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Colloidal semiconductor nanocrystals, known as quantum dots (QDs), are a rapidly growing class of materials in commercial electronics, such as light emitting diodes (LEDs) and photovoltaics (PVs). Among this material group, inorganic/organic perovskites have demonstrated significant improvement and potential towards high-efficiency, low-cost PV fabrication due to their high charge carrier mobilities and lifetimes. Despite the opportunities for perovskite QDs in large-scale PV and LED applications, the lack of fundamental and comprehensive understanding of their growth pathways has inhibited their adaptation within continuous nanomanufacturing strategies. Traditional flask-based screening approaches are generally expensive, labor-intensive, and imprecise for effectively characterizing the broad parameter space and synthesis variety relevant to colloidal QD reactions. In this work, a fully autonomous microfluidic platform is developed to systematically study the large parameter space associated with the colloidal synthesis of nanocrystals in a continuous flow format. Through the application of a novel translating three-port flow cell and modular reactor extension units, the system may rapidly collect fluorescence and absorption spectra across reactor lengths ranging 3 - 196 cm. The adjustable reactor length not only decouples the residence time from the velocity-dependent mass transfer, it also substantially improves the sampling rates and chemical consumption due to the characterization of 40 unique spectra within a single equilibrated system. Sample rates may reach up to 30,000 unique spectra per day, and the conditions cover 4 orders of magnitude in residence times ranging 100 ms - 17 min. Further applications of this system would substantially improve the rate and precision of the material discovery and screening in future studies. Detailed within this report are the system materials and assembly protocols with a general description of the automated sampling software and offline data processing.

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 CsPbCl3 QDs using a one‐pot high‐temperature synthesis chemistry is reported. Smart Dope continuously synthesizes multi‐cation‐doped CsPbCl3 QDs 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 CsPbCl3 QDs 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. [ABSTRACT FROM AUTHOR]

Development and scalable nanomanufacturing of high‐quality heavy metal‐free quantum dots (QDs) with high‐dimensional experimental design spaces still remain a challenge. In this work, a universal flow chemistry framework for accelerated fundamental and applied studies of heavy metal‐free QDs with multi‐stage chemistries is presented. By introducing flexible time‐ and temperature‐to‐distance transformation using modular fluidic blocks, an in‐flow synthetic route of InP QDs with the highest reported first excitonic absorption peak to valley ratio is unveiled with a reaction time one order of magnitude faster than batch reactors. The flexible time‐ and temperature‐to‐distance transformation as an enabling factor for generalization of flow reactors toward the accelerated discovery, development, and nanomanufacturing of high‐quality emerging nanomaterials for next‐generation energy, display, and chemical technologies is discussed. [ABSTRACT FROM AUTHOR]

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 CsPbI3 NC formation mechanism at temperatures higher than 150 °C, resulting in different CsPbI3 morphologies is revealed. Through comparison of the flow‐ versus flask‐based synthesis, deficiencies of batch reactors in reproducible and scalable synthesis of CsPbI3 NCs 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Oscillatory flow reactors provide a surface energy-driven approach for automatically screening reaction conditions and studying reaction mechanisms of biphasic nanocrystal ligand-exchange reactions. Sulfide and cysteine ligand-exchange reactions with as-synthesized CdSe quantum dots (QDs) are chosen as two model reactions. Different reaction variables including the new-ligand-to-QD ratio, the size of the particles, and the original ligand type are examined systematically. Based on the in situ-obtained UV/Vis absorption spectra during the reaction, we propose two different exchange pathways for the sulfide exchange reaction. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• Kinetic Monte Carlo (kMC) simulation of quantum dot (QD) synthesis is described. • An artificial neural network is used as a surrogate model for the kMC. • A multiscale model for a continuous plug flow crystallizer (PFC) has been formulated. • Construction of an optimal operation framework for set-point tracking of QD size. • An automated microfluidic platform is used for model validation purposes. Inorganic lead halide perovskite quantum dots (QDs) have emerged as a promising semiconducting nanomaterial candidate for widespread applications, including next-generation solar cells, displays, and photocatalysts. The optoelectronic properties of colloidal QDs are majorly dictated by their bandgap energy (related to their size). Thus, it is important to fine-tune the size while having fast and continuous production of QDs. However, the mass and heat transfer limitations of batch reactors with batch-to-batch variations have hindered precise control over the size-dependent optoelectronic properties of QDs. Thus, to address this knowledge gap, we propose a multiscale model for continuous flow manufacturing of colloidal perovskite QDs. Specifically, a first-principled kinetic Monte Carlo model is integrated with a continuum model to describe a plug-flow crystallizer (PFC). The PFC has two manipulated inputs, precursor concentration and superficial flow velocity, to fine-tune the size of QDs. Furthermore, a neural network based surrogate model is designed to identify an optimal input trajectory which will ensure that the desired QD size is achieved, thereby taking a step towards controlled and reliable nanomanufacturing of QDs. [ABSTRACT FROM AUTHOR]