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With the rise of self-driving labs (SDLs) and automated experimentation across chemical and materials sciences, there is a considerable challenge in designing the best autonomous lab for a given problem based on published studies alone. Determining what digital and physical features are germane to a specific study is a critical aspect of SDL design that needs to be approached quantitatively. Even when controlling for features such as dimensionality, every experimental space has unique requirements and challenges that influence the design of the optimal physical platform and algorithm. Metrics such as optimization rate are therefore not necessarily indicative of the capabilities of an SDL across different studies. In this perspective, we highlight some of the critical metrics for quantifying performance in SDLs to better guide researchers in implementing the most suitable strategies. We then provide a brief review of the existing literature under the lens of quantified performance as well as heuristic recommendations for platform and experimental space pairings.

 

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.

 

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.

 

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.

 

The urgency of finding solutions to global energy, sustainability, and healthcare challenges has motivated rethinking of the conventional chemistry and material science workflows. Self‐driving labs, emerged through integration of disruptive physical and digital technologies, including robotics, additive manufacturing, reaction miniaturization, and artificial intelligence, have the potential to accelerate the pace of materials and molecular discovery by 10–100X. Using autonomous robotic experimentation workflows, self‐driving labs enable access to a larger part of the chemical universe and reduce the time‐to‐solution through an iterative hypothesis formulation, intelligent experiment selection, and automated testing. By providing a data‐centric abstraction to the accelerated discovery cycle, in this perspective article, the required hardware and software technological infrastructure to unlock the true potential of self‐driving labs is discussed. In particular, process intensification as an accelerator mechanism for reaction modules of self‐driving labs and digitalization strategies to further accelerate the discovery cycle in chemical and materials sciences are discussed.

 

Microfluidic devices and systems have entered many areas of chemical engineering, and the rate of their adoption is only increasing. As we approach and adapt to the critical global challenges we face in the near future, it is important to consider the capabilities of flow chemistry and its applications in next-generation technologies for sustainability, energy production, and tailor-made specialty chemicals. We present the introduction of microfluidics into the fundamental unit operations of chemical engineering. We discuss the traits and advantages of microfluidic approaches to different reactive systems, both well-established and emerging, with a focus on the integration of modular microfluidic devices into high-efficiency experimental platforms for accelerated process optimization and intensified continuous manufacturing. Finally, we discuss the current state and new horizons in self-driven experimentation in flow chemistry for both intelligent exploration through the chemical universe and distributed manufacturing. Expected final online publication date for the Annual Review of Chemical and Biomolecular Engineering, Volume 13 is October 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates. 

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.