSTR cascades, we synthesized gluconolactone-capped polyethylenimine-modified silica NPs in a fully continuous manner. The inherent control over NP surface charge, reactor scalability, and the significant shortening of processing times (less than 10 min) compared to batch methodologies (several days) strongly indicate the ability of the reactor technology to accelerate continuous nanomanufacturing. In general, it provides a simple route for the reproducible preparation of functionalized NPs, thus expanding the gamut of flow reactors for material synthesis.

Recent advances in the development of hybrid organic-inorganic lead halide perovskite (LHP) nanocrystals (NCs) have demonstrated their versatility and potential application in photovoltaics and as light sources through compositional tuning of optical properties. That said, due to their compositional complexity, the targeted synthesis of mixed-cation and/or mixed-halide LHP NCs still represents an immense challenge for traditional batch-scale chemistry. To address this limitation, we herein report the integration of a high-throughput segmented-flow microfluidic reactor and a self-optimizing algorithm for the synthesis of NCs with defined emission properties. The algorithm, named Multiparametric Automated Regression Kriging Interpolation and Adaptive Sampling (MARIA), iteratively computes optimal sampling points at each stage of an experimental sequence to reach a target emission peak wavelength based on spectroscopic measurements. We demonstrate the efficacy of the method through the synthesis of multinary LHP NCs, (Cs/FA)Pb(I/Br) 3 (FA = formamidinium) and (Rb/Cs/FA)Pb(I/Br) 3 NCs, using MARIA to rapidly identify reagent concentrations that yield user-defined photoluminescence peak wavelengths in the green-red spectral region. The procedure returns a robust model around a target output in far fewer measurements than systematic screening of parametric space and additionally enables the prediction of other spectral properties, such as, full-width at half-maximum and intensity, for conditions yielding NCs with similar emission peak wavelength.