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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 2 × 107, 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 40 min 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.
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Microfluidic Synthesis of Semiconductor Materials: Toward Accelerated Materials Development in Flow
Abstract Controlled synthesis of semiconductor nano/microparticles has attracted substantial attention for use in numerous applications from photovoltaics to photocatalysis and bioimaging due to the breadth of available physicochemical and optoelectronic properties. Microfluidic material synthesis strategies have recently been demonstrated as an effective technique for rapid development, controlled synthesis, and continuous manufacturing of solution‐processed semiconductor nano/microparticles, due to enhanced parametric control enabling precise tuning of material properties, size, and morphologies. In this review, the basics of microfluidic material synthesis approaches complemented with recent advances in the flow fabrication of metal oxide, chalcogenide, and perovskite semiconductor particles are discussed. Furthermore, advancements in artificial intelligence (AI)‐driven materials–space exploration and accelerated formulation optimization using modular microfluidic reactors are outlined. Finally, future directions for the fabrication of semiconducting materials in flow and the implementation of AI with automated microfluidic reactors for accelerated material discovery and development are presented.
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- Award ID(s):
- 1940959
- PAR ID:
- 10382441
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Particle & Particle Systems Characterization
- Volume:
- 37
- Issue:
- 12
- ISSN:
- 0934-0866
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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