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.
Keywords: Kriging; microfluidics; nanocrystals; optimization; perovskites; quantum dots.