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Although a vital parameter in many colloidal nanomaterial syntheses, precursor mixing rates are typically inconsistent in batch processes and difficult to separate from reaction time in continuous flow systems. Here, we present a flow chemistry platform that decouples early-stage precursor mixing rates from reaction time (residence time) using solely off-the-shelf, commercially available, and standard dimension components. We then utilize the developed flow chemistry platform towards time- and material-efficient studies of the mass transfer-controlled synthesis of cesium lead bromide perovskite quantum dots. 

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.

 

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.

 

In an effort to produce the materials of next‐generation photoelectronic devices, postsynthesis halide exchange reactions of perovskite quantum dots are explored to achieve enhanced bandgap tunability. However, comprehensive understanding of the multifaceted halide exchange reactions is inhibited by their vast relevant parameter space and complex reaction network. In this work, a facile room‐temperature strategy is presented for rapid halide exchange of inorganic perovskite quantum dots. A comprehensive understanding of the halide exchange reactions is provided by isolating reaction kinetics from precursor mixing rates utilizing a modular microfluidic platform, Quantum Dot Exchanger (QDExer). The effects of ligand composition and halide salt source on the rate and extent of the halide exchange reactions are illustrated. This fluidic platform offers a unique time‐ and material‐efficient approach for studies of solution phase‐processed colloidal nanocrystals beyond those studied here and may accelerate the discovery and optimization of next‐generation materials for energy technologies.

 

The optimal synthesis of advanced nanomaterials with numerous reaction parameters, stages, and routes, poses one of the most complex challenges of modern colloidal science, and current strategies often fail to meet the demands of these combinatorially large systems. In response, an Artificial Chemist is presented: the integration of machine‐learning‐based experiment selection and high‐efficiency autonomous flow chemistry. With the self‐driving Artificial Chemist, made‐to‐measure inorganic perovskite quantum dots (QDs) in flow are autonomously synthesized, and their quantum yield and composition polydispersity at target bandgaps, spanning 1.9 to 2.9 eV, are simultaneously tuned. Utilizing the Artificial Chemist, eleven precision‐tailored QD synthesis compositions are obtained without any prior knowledge, within 30 h, using less than 210 mL of total starting QD solutions, and without user selection of experiments. Using the knowledge generated from these studies, the Artificial Chemist is pre‐trained to use a new batch of precursors and further accelerate the synthetic path discovery of QD compositions, by at least twofold. The knowledge‐transfer strategy further enhances the optoelectronic properties of the in‐flow synthesized QDs (within the same resources as the no‐prior‐knowledge experiments) and mitigates the issues of batch‐to‐batch precursor variability, resulting in QDs averaging within 1 meV from their target peak emission energy.