Self-Driven Multistep Quantum Dot Synthesis Enabled by Autonomous Robotic Experimentation in Flow

Identifying the optimal formulation of emerging inorganic lead halide perovskite quantum dots (LHP QDs) with their vast colloidal synthesis universe and multiple synthesis/postsynthesis processing parameters is a challenging undertaking for material- and time-intensive, batch synthesis strategies. Herein, a modular microfluidic synthesis strategy, integrated with an artificial intelligence (AI)-guided decision-making agent for intelligent navigation through the complex colloidal synthesis universe of LHP QDs with 10 individually controlled synthesis parameters and an accessible parameter space exceeding 2x10(7), is introduced. Utilizing the developed autonomous microfluidic experimentation strategy within a global learning framework, the optimal formulation of LHP QDs is rapidly identified through a two-step colloidal synthesis and postsynthesis halide exchange reaction, for 10 different emission colors in less than 40min per desired peak emission energy. Using two in-series microfluidic reactors enables continuous bandgap engineering of LHP QDs via in-line halide exchange reactions without the need for an intermediate washing step. Using an inert gas within a three-phase flow format enables successful, self-synchronized continuous delivery of halide salt precursor into moving droplets containing LHP QDs, resulting in accelerated closed-loop formulation optimization and end-to-end continuous manufacturing of LHP QDs with desired optoelectronic properties.

Automated summary

The article introduces a microfluidic synthesis strategy for intelligent navigation of LHP QDs, rapidly identifying the optimal formulation of LHP QDs through autonomous microfluidic experimentation within a global learning framework, achieving continuous bandgap engineering and continuous manufacturing of LHP QDs.