Metal cation‐doped lead halide perovskite (LHP) quantum dots (QDs) with photoluminescence quantum yields (PLQYs) higher than unity, due to quantum cutting phenomena, are an important building block of the next‐generation renewable energy technologies. However, synthetic route exploration and development of the highest‐performing QDs for device applications remain challenging. In this work, Smart Dope is presented, which is a self‐driving fluidic lab (SDFL), for the accelerated synthesis space exploration and autonomous optimization of LHP QDs. Specifically, the multi‐cation doping of CsPbCl3QDs using a one‐pot high‐temperature synthesis chemistry is reported. Smart Dope continuously synthesizes multi‐cation‐doped CsPbCl3QDs using a high‐pressure gas‐liquid segmented flow format to enable continuous experimentation with minimal experimental noise at reaction temperatures up to 255°C. Smart Dope offers multiple functionalities, including accelerated mechanistic studies through digital twin QD synthesis modeling, closed‐loop autonomous optimization for accelerated QD synthetic route discovery, and on‐demand continuous manufacturing of high‐performing QDs. Through these developments, Smart Dope autonomously identifies the optimal synthetic route of Mn‐Yb co‐doped CsPbCl3QDs with a PLQY of 158%, which is the highest reported value for this class of QDs to date. Smart Dope illustrates the power of SDFLs in accelerating the discovery and development of emerging advanced energy materials.
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Abstract Despite the groundbreaking advancements in the synthesis of inorganic lead halide perovskite (LHP) nanocrystals (NCs), stimulated from their intriguing size‐, composition‐, and morphology‐dependent optical and optoelectronic properties, their formation mechanism through the hot‐injection (HI) synthetic route is not well‐understood. In this work, for the first time, in‐flow HI synthesis of cesium lead iodide (CsPbI3) NCs is introduced and a comprehensive understanding of the interdependent competing reaction parameters controlling the NC morphology (nanocube vs nanoplatelet) and properties is provided. Utilizing the developed flow synthesis strategy, a change in the CsPbI3NC formation mechanism at temperatures higher than 150 °C, resulting in different CsPbI3morphologies is revealed. Through comparison of the flow‐ versus flask‐based synthesis, deficiencies of batch reactors in reproducible and scalable synthesis of CsPbI3NCs with fast formation kinetics are demonstrated. The developed modular flow chemistry route provides a new frontier for high‐temperature studies of solution‐processed LHP NCs and enables their consistent and reliable continuous nanomanufacturing for next‐generation energy technologies.
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Lead halide perovskite (LHP) nanocrystals (NCs) are considered an emerging class of advanced functional materials with numerous outstanding optoelectronic characteristics. Despite their success in the field, their precision synthesis and fundamental mechanistic studies remain a challenge. The vast colloidal synthesis and processing parameters of LHP NCs in combination with the batch‐to‐batch and lab‐to‐lab variation problems further complicate their progress. In response, a self‐driving fluidic micro‐processor is presented for accelerated navigation through the complex synthesis and processing parameter space of NCs with multistage chemistries. The capability of the developed autonomous experimentation strategy is demonstrated for a time‐, material‐, and labor‐efficient search through the sequential halide exchange and cation doping reactions of LHP NCs. Next, a machine learning model of the modular fluidic micro‐processors is autonomously built for accelerated fundamental studies of the in‐flow metal cation doping of LHP NCs. The surrogate model of the sequential halide exchange and cation doping reactions of LHP NCs is then utilized for five closed‐loop synthesis campaigns with different target NC doping levels. The precise and intelligent NC synthesis and processing strategy, presented herein, can be further applied toward the autonomous discovery and development of novel impurity‐doped NCs with applications in next‐generation energy technologies.
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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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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.