Pick a Color MARIA: Adaptive Sampling Enables the Rapid Identification of Complex Perovskite Nanocrystal Compositions with Defined Emission Characteristics

The Future of Material Scientists in an Age of Artificial Intelligence

Material science has historically evolved in tandem with advancements in technologies for characterization, synthesis, and computation. Another type of technology to add to this mix is machine learning (ML) and artificial intelligence (AI). Now increasingly sophisticated AI-models are seen that can solve progressively harder problems across a variety of fields. From a material science perspective, it is indisputable that machine learning and artificial intelligence offer a potent toolkit with the potential to substantially accelerate research efforts in areas such as the development and discovery of new functional materials. Less clear is how to best harness this development, what new skill sets will be required, and how it may affect established research practices. In this paper, those question are explored with respect to increasingly more sophisticated ML/AI-approaches. To structure the discussion, a conceptual framework of an AI-ladder is introduced. This AI-ladder ranges from basic data-fitting techniques to more advanced functionalities such as semi-autonomous experimentation, experimental design, knowledge generation, hypothesis formulation, and the orchestration of specialized AI modules as stepping-stones toward general artificial intelligence. This ladder metaphor provides a hierarchical framework for contemplating the opportunities, challenges, and evolving skill sets required to stay competitive in the age of artificial intelligence.

Cognitively Driven Autonomous Flow Chemistry for Producing On-Demand Perovskite Quantum Dots Via Advanced Closed-Loop Feedback Control

Recent developments in the synthesis of hybrid organic-inorganic halide perovskite quantum dots (HP-QDs) through compositional adjustments have highlighted their potential applications in the fields of photovoltaics and light sources due to their unique optoelectronic properties. However, traditional methods to fine-tune their composition involve repetitive, labor-intensive, and costly processes. Herein, the utilization of a continuous flow chemistry approach is developed, in combination with a Proportional-Integral (PI) feedback control system as an effective method for producing on-demand methylammonium lead bromoiodide (MAPbBrx I3-x ) HP-QDs. The PI feedback control allows for real-time optimization of the flow rates of halide precursor solutions (halide PSs), enabling the precise tuning of the emission wavelength of HP-QDs. HP-QDs having an emission wavelength of 550 and 650 nm are synthesized through a blue-shifted and red-shifted algorithm, respectively, from any arbitrary reaction condition within 400 s. The iterative process through the PI feedback control produces the target HP-QDs with short rise time and low overshoot. The proposed automatic flow chemistry system integrated with a universal and accessible control algorithm of PI can generate the target HP-QDs with high accuracy, stability, and robustness, demonstrating a significant advancement in constructing an autonomous flow chemistry synthetic system.

Performance metrics to unleash the power of self-driving labs in chemistry and materials science

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.

Accelerating the Design of Multishell Upconverting Nanoparticles through Bayesian Optimization

The photon upconverting properties of lanthanide-doped nanoparticles drive their applications in imaging, optoelectronics, and additive manufacturing. To maximize their brightness, these upconverting nanoparticles (UCNPs) are often synthesized as core/shell heterostructures. However, the large numbers of compositional and structural parameters in multishell heterostructures make optimizing optical properties challenging. Here, we demonstrate the use of Bayesian optimization (BO) to learn the structure and design rules for multishell UCNPs with bright ultraviolet and violet emission. We leverage an automated workflow that iteratively recommends candidate UCNP structures and then simulates their emission spectra using kinetic Monte Carlo. Yb3+/Er3+- and Yb3+/Er3+/Tm3+-codoped UCNP nanostructures optimized with this BO workflow achieve 10- and 110-fold brighter emission within 22 and 40 iterations, respectively. This workflow can be expanded to structures with higher compositional and structural complexity, accelerating the discovery of novel UCNPs while domain-specific knowledge is being developed.

Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation

Decades of nanotoxicology research have generated extensive and diverse data sets. However, data is not equal to information. The question is how to extract critical information buried in vast data streams. Here we show that artificial intelligence (AI) and molecular simulation play key roles in transforming nanotoxicity data into critical information, i.e., constructing the quantitative nanostructure (physicochemical properties)-toxicity relationships, and elucidating the toxicity-related molecular mechanisms. For AI and molecular simulation to realize their full impacts in this mission, several obstacles must be overcome. These include the paucity of high-quality nanomaterials (NMs) and standardized nanotoxicity data, the lack of model-friendly databases, the scarcity of specific and universal nanodescriptors, and the inability to simulate NMs at realistic spatial and temporal scales. This review provides a comprehensive and representative, but not exhaustive, summary of the current capability gaps and tools required to fill these formidable gaps. Specifically, we discuss the applications of AI and molecular simulation, which can address the large-scale data challenge for nanotoxicology research. The need for model-friendly nanotoxicity databases, powerful nanodescriptors, new modeling approaches, molecular mechanism analysis, and design of the next-generation NMs are also critically discussed. Finally, we provide a perspective on future trends and challenges.

AlphaFlow: autonomous discovery and optimization of multi-step chemistry using a self-driven fluidic lab guided by reinforcement learning

Closed-loop, autonomous experimentation enables accelerated and material-efficient exploration of large reaction spaces without the need for user intervention. However, autonomous exploration of advanced materials with complex, multi-step processes and data sparse environments remains a challenge. In this work, we present AlphaFlow, a self-driven fluidic lab capable of autonomous discovery of complex multi-step chemistries. AlphaFlow uses reinforcement learning integrated with a modular microdroplet reactor capable of performing reaction steps with variable sequence, phase separation, washing, and continuous in-situ spectral monitoring. To demonstrate the power of reinforcement learning toward high dimensionality multi-step chemistries, we use AlphaFlow to discover and optimize synthetic routes for shell-growth of core-shell semiconductor nanoparticles, inspired by colloidal atomic layer deposition (cALD). Without prior knowledge of conventional cALD parameters, AlphaFlow successfully identified and optimized a novel multi-step reaction route, with up to 40 parameters, that outperformed conventional sequences. Through this work, we demonstrate the capabilities of closed-loop, reinforcement learning-guided systems in exploring and solving challenges in multi-step nanoparticle syntheses, while relying solely on in-house generated data from a miniaturized microfluidic platform. Further application of AlphaFlow in multi-step chemistries beyond cALD can lead to accelerated fundamental knowledge generation as well as synthetic route discoveries and optimization.

A versatile non-fouling multi-step flow reactor platform: demonstration for partial oxidation synthesis of iron oxide nanoparticles

In the last decade flow reactors for material synthesis were firmly established, demonstrating advantageous operating conditions, reproducible and scalable production via continuous operation, as well as high-throughput screening of synthetic conditions. Reactor fouling, however, often restricts flow chemistry and the common fouling prevention via segmented flow comes at the cost of inflexibility. Often, the difficulty of feeding reagents into liquid segments (droplets or slugs) constrains flow syntheses using segmented flow to simple synthetic protocols with a single reagent addition step prior or during segmentation. Hence, the translation of fouling prone syntheses requiring multiple reagent addition steps into flow remains challenging. This work presents a modular flow reactor platform overcoming this bottleneck by fully exploiting the potential of three-phase (gas-liquid-liquid) segmented flow to supply reagents after segmentation, hence facilitating fouling free multi-step flow syntheses. The reactor design and materials selection address the operation challenges inherent to gas-liquid-liquid flow and reagent addition into segments allowing for a wide range of flow rates, flow ratios, temperatures, and use of continuous phases (no perfluorinated solvents needed). This "Lego®-like" reactor platform comprises elements for three-phase segmentation and sequential reagent addition into fluid segments, as well as temperature-controlled residence time modules that offer the flexibility required to translate even complex nanomaterial synthesis protocols to flow. To demonstrate the platform's versatility, we chose a fouling prone multi-step synthesis, i.e., a water-based partial oxidation synthesis of iron oxide nanoparticles. This synthesis required I) the precipitation of ferrous hydroxides, II) the addition of an oxidation agent, III) a temperature treatment to initiate magnetite/maghemite formation, and IV) the addition of citric acid to increase the colloidal stability. The platform facilitated the synthesis of colloidally stable magnetic nanoparticles reproducibly at well-controlled synthetic conditions and prevented fouling using heptane as continuous phase. The biocompatible particles showed excellent heating abilities in alternating magnetic fields (ILP values >3 nH m² kg_Fe^-1), hence, their potential for magnetic hyperthermia cancer treatment. The platform allowed for long term operation, as well as screening of synthetic conditions to tune particle properties. This was demonstrated via the addition of tetraethylenepentamine, confirming its potential to control particle morphology. Such a versatile reactor platform makes it possible to translate even complex syntheses into flow, opening up new opportunities for material synthesis.

Creating ground truth for nanocrystal morphology: a fully automated pipeline for unbiased transmission electron microscopy analysis

Control over colloidal nanocrystal morphology (size, size distribution, and shape) is important for tailoring the functionality of individual nanocrystals and their ensemble behavior. Despite this, traditional methods to quantify nanocrystal morphology are laborious. New developments in automated morphology classification will accelerate these analyses but the assessment of machine learning models is limited by human accuracy for ground truth, causing even unsupervised machine learning models to have inherent bias. Herein, we introduce synthetic image rendering to solve the ground truth problem of nanocrystal morphology classification. By simulating 2D images of nanocrystal shapes via a function of high-dimensional parameter space, we trained a convolutional neural network to link unique morphologies to their simulated parameters, defining nanocrystal morphology quantitatively rather than qualitatively. An automated pipeline then processes, quantitatively defines, and classifies nanocrystal morphology from experimental transmission electron microscopy (TEM) images. Using improved computer vision techniques, 42 650 nanocrystals were identified, assessed, and labeled with quantitative parameters, offering a 600-fold improvement in efficiency over best-practice manual measurements. A classification algorithm was trained with a prediction accuracy of 99.5%, which can successfully analyze a range of concave, convex, and irregular nanocrystal shapes. The resulting pipeline was applied to differentiating two syntheses of nominally cuboidal CsPbBr₃ nanocrystals and uniquely classifying binary nickel sulfide nanocrystal phase based on morphology. This pipeline provides a simple, efficient, and unbiased method to quantify nanocrystal morphology and represents a practical route to construct large datasets with an absolute ground truth for training unbiased morphology-based machine learning algorithms.

Kinetic analysis of the growth behavior of perovskite CsPbBr3 nanocrystals in a microfluidic system

Understanding the growth behavior of nanoparticles and semiconductor nanocrystals under dynamic environments is of profound importance in controlling the sizes and uniformity of the prepared nanoparticles and semiconductor nanocrystals. In this work, we develop a relation between the bandgap (the photoluminescence peak wavelength) of semiconductor nanocrystals and the total flow rate for the synthesis of semiconductor nanocrystals in microfluidic systems under the framework of the quantum confinement effect without the contribution of Coulomb interaction. Using this relation, we analyze the growth behavior of CsPbBr₃ nanocrystals synthesized in a microfluidic system by an antisolvent method in the temperature range of 303 to 363 K. The results demonstrate that the square of the average size of the CsPbBr₃ nanocrystals is inversely proportional to the total flow rate and support the developed relation. The activation energy for the rate process controlling the growth of the CsPbBr₃ nanocrystals in the microfluidic system is 2.05 kJ mol^-1. Increasing the synthesis temperature widens the size distribution of the CsPbBr₃ NCs prepared in the microfluidic system. The method developed in this work provides a simple approach to use photoluminescent characteristics to in situ monitor and analyze the growth of semiconductor nanocrystals under dynamic environments.

Toward continuous production of high-quality nanomaterials using microfluidics: nanoengineering the shape, structure and chemical composition

Over the last decade, a multitude of synthesis strategies has been reported for the production of high-quality nanoparticles. Wet-chemical methods are generally the most efficient synthesis procedures since high control of crystallinity and physicochemical properties can be achieved. However, a number of challenges remain from inadequate reaction control during the nanocrystallization process; specifically variability, selectivity, scalability and safety. These shortcomings complicate the synthesis, make it difficult to obtain a uniform product with desired properties, and present serious limitations for scaling the production of colloidal nanocrystals from academic studies to industrial applications. Continuous flow reactors based on microfluidic principles offer potential solutions and advantages. The reproducibility of reaction conditions in microfluidics and therefore product quality have proved to exceed those obtained by batch processing. Considering that in nanoparticles' production not only is it crucial to control the particle size distribution, but also the shape and chemical composition, this review presents an overview of the current state-of-the-art in synthesis of anisotropic and faceted nanostructures by using microfluidics techniques. The review surveys the available tools that enable shape and chemical control, including secondary growth methods, active segmented flow, and photoinduced shape conversion. In addition, emphasis is placed on the available approaches developed to tune the structure and chemical composition of nanomaterials in order to produce complex heterostructures in a continuous and reproducible fashion.

Flow Chemistry: A Sustainable Voyage Through the Chemical Universe en Route to Smart Manufacturing

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.

Electrochemical pH regulation in droplet microfluidics

We report a method for electrochemical pH regulation in microdroplets generated in a microfluidic device. The key finding is that controlled quantities of reagents can be generated electrochemically in moving microdroplets confined within a microfluidic channel. Additionally, products generated at the anode and cathode can be isolated within descendant microdroplets. Specifically, ∼5 nL water-in-oil microdroplets are produced at a T-junction and then later split into two descendant droplets. During splitting, floor-patterned microelectrodes drive water electrolysis within the aqueous microdroplets to produce H⁺ and OH^-. This results in a change in the pHs of the descendant droplets. The droplet pH can be regulated over a range of 5.9 to 7.7 by injecting controlled amounts of charge into the droplets. When the injected charge is between -6.3 and 54.5 nC nL^-1, the measured pH of the resulting droplets is within ±0.1 pH units of that predicted based on the magnitude of the injected charge. This technique can likely be adapted to electrogeneration of other reagents within microdroplets.

Microfluidic technologies for nanoparticle formation

Functional nanoparticles (NPs) hold immense promise in diverse fields due to their unique biological, chemical, and physical properties associated with size or morphology. Microfluidic technologies featuring precise fluid manipulation have become versatile toolkits for manufacturing NPs in a highly controlled manner with low batch-to-batch variability. In this review, we present the fundamentals of microfluidic fabrication strategies, including mixing-, droplet-, and multiple field-based microfluidic methods. We highlight the formation of functional NPs using these microfluidic reactors, with an emphasis on lipid NPs, polymer NPs, lipid-polymer hybrid NPs, supramolecular NPs, metal and metal-oxide NPs, metal-organic framework NPs, covalent organic framework NPs, quantum dots, perovskite nanocrystals, biomimetic NPs, etc. we discuss future directions in microfluidic fabrication for accelerated development of functional NPs, such as device parallelization for large-scale NP production, highly efficient optimization of NP formulations, and AI-guided design of multi-step microfluidic reactors.

Microfluidic nanomaterials: From synthesis to biomedical applications

Microfluidic platforms gain popularity in biomedical research due to their attractive inherent features, especially in nanomaterials synthesis. This review critically evaluates the current state of the controlled synthesis of nanomaterials using microfluidic devices. We describe nanomaterials' screening in microfluidics, which is very relevant for automating the synthesis process for biomedical applications. We discuss the latest microfluidics trends to achieve noble metal, silica, biopolymer, quantum dots, iron oxide, carbon-based, rare-earth-based, and other nanomaterials with a specific size, composition, surface modification, and morphology required for particular biomedical application. Screening nanomaterials has become an essential tool to synthesize desired nanomaterials using more automated processes with high speed and repeatability, which can't be neglected in today's microfluidic technology. Moreover, we emphasize biomedical applications of nanomaterials, including imaging, targeting, therapy, and sensing. Before clinical use, nanomaterials have to be evaluated under physiological conditions, which is possible in the microfluidic system as it stimulates chemical gradients, fluid flows, and the ability to control microenvironment and partitioning multi-organs. In this review, we emphasize the clinical evaluation of nanomaterials using microfluidics which was not covered by any other reviews. In the future, the growth of new materials or modification in existing materials using microfluidics platforms and applications in a diversity of biomedical fields by utilizing all the features of microfluidic technology is expected.

Microfluidic based nanoparticle synthesis and their potential applications

A lot of substantial innovation in advancement of microfluidic field in recent years to produce nanoparticle reveals a number of distinctive characteristics for instance compactness, controllability, fineness in process, and stability along with minimal reaction amount. Recently, a prompt development, as well as realization in production of nanoparticles in microfluidic environs having dimension of micro to nanometers and constituents extending from metals, semiconductors to polymers, has been made. Microfluidics technology integrates fluid mechanics for production of nanoparticles having exclusive with homogenous sizes, shapes, and morphology, which are utilized in several bioapplications such as biosciences, drug delivery, healthcare, including food engineering. Nanoparticles are usually well-known for having fine and rough morphology because of their small dimensions including exceptional physical, biological, chemical, and optical properties. Though the orthodox procedures need huge instruments, costly autoclaves, use extra power, extraordinary heat loss, as well as take surplus time for synthesis. Additionally, this is fascinating in order to systematize, assimilate, in addition, to reduce traditional tools onto one platform to produce micro and nanoparticles. The synthesis of nanoparticles by microfluidics permits fast handling besides better efficacy of method utilizing the smallest components for process. Herein, we will focus on synthesis of nanoparticles by means of microfluidic devices intended for different bioapplications. This article is protected by copyright. All rights reserved.

Accelerated screening of colloidal nanocrystals using artificial neural network-assisted autonomous flow reactor technology

Colloidal semiconductor nanocrystals with tunable optical and electronic properties are opening up exciting opportunities for high-performance optoelectronics, photovoltaics, and bioimaging applications. Identifying the optimal synthesis conditions and screening of synthesis recipes in search of efficient synthesis pathways to obtain nanocrystals with desired optoelectronic properties, however, remains one of the major bottlenecks for accelerated discovery of colloidal nanocrystals. Conventional strategies, often guided by limited understanding of the underlying mechanisms remain expensive in both time and resources, thus significantly impeding the overall discovery process. In response, an autonomous experimentation platform is presented as a viable approach for accelerated synthesis screening and optimization of colloidal nanocrystals. Using a machine-learning-based predictive synthesis approach, integrated with automated flow reactor and inline spectroscopy, indium phosphide nanocrystals are autonomously synthesized. Their polydispersity for different target absorption wavelengths across the visible spectrum is simultaneously optimized during the autonomous experimentation, while utilizing minimal self-driven experiments (less than 50 experiments within 2 days). Starting with no-prior-knowledge of the synthesis, an ensemble neural network is trained through autonomous experiments to accurately predict the reaction outcome across the entire synthesis parameter space. The predicted parameter space map also provides new nucleation-growth kinetic insights to achieve high monodispersity in size of colloidal nanocrystals.

Development of an in-line magnetometer for flow chemistry and its demonstration for magnetic nanoparticle synthesis

Despite the wide usage of magnetic nanoparticles, it remains challenging to synthesise particles with properties that exploit each application's full potential. Time consuming experimental procedures and particle analysis hinder process development, which is commonly constrained to a handful of experiments without considering particle formation kinetics, reproducibility and scalability. Flow reactors are known for their potential of large-scale production and high-throughput screening of process parameters. These advantages, however, have not been utilised for magnetic nanoparticle synthesis where particle characterisation is performed, with a few exceptions, post-synthesis. To overcome this bottleneck, we developed a highly sensitive magnetometer for flow reactors to characterise magnetic nanoparticles in solution in-line and in real-time using alternating current susceptometry. This flow magnetometer enriches the flow-chemistry toolbox by facilitating continuous quality control and high-throughput screening of magnetic nanoparticle syntheses. The sensitivity required to monitor magnetic nanoparticle syntheses at the typically low concentrations (<100 mM of Fe) was achieved by comparing the signals induced in the sample and reference cell, each of which contained near-identical pairs of induction and pick-up coils. The reference cell was filled only with air, whereas the sample cell was a flow cell allowing sample solution to pass through. Balancing the flow and reference cell impedance with a newly developed electronic circuit was pivotal for the magnetometer's sensitivity. To showcase its potential, the flow magnetometer was used to monitor two iron oxide nanoparticle syntheses with well-known particle formation kinetics, i.e., co-precipitation syntheses with sodium carbonate and sodium hydroxide as base, which have been previously studied via synchrotron X-ray diffraction. The flow magnetometer facilitated batch (on-line) and flow (in-line) synthesis monitoring, providing new insights into the particle formation kinetics as well as, effect of temperature and pH. The compact lab-scale flow device presented here, opens up new possibilities for magnetic nanoparticle synthesis and manufacturing, including 1) early stage reaction characterisation 2) process monitoring and control and 3) high-throughput screening in combination with flow reactors.

Microdroplet Operations in Polymeric Microtubes

Microsystem technologies allow a plethora of operations to be achieved for microemulsion- and microdroplet-based assays, providing miniaturized, yet large-throughput capabilities to assist experimentation in analytical chemistry, biology, and synthetic biology. Many of such approaches have been implemented on-chip, using microfluidic and lab-on-a-chip technologies. However, the microfabrication of such devices relies on expensive equipment and time-consuming methods, thus hindering their uptake and use by many research laboratories where microfabrication expertise is not available. Here, we demonstrate how fundamental water-in-oil microdroplet operations, such as droplet trapping, merging, diluting, and splitting, can be obtained using straightforward, inexpensive, and manually fabricated polymeric microtube modules. The modules are based on creating an angled tubing interface at the interconnection between two polymeric microtubes. We have characterized how the geometry and fluid dynamic conditions at this interface enabled different droplet operations to be achieved in a versatile and functional manner. We envisage this approach to be an alternative solution to expensive and laborious microfabrication protocols for droplet microfluidic applications.

Opportunities for Next-Generation Luminescent Materials through Artificial Intelligence

Luminescent materials are continually sought for application in solid-state LED-based lighting and display applications. This has traditionally required extensive experimental effort. More recently, the employment of data-driven approaches in materials science has provided an alternative avenue to accelerate the discovery and development of luminescent materials. In this Perspective, we give an overview of luminescent materials used for lighting and display applications with a specific focus on inorganic phosphors, quantum dots, and organic light-emitting diodes. We discuss recent progress using data-driven approaches to discover new compounds, predict optical properties, and optimize synthesis, among other topics for each type of material. We then highlight future research directions focusing on using artificial intelligence (AI) to advance these fields and address some cross-cutting challenges limiting the current application of AI techniques in luminescence-related research.

Using design of experiment to obtain a systematic understanding of the effect of synthesis parameters on the properties of perovskite nanocrystals

Design of experiments was used to systematically investigate the synthesis of MAPbI₃ nanoparticles in a flow reactor. By controlling the solvents and the ligands, we were able to tune the MAPbI₃ photoluminescence peak between 614 and 737 nm.

Accelerated Development of Colloidal Nanomaterials Enabled by Modular Microfluidic Reactors: Toward Autonomous Robotic Experimentation

In recent years, microfluidic technologies have emerged as a powerful approach for the advanced synthesis and rapid optimization of various solution-processed nanomaterials, including semiconductor quantum dots and nanoplatelets, and metal plasmonic and reticular framework nanoparticles. These fluidic systems offer access to previously unattainable measurements and synthesis conditions at unparalleled efficiencies and sampling rates. Despite these advantages, microfluidic systems have yet to be extensively adopted by the colloidal nanomaterial community. To help bridge the gap, this progress report details the basic principles of microfluidic reactor design and performance, as well as the current state of online diagnostics and autonomous robotic experimentation strategies, toward the size, shape, and composition-controlled synthesis of various colloidal nanomaterials. By discussing the application of fluidic platforms in recent high-priority colloidal nanomaterial studies and their potential for integration with rapidly emerging artificial intelligence-based decision-making strategies, this report seeks to encourage interdisciplinary collaborations between microfluidic reactor engineers and colloidal nanomaterial chemists. Full convergence of these two research efforts offers significantly expedited and enhanced nanomaterial discovery, optimization, and manufacturing.

Accelerated AI development for autonomous materials synthesis in flow

Autonomous robotic experimentation strategies are rapidly rising in use because, without the need for user intervention, they can efficiently and precisely converge onto optimal intrinsic and extrinsic synthesis conditions for a wide range of emerging materials. However, as the material syntheses become more complex, the meta-decisions of artificial intelligence (AI)-guided decision-making algorithms used in autonomous platforms become more important. In this work, a surrogate model is developed using data from over 1000 in-house conducted syntheses of metal halide perovskite quantum dots in a self-driven modular microfluidic material synthesizer. The model is designed to represent the global failure rate, unfeasible regions of the synthesis space, synthesis ground truth, and sampling noise of a real robotic material synthesis system with multiple output parameters (peak emission, emission linewidth, and quantum yield). With this model, over 150 AI-guided decision-making strategies within a single-period horizon reinforcement learning framework are automatically explored across more than 600 000 simulated experiments – the equivalent of 7.5 years of continuous robotic operation and 400 L of reagents – to identify the most effective methods for accelerated materials development with multiple objectives. Specifically, the structure and meta-decisions of an ensemble neural network-based material development strategy are investigated, which offers a favorable technique for intelligently and efficiently navigating a complex material synthesis space with multiple targets. The developed ensemble neural network-based decision-making algorithm enables more efficient material formulation optimization in a no prior information environment than well-established algorithms.

A surrogate model is designed to represent a microfluidic material synthesis system using 1000 automatically conducted experiments. With this model, over 600 000 experiments are simulated to optimize an AI-guided material synthesis algorithm.

Optical and Electronic Properties of Colloidal CdSe Quantum Rings

Luminescent colloidal CdSe nanorings are a recently developed type of semiconductor structure that have attracted interest due to the potential for rich physics arising from their nontrivial toroidal shape. However, the exciton properties and dynamics of these materials with complex topology are not yet well understood. Here, we use a combination of femtosecond vibrational spectroscopy, temperature-resolved photoluminescence (PL), and single-particle measurements to study these materials. We find that on transformation of CdSe nanoplatelets to nanorings, by perforating the center of platelets, the emission lifetime decreases and the emission spectrum broadens due to ensemble variations in the ring size and thickness. The reduced PL quantum yield of nanorings (∼10%) compared to platelets (∼30%) is attributed to an enhanced coupling between (i) excitons and CdSe LO-phonons at 200 cm^-1 and (ii) negatively charged selenium-rich traps, which give nanorings a high surface charge (∼-50 mV). Population of these weakly emissive trap sites dominates the emission properties with an increased trap emission at low temperatures relative to excitonic emission. Our results provide a detailed picture of the nature of excitons in nanorings and the influence of phonons and surface charge in explaining the broad shape of the PL spectrum and the origin of PL quantum yield losses. Furthermore, they suggest that the excitonic properties of nanorings are not solely a consequence of the toroidal shape but also a result of traps introduced by puncturing the platelet center.

Artificial Chemist: An Autonomous Quantum Dot Synthesis Bot

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.

Continuous Synthesis of Nanocrystals via Flow Chemistry Technology

Modern nanotechnologies bring humanity to a new age, and advanced methods for preparing functional nanocrystals are cornerstones. A considerable variety of nanomaterials has been created over the past decades, but few were prepared on the macro scale, even fewer making it to the stage of industrial production. The gap between academic research and engineering production is expected to be filled by flow chemistry technology, which relies on microreactors. Microreaction devices and technologies for synthesizing different kinds of nanocrystals are discussed from an engineering point of view. The advantages of microreactors, the important features of flow chemistry systems, and methods to apply them in the syntheses of salt, oxide, metal, alloy, and quantum dot nanomaterials are summarized. To further exhibit the scaling-up of nanocrystal synthesis, recent reports on using microreactors with gram per hour and larger production rates are highlighted. Finally, an industrial example for preparing 10 tons of CaCO₃ nanoparticles per day is introduced, which shows the great potential for flow chemistry processes to transfer lab research to industry.

Automated droplet reactor for the synthesis of iron oxide/gold core-shell nanoparticles

Core-shell nanoparticles are promising candidates for theranostic drugs, as they combine different intrinsic properties with a small size and large surface area. However, their controlled synthesis, or the screening and optimization of synthesis conditions are often difficult and labor intensive. Through the precise control over mass and heat transfer, and automatization possibilities, microfluidic devices could be a solution to this problem in a lab scale synthesis. Here, we demonstrate a microfluidic, capillary, droplet reactor for the multi-step synthesis of iron oxide/gold core-shell nanoparticles. Through the integration of a transmission measurement at the outlet of the reactor, synthesis results can be monitored in a real-time manner. This allowed for the implementation of an optimization algorithm. Starting from three separate initial guesses, the algorithm converged to the same synthesis conditions in less than 30 minutes for each initial guess. These conditions resulted in diameter for the iron oxide core of 5.8 ± 1.4 nm, a thickness for the gold shell of 3.5 ± 0.6 nm, and a total diameter of the core-shell particles of 13.1 ± 2.5 nm. Finally, applications of the iron oxide/gold core-shell nanoparticles were demonstrated for Surface Enhanced Raman Spectroscopy (SERS), photothermal therapy, and magnetic resonance imaging (MRI).

An automated flow chemistry platform to decouple mixing and reaction times

We present a flow chemistry platform that decouples precursor mixing rates from reaction time using solely off-the-shelf components. We then utilize this platform towards material-efficient studies of mass transfer-controlled synthesis of inorganic perovskite quantum dots.

Rapid synthesis of quantum-confined CsPbBr3 perovskite nanowires using a microfluidic reactor

Microfluidics has been considered as an effective platform in the mechanism study and large-scale manufacturing of nanomaterials. In this work, we report the facile synthesis of quantum-confined CsPbBr₃ nanowires (NWs) by using a continuous-flow microfluidic reactor. The optimized reaction temperature is around 50 °C, and one "synthesis run" by microfluidics requires only ten minutes. This study reveals that the formation of CsPbBr₃ NWs takes place by a hybrid growth mechanism of seed-mediated growth and oriented attachment growth. This microfluidic approach benefits the alignment of the short quantum-confined NWs and promotes their oriented attachment to form long NWs, while conventional flask synthesis results in large and irregular nanorods under the same reaction conditions. This work not only provides a new synthetic path for the preparation of CsPbX₃ NWs but also sheds some light on the fundamental study of CsPbX₃ NWs.

Machine Learning Accelerates Discovery of Optimal Colloidal Quantum Dot Synthesis

Colloidal quantum dots (CQDs) allow broad tuning of the bandgap across the visible and near-infrared spectral regions. Recent advances in applying CQDs in light sensing, photovoltaics, and light emission have heightened interest in achieving further synthetic improvements. In particular, improving monodispersity remains a key priority in order to improve solar cells' open-circuit voltage, decrease lasing thresholds, and improve photodetectors' noise-equivalent power. Here we utilize machine-learning-in-the-loop to learn from available experimental data, propose experimental parameters to try, and, ultimately, point to regions of synthetic parameter space that will enable record-monodispersity PbS quantum dots. The resultant studies reveal that adding a growth-slowing precursor (oleylamine) allows nucleation to prevail over growth, a strategy that enables record-large-bandgap (611 nm exciton) PbS nanoparticles with a well-defined excitonic absorption peak (half-width at half-maximum (hwhm) of 145 meV). At longer wavelengths, we also achieve improved monodispersity, with an hwhm of 55 meV at 950 nm and 24 meV at 1500 nm, compared to the best published to date values of 75 and 26 meV, respectively.

Continuous Flow Methods of Fabricating Catalytically Active Metal Nanoparticles

One of the obstacles preventing the commercialization of colloidal nanoparticle catalysts is the difficulty in fabricating these materials at scale while maintaining a high level of control over their resulting morphologies, and ultimately, their properties. Translation of batch-scale solution nanoparticle syntheses to continuous flow reactors has been identified as one method to address the scaling issue. The superior heat and mass transport afforded by the high surface-area-to-volume ratios of micro- and millifluidic channels allows for high control over reaction conditions and oftentimes results in decreased reaction times, higher yields, and/or more monodisperse size distributions compared to an analogous batch reaction. Furthermore, continuous flow reactors are automatable and have environmental health and safety benefits, making them practical for commercialization. Herein, a discussion of continuous flow methods, reactor design, and potential challenges is presented. A thorough account of the implementation of these technologies for the fabrication of catalytically active metal nanoparticles is reviewed for hydrogenation, electrocatalysis, and oxidation reactions.

Droplet microfluidics: from proof-of-concept to real-world utility?

Droplet microfluidics constitutes a diverse and practical tool set that enables chemical and biological experiments to be performed at high speed and with enhanced efficiency when compared to conventional instrumentation. Indeed, in recent years, droplet-based microfluidic tools have been used to excellent effect in a range of applications, including materials synthesis, single cell analysis, RNA sequencing, small molecule screening, in vitro diagnostics and tissue engineering. Our 2011 Chemical Communications Highlight Article [Chem. Commun., 2011, 47, 1936-1942] reviewed some of the most important technological developments and applications of droplet microfluidics, and identified key challenges that needed to be addressed in the short term. In the current contribution, we consider the intervening eight years, and assess the contributions that droplet-based microfluidics has made to experimental science in its broadest sense.

Fully Inorganic Ruddlesden-Popper Double Cl-I and Triple Cl-Br-I Lead Halide Perovskite Nanocrystals

The vast majority of lead halide perovskite (LHP) nanocrystals (NCs) are currently based on either a single halide composition (CsPbCl₃, CsPbBr₃, and CsPbI₃) or an alloyed mixture of bromide with either Cl^- or I^- [i.e., CsPb(Br:Cl)₃ or CsPb(Br:I)₃]. In this work, we present the synthesis as well as a detailed optical and structural study of two halide alloying cases that have not previously been reported for LHP NCs: Cs₂PbI₂Cl₂ NCs and triple halide CsPb(Cl:Br:I)₃ NCs. In the case of Cs₂PbI₂Cl₂, we observe for the first time NCs with a fully inorganic Ruddlesden-Popper phase (RPP) crystal structure. Unlike the well-explored organic-inorganic RPP, here, the RPP formation is triggered by the size difference between the halide ions. These NCs exhibit a strong excitonic absorption, albeit with a weak photoluminescence quantum yield (PLQY). In the case of the triple halide CsPb(Cl:Br:I)₃ composition, the NCs comprise a CsPbBr₂Cl perovskite crystal lattice with only a small amount of incorporated iodide, which segregates at RPP planes' interfaces within the CsPb(Cl:Br:I)₃ NCs. Supported by density functional theory calculations and postsynthetic surface treatments to enhance the PLQY, we show that the combination of iodide segregation and defective RPP interfaces are most likely linked to the strong PL quenching observed in these nanostructures. In summary, this work demonstrates the limits of halide alloying in LHP NCs because a mixture that contains halide ions of very different sizes leads to the formation of defective RPP interfaces and a severe quenching of LHP NC's optical properties.