Facile Synthesis of Cu-Doped TiO2 Particles for Accelerated Visible Light-Driven Antiviral and Antibacterial Inactivation.

In this work, we present a facile and scalable hydrolysis-based route for the synthesis of copper-doped TiO2 particles for highly effective light-activated antiviral and antibacterial applications. The performance of the synthesized Cu-doped TiO2 particles is then evaluated using solution-phase antimicrobial photodynamic inactivation assays. We demonstrate that the Cu-doped TiO2 particles can successfully inactivate a wide range of pathogens with exposure to light for 90 min, including bacteria ranging from methicillin-resistant Staphylococcus aureus (99.9999%, ∼6 log units) to Klebsiella pneumoniae (99.93%, ∼3.3 log units), and viruses including feline calicivirus (99.94%, ∼3.4 log units) and HCoV-229E (99.996%, ∼4.6 log units), with the particles demonstrating excellent robustness toward photobleaching. Furthermore, a spray-coated polymer film, loaded with the synthesized Cu-doped TiO2 particles achieves inactivation of methicillin-resistant S. aureus up to 99.998% (∼4.8 log units). The presented results provide a clear advance forward in the use of metal-doped TiO2 for aPDI applications, including the scalable synthesis (kg/day) of well-characterized and robust particles, their facile incorporation into a nontoxic, photostable coating that may be easily and cheaply applied to a multitude of surfaces, and a broad efficacy against drug-resistant Gram-positive and Gram-negative bacteria, as well as against enveloped and nonenveloped viruses.

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.

Autonomous nanomanufacturing of lead-free metal halide perovskite nanocrystals using a self-driving fluidic lab.

Lead-based metal halide perovskite (MHP) nanocrystals (NCs) have emerged as a promising class of semiconducting nanomaterials for a wide range of optoelectronic and photoelectronic applications. However, the intrinsic lead toxicity of MHP NCs has significantly hampered their large-scale device applications. Copper-base MHP NCs with composition-tunable optical properties have emerged as a prominent lead-free MHP NC candidate. However, comprehensive synthesis space exploration, development, and synthesis science studies of copper-based MHP NCs have been limited by the manual nature of flask-based synthesis and characterization methods. In this study, we present an autonomous approach for the development of lead-free MHP NCs via seamless integration of a modular microfluidic platform with machine learning-assisted NC synthesis modeling and experiment selection to establish a self-driving fluidic lab for accelerated NC synthesis science studies. For the first time, a successful and reproducible in-flow synthesis of Cs3Cu2I5 NCs is presented. Autonomous experimentation is then employed for rapid in-flow synthesis science studies of Cs3Cu2I5 NCs. The autonomously generated experimental NC synthesis dataset is then utilized for fast-tracked synthetic route optimization of high-performing Cs3Cu2I5 NCs.

Continuous Ligand-Free Catalysis Using a Hybrid Polymer Network Support.

Although the pharmaceutical and fine chemical industries primarily utilize batch homogeneous reactions to carry out chemical transformations, emerging platforms seek to improve existing shortcomings by designing effective heterogeneous catalysis systems in continuous flow reactors. In this work, we present a versatile network-supported palladium (Pd) catalyst using a hybrid polymer of poly(methylvinylether-alt-maleic anhydride) and branched polyethyleneimine for intensified continuous flow synthesis of complex organic compounds via heterogeneous Suzuki-Miyaura cross-coupling and nitroarene hydrogenation reactions. The hydrophilicity of the hybrid polymer network facilitates the reagent mass transfer throughout the bulk of the catalyst particles. Through rapid automated exploration of the continuous and discrete parameters, as well as substrate scope screening, we identified optimal hybrid network-supported Pd catalyst composition and process parameters for Suzuki-Miyaura cross-coupling reactions of aryl bromides with steady-state yields up to 92% with a nominal residence time of 20 min. The developed heterogeneous catalytic system exhibits high activity and mechanical stability with no detectable Pd leaching at reaction temperatures up to 95 °C. Additionally, the versatility of the hybrid network-supported Pd catalyst is demonstrated by successfully performing continuous nitroarene hydrogenation with short residence times (<5 min) at room temperature. Room temperature hydrogenation yields of >99% were achieved in under 2 min nominal residence times with no leaching and catalyst deactivation for more than 20 h continuous time on stream. This catalytic system shows its industrial utility with significantly improved reaction yields of challenging substrates and its utility of environmentally-friendly solvent mixtures, high reusability, scalable and cost-effective synthesis, and multi-reaction successes.

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.

Continuous Room-Temperature Hydrogen Release from Liquid Organic Carriers in a Photocatalytic Packed-Bed Flow Reactor.

Invited for this month's cover is the group of Prof. Dr. Milad Abolhasani at North Carolina State University. The image shows an energy-efficient strategy for on-site and room-temperature hydrogen release from liquid organic hydrogen carriers. The Research Article itself is available at 10.1002/cssc.202200733.

Recyclable cooperative catalyst for accelerated hydroaminomethylation of hindered amines in a continuous segmented flow reactor.

Synthesis of hindered amines using the atom-efficient hydroaminomethylation (HAM) route remains a challenge. Here, we report a general and accelerated HAM in segmented flow, achieved via a cooperative effect between rhodium (Rh)/N-Xantphos and a co-catalyst (2-Fluoro-4-methylbenzoic acid) to increase the reactivity by 70 fold when compared to Rh/Xantphos in batch reactors. The cooperation between Rh and the co-catalyst facilitates the cleavage of the H-H bond and drives the equilibrium-limited condensation step forward. Online reaction optimization expands the scope to include alkyl, aryl, and primary amines. In-flow solvent tuning enables selectivity switching from amine to enamine without the need for changing the ligand. Furthermore, leveraging the ionic nature of the catalyst, we present a robust Rh recovery strategy up to 4 recycles without loss of activity.

Continuous Room-Temperature Hydrogen Release from Liquid Organic Carriers in a Photocatalytic Packed-Bed Flow Reactor.

Despite the potential of hydrogen (H2 ) storage in liquid organic carriers to achieve carbon neutrality, the energy required for H2 release and the cost of catalyst recycling have hindered its large-scale adoption. In response, a photo flow reactor packed with rhodium (Rh)/titania (TiO2 ) photocatalyst was reported for the continuous and selective acceptorless dehydrogenation of 1,2,3,4-tetrahydroquinoline to H2 gas and quinoline under visible light irradiation at room temperature. The tradeoff between the reactor pressure drop and its photocatalytic surface area was resolved by selective in-situ photodeposition of Rh in the photo flow reactor post-packing on the outer surface of the TiO2 microparticles available to photon flux, thereby reducing the optimal Rh loading by 10 times compared to a batch reactor, while facilitating catalyst reuse and regeneration. An example of using quinoline as a hydrogen acceptor to lower the energy of the hydrogen production step was demonstrated via the water-gas shift reaction.

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.

Intensified recovery of switchable hydrophilicity solvents in flow.

We present an integrated flow chemistry strategy using two membrane-based flow reactors to enhance the extraction and recovery rates of switchable hydrophilicity solvents (SHSs) by five times compared to batch reactors. The developed green flow chemistry strategy achieves an overall single-pass recovery efficiency of 60.1% for 2-(dibutyl amino)ethanol.

A Versatile Compact Parahydrogen Membrane Reactor.

We introduce a Spin Transfer Automated Reactor (STAR) that produces continuous parahydrogen induced polarization (PHIP), which is stable for hours to days. We use the PHIP variant called signal amplification by reversible exchange (SABRE), which is particularly well suited to produce continuous hyperpolarization. The STAR is operated in conjunction with benchtop (1.1 T) and high field (9.4 T) NMR magnets, highlighting the versatility of this system to operate with any NMR or MRI system. The STAR uses semipermeable membranes to efficiently deliver parahydrogen into solutions at nano to milli Tesla fields, which enables 1 H, 13 C, and 15 N hyperpolarization on a large range of substrates including drugs and metabolites. The unique features of the STAR are leveraged for important applications, including continuous hyperpolarization of metabolites, desirable for examining steady-state metabolism in vivo, as well as for continuous RASER signals suitable for the investigation of new physics.

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.

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.

Facile Synthesis of a Color-Tunable Microcrystal Phosphor for Anti-Counterfeit Applications.

Developing luminescent materials with tunable emission colors provides exciting opportunities for application in the display, anti-counterfeiting, and optical sensors. Here, we report a convenient, versatile approach to synthesize color-tunable, up/down-conversion luminescence in an inorganic host material. The emission color can be tuned by varying the excitation wavelength, allowing dynamic color tuning in the visible spectrum. We demonstrate that an unprecedented luminescence tunability from these phosphors can be achieved by tailoring the intensity ratio of different emission peaks. These findings provide valuable insights into controlling multiple emission color processes while offering the possibility for dynamic anti-counterfeiting and visual sensing of ultraviolet light in the range from 250 to 320 nm. These results open the opportunity for developing next-generation stimuli-responsive luminescent materials and smart devices.

Accelerating gas-liquid chemical reactions in flow.

Over the past decade, continuous flow reactors have emerged as a powerful tool for accelerated fundamental and applied studies of gas-liquid reactions, offering facile gas delivery and process intensification. In particular, unique features of highly gas-permeable tubular membranes in flow reactors (i.e., tube-in-tube flow reactor configuration) have been exploited as (i) an efficient analytic tool for gas-liquid solubility and diffusivity measurements and (ii) reliable gas delivery/generation strategy, providing versatile adaptability for a wide range of gas-liquid processes. The tube-in-tube flow reactors have been successfully adopted for rapid exploration of a wide range of gas-liquid reactions (e.g., amination, carboxylation, carbonylation, hydrogenation, ethylenation, oxygenation) using gaseous species both as the reactant and the product, safely handling toxic and flammable gases or unstable intermediate compounds. In this highlight, we present an overview of recent developments in the utilization of such intensified flow reactors within modular flow chemistry platforms for different gas-liquid processes involving carbon dioxide, oxygen, and other gases. We provide a detailed step-by-step guideline for robust assembly and safe operation of tube-in-tube flow reactors. We also discuss the current challenges and potential future directions for further development and utilization of tubular membrane-based flow reactors for gas-liquid processes.

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 flow synthesis of phase transition-resistant titania microparticles with tunable morphologies.

Titania microspheres have attracted substantial attention for a variety of applications, including ion scavenging, catalysis, and energy generation, though most synthetic techniques are limited to a few basic morphologies and narrow size ranges. Here, an intensified microfluidic strategy for continuous synthesis of anatase titania microspheres is presented. In-flow photo crosslinking, incorporated with a flow reactor and polar aprotic solvent, enables access to precursor compositions up to an order of magnitude higher than those previously reported, with size tunability approaching two orders of magnitude. Morphological and surface area effects associated with precursor composition are explored, resulting in hollow, yolk-shell, macroporous, and dense titania microspheres containing no detectable rutile phase and possessing surface areas exceeding 350 m2 g-1 post calcination. Furthermore, effects of calcination temperature and time on the surface area, crystallinity and phase composition, and morphology of the synthesized titania microspheres are studied in detail. The synthesized microspheres are shown to remain completely in the anatase phase, even at temperatures up to 900 °C, far beyond the expected phase transition temperature. Thus, the breadth of attainable morphologies, specific surface areas, and phase compositions present a variety of intriguing substrate candidates for such applications as heterogeneous (photo) catalysis, adsorption and ion capture, electrochemistry, and photovoltaics.

A low-cost, non-invasive phase velocity and length meter and controller for multiphase lab-in-a-tube devices.

Opportunities for accessible microfluidic device integration have sharply grown with the rise of readily available lab-in-a-tube strategies. Herein, we present a facile, non-invasive, plug-and-play phase velocity and length measuring strategy for rapid deployment onto tube-based microfluidic systems, enabling quick and accurate residence (reaction) time measurement and tuning. Our approach utilizes inexpensive off-the-shelf optical phase sensors and requires no prior knowledge of the fluid composition or physical properties. Compared to camera-based measurements in fluoropolymer tubing, the optical phase sensor-based technique shows mean absolute percentage errors of 1.3% for velocity and 3.3% for length. Utilizing the developed multiphase flow monitoring technique, we screen the accessible parameter space of gas-liquid segmented flows. To further demonstrate the functionality of this process monitoring strategy, we implement two feedback controllers to establish simultaneous setpoint control for phase velocity and length. Next, to showcase the effectiveness and versatility of the developed multiphase flow process controller, we apply it to systematic studies of the effect of liquid slug velocity (controlling precursor mixing timescale) on the colloidal synthesis of cesium lead tribromide nanocrystals. By varying the liquid slug velocity and maintaining constant precursor composition, liquid slug length, and residence time, we observe a bandgap tunability from 2.43 eV (510 nm) to 2.52 eV (494 nm).

Flow chemistry-enabled studies of rhodium-catalyzed hydroformylation reactions.

We present an automated microscale flow chemistry platform for rapid performance evaluation of continuous and discrete reaction parameters in homogeneous hydroformylation reactions. We demonstrate the versatility of the developed microfluidic platform through a systematic study of the effects of a library of phosphine-based ligands on catalytic activity and regioselectivity.

A Modular Microfluidic Technology for Systematic Studies of Colloidal Semiconductor Nanocrystals.

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.