“…Compared to batch reactors, droplet-based microfluidic synthesis strategies have been demonstrated as a reliable reactor of choice for high-throughput screening, mechanistic studies, and continuous production of colloidal NCs, including metal oxide, silver, and gold NCs, as well as II-VI, III-V, and Pb-based MHP NCs. [30][31][32][33][34][35][36][37][38][39] The continuous nature of microfluidic reactors along with their modularity, facile automation, and integration with multimodal in situ characterization tools (e.g., spectroscopy) 28,40,41 offer an exciting avenue to accelerate parameter space and synthesis-property relationship mapping of NCs through integration with data science tools in a closed-loop format. Such integration of an automated microfluidic reactor with machine learning (ML)-assisted process modelling and experiment-selection results in establishment of self-driving fluidic labs (SDFLs).…”