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. 2023 Feb 14;145(8):4355–4365. doi: 10.1021/jacs.2c13670

Quid Pro Flow

Andrea Laybourn †,*, Karen Robertson †,*, Anna G Slater ‡,*
PMCID: PMC9983017  PMID: 36786813

Abstract

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How do you get into flow? We trained in flow chemistry during postdoctoral research and are now applying it in new areas: materials chemistry, crystallization, and supramolecular synthesis. Typically, when researchers think of “flow”, they are considering predominantly liquid-based organic synthesis; application to other disciplines comes with its own challenges. In this Perspective, we highlight why we use and champion flow technologies in our fields, summarize some of the questions we encounter when discussing entry into flow research, and suggest steps to make the transition into the field, emphasizing that communication and collaboration between disciplines is key.

Introduction

Flow chemistry is a broad, interdisciplinary, and evolving field. The area has its roots in chemical engineering1,2 and industrial processes from the beginning of modern chemical production: e.g., blast furnaces, oil refining, and the Haber process. By comparison, flow chemistry in the research lab is a relatively recent development with very different challenges;3 new entrants into the field benefit from a long tradition of research,4,5 but it can be a challenging or expensive technique to enter into.

Reviews611 and text books1215 offer invaluable help, especially in fields where extensive examples of the benefits are available: e.g., organic synthesis,16 the pharmaceutical industry,1719 and, more recently, nanoparticle synthesis.20 Despite this, there are still barriers in terms of skills and knowledge gaps, disciplinary differences, and access to technology that prevent widespread use of flow in chemistry lab settings, particularly in fields where it is not an established technique.

Here, we have addressed those barriers and preconceptions, seeking to demystify the subject and ease the transition to flow. Throughout, we point to resources, literature, and strategies to assist the new user of the technique.

In this Perspective, which does not seek to be exhaustive, we chiefly discuss lab-scale flow processes, with a focus on solution-based chemistry for synthesis and crystallization. We have excluded areas such as continuous mechanochemistry (e.g., twin screw extrusion21) and pilot-plant continuous processes, large areas of research in their own right.

Terminology and First Steps

A shared vocabulary is an important foundation for collaboration between disciplines; conversely, a lack of clear definitions or multiple words being used to mean similar things can be a barrier to entry. There is a myriad of contrasting and nuanced terminology used in flow chemistry, in fact, even the field itself is called different things and invokes many disciplines: continuous flow, process engineering, lab-on-a-chip, microfluidics, millifluidics, microreactors, and more. Flow chemistry also takes strong elements from, e.g., reactor design, fluid dynamics, process engineering, etc.; specific terms in use will differ depending on the training and background of the person using them. To alleviate this, there are several reviews aimed at the new flow chemist,11,22,23 many of which include explanations of key terminology, e.g., steady state, residence time and residence time distribution, mass and heat transfer, and whether an analytical technique is described as being used offline, in-/online, or at-line. Establishing a shared vocabulary and how terms are used is a sensible starting point at the initiation of a new research partnership.

The first question is often: “should flow be used at all?” Reviews aimed at answering when one should “go with the flow” offer a helpful guide.24 Many reactions progress entirely satisfactorily in a round-bottomed flask (RBF); if that is the case, the RBF is the easier solution. However, there are many examples where selectivity, yield, reproducibility, or scale is limited in a batch vessel. Is initial mixing important, or is temperature control critical? Is the reaction time scale too long, or is one of the intermediates highly unstable or hazardous?25 Would inline analysis give new information about a process?26 In these cases, the challenges in developing flow chemistry skills could well be worth it (Figure 1).

Figure 1.

Figure 1

Potential benefits of flow.

Unlike more common laboratory procedures such as Schlenk line techniques, flow has a somewhat deserved reputation as a technique that takes a long time to perfect. The question here is which skills are required to be a successful flow chemist? It may not be obvious how much engineering knowledge is required to construct and use flow equipment or that a full appreciation of fluid dynamics is not always necessary to interpret the results of an experiment. Manufacturers of commercial equipment have attempted to reduce this skills barrier by developing “plug-and-play” systems that are robust—for example, designed for undergraduate education—but these still require knowledge of steady state, mass and heat transfer, and, of course, reaction design and optimization strategies in the context of flow. However, a brief introduction to these key concepts, undergraduate level physical chemistry, and knowledge of the chemical system under study is enough to start predicting and testing whether flow is likely to bring benefits to a given reaction process.

There is no substitute for learning from people who are experienced in the area, whether through lecture courses, practical training, or research exchanges. Predesigned flow chemistry experiments targeted at undergraduates have been developed and are a useful component of new researchers’ training.2729 However, many users will only start in flow at the postgraduate level: several flow-specific training courses are now available to meet this need. For example, in the UK, the Dial-a-Molecule Network30 has offered graduate students and industrial researchers a week-long residential Summer School which includes flow chemistry, reaction design, and 3D printing, among other skills. Likewise, the Flow Chemistry Society, headquartered in Switzerland but active globally, offers events, courses, and textbooks.

As the skills required by graduate chemists change, it is likely that more universities will offer flow chemistry as part of undergraduate lab training, as some centers are already doing. Finally, research visits are a critical part of developing the flow-user ecosystem: all of us benefited from visits to established flow laboratories for proof-of-concept projects, funded by travel and small research grants. Skills in flow chemistry, however, depend on the availability of equipment and tools to apply them; it is more common to see flow equipment in chemistry departments, but this is still far from the standard.

Equipping the Lab

Once the question of “should” flow be used is answered, and the required training has been acquired, we turn to more practical questions. There is a bewildering array of choices for equipping a flow lab–or even setting up a first system. Navigating these choices is time-consuming and potentially off-putting to the new user.

The first important consideration is the use case: a system primarily designed for education or an introduction to flow will have dissimilar needs than a system for a single class of reaction or one designed to be as flexible as possible. Instructions for building a flow pathway from constituent parts are available31 or even the components themselves, such as 3D printed syringe pumps32 or microfluidic chips.33 In terms of control systems, various approaches have been used to build automatable flow reactors, with authors often publishing code that can be adapted for use in different settings.3438

Again, in the initial stages, dialogue with experts in the field is the best option to avoid a long and potentially expensive period of trial-and-error. Manufacturers of flow equipment are increasingly publishing case studies and application notes which can be extremely helpful to illustrate common problems and strategies to overcome them. There are also several online resources available to aid flow chemists, such as calculators, resources for drawing flow processes,39 video demonstrations, and beyond.

Although there are many strategies to transfer a process from batch to flow, depending on the specific challenges of each process, and the eventual solutions may be quite different, there are several common steps that most flow chemists will go through that are a great starting point for each process. Likewise, there are common challenges—such as pump selection and maintenance,40 hardware communication, slurry delivery,41 sampling frequency, fouling42 and blockages, cleaning and troubleshooting43—that are part of most flow chemists’ experience, discussed in more detail below.

Taking a Process into Flow

A flow chemist with a batch process that they want to improve by translating into flow will consider a series of questions; the order may vary, but common themes will appear (Figure 2). Among the early things to evaluate are the hypothesis to be tested and what the goal is—or conversely, what the issues are with the batch process. For example, does the process involve a hazardous intermediate that needs inline quenching? Does the process scale poorly in batch? Is it highly exothermic and requires careful heat management? Is yield low and likely to be increased by accessing elevated temperatures? Is the selectivity currently limited by the mixing regime accessible in the batch vessel? These all set the scene and allow the flow chemist to start sketching out the equipment and priority considerations for the flow pathway design.

Figure 2.

Figure 2

Scheme showing key questions and underpinning practical considerations when taking a process into flow.

Once the reaction safety, success measures, and, ideally, kinetic information have been understood, we turn to practical considerations. The core steps possible in lab scale flow synthesis are solution delivery, mixing, “reaction”, workup, purification, analysis, and collection.22 Several choices underpin these steps, from pump selection, tubing choice, reactor design, and flow pathway planning to telescoping, reaction optimization, and analysis.

It is important to establish the phase or phases present in the system: is the process in the liquid phase and homogeneous throughout, or are solids or gases present or generated at any point? A slurry delivery rules out piston pumps in favor of peristaltic pumps; the production of gases or solvents above their boiling points necessitates the use of back-pressure regulators to maintain steady state conditions and could require a gas/liquid separator in the flow pathway. Equally important for pump choice is the viscosity, chemical compatibility, and hazards of the reagents and required flow rates and scales.

Next, the flow chemist may consider the mixing regime required for the chemistry under study.44 Ultimately, the rate limiting step for your process should be identified and characterized in terms of the Damköhler number,45 a useful ratio for determining whether the process is diffusion limited. If the process is diffusion limited then high intensity immediate mixing is essential: e.g., micromixers could be used. If the process is not diffusion limited, then simple tee/T-pieces are sufficient. In some cases, it may even be beneficial to have very slow or inefficient mixing or use phase separation to avoid high-dilution and/or effect “slow addition” techniques commonly used, e.g., for macrocycle synthesis.46,47

The choice of downstream mixing options depends on whether different viscosities or phases are present, and how best the desired flow rate to mixing ratio can be achieved; as flow rates increase, so too does mixing intensity. Downstream mixing is typically achieved through serpentine bends imparting Dean vortices in microreactors or static mixers, e.g., Kenics or SMX,48 in millireactors. Oscillatory flow either through baffles49 or combined with static mixing elements50 can also help impart high intensity mixing, whereas Taylor or segmented flow generates plug flow (essentially creating microbatch reactors) with varied mixing intensity dependent on tubing/channel size.51,52

Next, the question is how to impart the required energy for the process into the system. Many different methods of heating are available to control reaction rate or to enable selective heating: conventional heating baths or blocks now sit alongside photochemical,5356 electrochemical,57 microwave,49,58,59 or sonochemical60 or inductive heat61 reactors. Here, the key is ensuring that the technology is appropriately integrated, and all significant parameters are considered: the principles differ based on the type of technology and cannot be generalized. For example, in mechanochemical methods, the geometry of the twin screw extruder is a key consideration, whereas in photocatalysis the reactor should be constructed from materials that allow maximum and uniform light energy transfer. For microwave flow synthesis, key considerations are quantifying the dielectric properties of the reaction mixture and subcomponents with the electromagnetic field (including the reactor itself!) and the electric field distribution as this dictates the power density in the heated phases of the reaction mixture. When working with immobilized reagents such as enzymes,62 solid-supported catalysts,63 or solid reagents, column packing, pressure drop, the avoidance of “hot zones”, and material surface area are important considerations. Again, collaborating with experts in each technology is recommended to get up to speed quickly.

Wherever possible, the overall efficiency and sustainability of the flow process should be considered from the beginning. In flow, waste minimization is achieved by coupling unit operations, enhanced mass and heat transfer kinetics, and intensified mixing leading to improved process control, conversion efficiencies, and higher yields.64,65 Further waste minimization can be accomplished by reusing and recycling, for example, unreacted starting materials and solvent from synthesis and purification steps.66 The energy efficiency of a process is enhanced by flow compared to batch through reduced reaction times and operating under steady state conditions. Energy may also be saved by heat recovery, for example, in exothermic reactions.67 Alternative sustainable approaches include using “greener” solvents such as supercritical carbon dioxide68 or the use of alternative energy sources, as discussed above. Both microwave and photochemical methods offer significantly reduced energy consumption; photochemical methods can use freely available sunlight to power chemical reactions,69 whereas microwave technology has the potential to be operated using sustainable electricity obtained from renewable sources including solar, wind, or hydro- and bioenergy. Opportunities also exist for reactions to be driven by electrochemical57 and even nonthermal plasma methods.7072

Post-reaction there may be a need for work up steps, especially for telescoped reactions—that is, directly going from one reaction to another within a single flow process. A range of workup steps have evolved to facilitate this including liquid-liquid (or gas-liquid) extraction, inline or online column chromatography,73,74 microcrystallization,75 microdistillation,76 counter-current extraction,77 and nanofiltration/dialysis.78 Finally, process safety is improved though reduced reactor volumes and control (e.g., relief valves) and monitoring systems and through the ability to produce then consume hazardous intermediates without the need for separation.

Arguably one of the greatest assets of flow chemistry is the ability to have inline and online analysis.26 Here, inline denotes that the analysis is taken within the flow pathway such that all process fluid passes the analysis point; online implies a sample loop such that only a portion of the process fluid is analyzed. Online techniques are typically used if sample prep, such as dilution, is required prior to analysis (e.g., ultra-high performance liquid chromatography, UHPLC, or dynamic light scattering, DLS). By moving the analytical probe or having multiple analysis points, reaction kinetics or pathway can be elucidated, given that flow path length = time under continuous conditions.79 As analytical chemistry offers increasingly rapid and sensitive measurements, more techniques are being incorporated into flow processes: LC,80 GC,81,82 MS,83,84 particle sizing,85 IR,86,87 optical methods,88 and NMR89,90 are more established, but more recently PXRD,91,92 optical emission spectroscopy,93 SAXS/WAXS,9496 3D microscopy,97 magnetometry,98 and even single crystal XRD99 have been reported as inline or online methods. In each case, the method must be optimized for the desired analyte, concentration, and sensitivity required.100

At the end of the process, there are collection options to consider: for an accurate comparison of batch vs flow, it is important to quench at the end of the reactor to ensure the reaction is only in progress in the flow pathway and not in the collection vessel. It can be important to confirm that steady state conditions have been reached, particularly if the solution has complex or changing rheological characteristics, and to adjust collection parameters accordingly. Autosamplers can be used as fraction collectors to queue up and collect multiple sequential flow experiments or sample multiple time-points during a single flow experiment and can even be used to integrate with other hardware for automated analysis.

One oft-cited barrier to the use of flow is the handling of solids: this is such a prevalent issue that it merits further discussion in its own section.

Solids–The Nemesis of the Flow Chemist?

The preconception that “solids mean flow is impossible” may stop such researchers exploring further. It is true that solid handling in flow is a challenge, but there are many engineering solutions that can address this challenge, e.g., slurry handling techniques,101 continuous mechanochemistry,21,102,103 and continuous stirred tank reactors.104 The key question is whether the engineering solution proposed fits the problem in terms of advantages, cost, and the time it takes, the particle size/loading required, and the environmental impact and resources. Often, the simplest strategy is to identify conditions, solvent choices, or temperature or concentration regimes where everything is kept in solution.

If a solid cannot be avoided, or is indeed desired, there are several common strategies to ease the handling of slurries. Such strategies include avoidance of pinch points and narrow tubing, e.g., using peristaltic pumps as back-pressure regulators, avoiding right-angle turns in tubing or connectors, or using wide-bore fittings. Flow pathways can also incorporate measures to homogenize particles, such as inline sonicators105,106 or using agitated reactors.107 Careful selection of pumps and tubing is required, for example using peristaltic91 rather than piston pumps that cannot tolerate particles. Finally, cleaning cycles108 can be essential to manage fouling. A combination of all these strategies may be required to arrive at a robust method for a flow process requiring slurry delivery, and it may take time to arrive at a functional solution. However, solid handling poses problems for all chemists, not just those using flow; using flow methods to move a slurry may be an easier and safer option than handling a static, hygroscopic, or highly hazardous powder.

Despite the challenges of solid handling, there are also great advantages in using flow for those deliberately seeking to make bespoke solid particles with a high degree of control. The area of flow crystallization addresses many of these issues with a focus on the control of actively precipitating material.109 The use of solids as a slurry which are in steady state—that is, neither crystallizing nor dissolving—can be achieved using the strategies highlighted above. During flow crystallization as an active process, there are greater challenges such as encrustation110 (i.e., nucleation and growth of particles on the reactor wall), crystal growth control,111 and filtration.112

Control over how and where material nucleates will prevent unwanted nucleation on the walls of the reactor, which, if unchecked, will lead to fouling, back-mixing, and eventually blockage. The use of antisolvent addition,113 rapid cooling,114 or sonication115 to force material to nucleate can be highly effective. Flow-focusing geometries can direct the location of nucleation away from the reactor walls, further ameliorating encrustation, particularly in microreactors116,117 (Figure 3).

Figure 3.

Figure 3

Schematic representation of flow within a range of crystallizers that may be used to improve suspension and mitigate encrustation of particles on reactor walls. White arrows show fluid pathways, solution in green, secondary fluids in blue/white: a. segmented flow, b. continuous oscillatory baffled reactor, c. kenics-type static mixer, d. moving insert (vertical motion of insert denoted by black arrow), e. flow-focused/sheath flow.

Once nucleated, crystal growth can be controlled though temperature gradients118,119 or further addition of antisolvent and mixing or suspension of solids.120 The most basic downstream mixing setup is a continuous or cascade stirred tank reactor,104 which uses a series of stirred tank reactors with an impeller to effect mixing. Plug flow reactors typically employ a variety of static mixers to ensure good mass transfer, reducing agglomeration and improving suspension of solids. Inserts or 3D printed reactors with inbuilt static mixers can help suspend and mix slurries with optimal operation directly linked to flow rate.121 Oscillatory baffled reactors (OBRs)122 comprise a series of single- or multi-orifice baffles and typically use piston-driven oscillatory flow to generate eddies in the reactor.123 A net flow then drives the crystallizing material through the tubing. A combination of oscillation amplitude and frequency and net flow rate provide optimal suspension and plug flow.

Segmented or Taylor flow uses immiscible fluids to separate the crystallizing solution into discrete droplets or “slugs” which mix as a function of flow rate and slug size through bolus flow without contact to a solid surface.124,125 Removing contact with the walls inherently prevents encrustation but can raise issues with compatibility of fluids and filtration due to capillary forces of the carrier fluid on the solid particles.

The ideal flow crystallization setup for a given application is a question of particle size, density, and throughput. Nanoparticle crystallization typically requires microreactors to effect the degree of mass transfer control necessary to produce smaller and homogeneous particles.126 Due to the small size, blockages with nanoparticles is unlikely but encrustation may be an issue. If particles adhere to the reactor walls, they are likely to grow larger and influence passing growing particles, affecting the particle size distribution. The crystallizer material and surface roughness can therefore be an important decision for all crystallizers, regardless of target size. Traditional flow chip reactors are well-suited to this type of crystallization.127 Sub-micrometer sized particles may be more likely to block reactors especially due to agglomeration. Larger, millifluidic reactor parts may therefore be more appropriate, e.g., ca. 1 mm internal diameter (ID). These work well with static mixers, small OBRs, and tightly coiled reactor tubing. Milli–macrosized crystallizations, such as small molecule organics, require careful crystallizer planning as the large size makes them very likely to block.128,129 Larger static mixer reactors, OBRs, and millisized tubing (ca. 3 mm ID) are necessary to prevent blockages with particular care taken for the joints of reactor parts.

Taken together, it is clear that a plethora of engineering solutions have been developed to transfer a process into flow, yet there are still many challenges to be solved.

Is There Room for Innovation?

Proposing to use flow in research settings sometimes raises a different question: “has this all been done before?” Here, the preconception is that because flow chemistry is firmly established in some areas, there is nothing new to be found.

Indeed, off-the-shelf flow systems and analytical kits are excellent for well-established flow regimes and reaction types; here, the novelty lies in finding new uses for established tools. However, for those focusing on atypical flow (such as venturi mixers, split-recombine or segmented flow) or demanding processes (such as extreme or changing viscosities,130 active precipitation of solids, heterogeneous media, or complex temperature gradients)118,131,132 these standard set-ups may not be appropriate. Finally, although membrane reactors and packed drying columns can help remove water, there are limited options for a flow equivalent of a Dean–Stark adaptor to help drive condensation reactions.133 These challenges present enormous opportunities for research and development, and as new tools are becoming more readily available, it becomes easier to transfer from batch to flow.

Another area of potentially rapid development is the breadth of chemistries that are performed in flow. Traditionally focused on organic synthesis and small molecules, there are now increasing reports of more complex systems being synthesized in flow including supramolecular46,84,134,135 and macromolecular materials.37,90,136139 The opportunities that flow offers for enhanced safety and process control also particularly benefit situations where the reagents or transient species are hazardous (e.g., diazonium salts, organometallics, hydrogenation, and fluorination).25,140143

Other major areas of development include automation, real-time inline analysis, and self-optimization, which enables kinetic studies144 and rapid screening of reaction and materials space.145,146 The potential insight these areas offer for robust discovery and deep understanding of reactions and materials assembly has caught the attention of many newcomers to the field.

Automation in chemistry is a rapidly developing field that is predicted to transform how research is carried out across all areas of science.147150 Flow chemistry is perhaps uniquely suited to benefit from automated methods due to the time resolution and sequential nature of steps in a flow pathway. For example, commercial and custom-build flow platforms are commonly linked with liquid handlers for autosampling151 and fraction collection, and pumps are readily controlled by code to automate reaction sequencing, dilution,152 screening,153 and trigger analysis.84 A major selling point of moving to flow is the ability to set up a reaction sequence that screens several parameters while the operator, who can be working entirely remotely,154 is freed up to work on other tasks.

A clear benefit of automation is the ability to handle extremely large data sets to rapidly understand and optimize processes. Here, autonomous algorithms are powerful tools to navigate process space and identify ideal conditions in terms of reaction yield, selectivity, throughput, or efficiency.36,37,145,151,155163 “Autonomous reactors” in combination with multifactor optimization have been used to identify flow conditions that are both high yielding and efficient in terms of feedstock use, important in translating lab conditions to process scale.164

Optimization algorithms are only effective if robust analytical methods are available for a given system. Here, flow has another advantage: the real-time data that can be collected via inline analysis.100 Typically, such measurements are taken under steady state conditions to allow for stable measurement. However, methods have been recently reported that use transient flow measurements without reaching steady state; parameters are continually varied to rapidly explore the impact of, e.g., temperature on kinetics.165168 This powerful technique dramatically shortens the experimental time needed to extract useful information about a process, resulting in many data points from a single run.90

Sensitive analytical methods have also enabled extremely high-throughput droplet screening in flow,169,168170 only possible because robust MS data can now be obtained from nanogram quantities. Combining such experiments with autonomous algorithms offers an extremely powerful route to reaction screening and optimization;171 here, the backlog becomes data processing and, potentially, generating new hypotheses to test. It will also become increasingly important to train chemists in the skills required for such techniques: coding, data processing, and automation, as well as hardware integration, machine learning approaches, and the limits of these methods.

The increased process windows, reproducibility, and ability to access transient species available in flow coupled with the opportunity for extremely data-rich screening offers a gold-mine of information about the process under study—more than justifying the time and training it takes to develop flow chemistry skills.

Where Next for Flow?

Alongside the future opportunities discussed, a critical challenge for all chemists is to move away from unsustainable methods of working. Continuous flow reactors can be employed to address principles from process intensification,172 green chemistry,173 and the circular economy model174 such as minimizing waste and/or emissions, energy efficiency, and process safety with the aim of reducing environmental burden. Here, it is important to assess the entire process to quantify the overall environmental impact and to make an informed choice of whether to switch a given process to flow.

There are also enormous opportunities for computational methods to be used at all stages of flow chemistry, from design to discovery to optimization, e.g., predicting complex synthetic targets that are then isolated, optimized, and scaled up in flow. For example, data mining and machine learning have been used to rationalize and accelerate MOF discovery in batch;175,176 transferring such approaches to flow would potentially vastly increase the chemical space, data intensity, and scalability of the process. To reach the full potential of flow discovery, there is a need for analytical methods coupled with deep learning algorithms that can interface with harsh and/or complex reaction conditions (particularly multiphase reactions) to analyze and make decisions more rapidly under continuous conditions.

Here, again, the need to collaborate with people across disciplines with differing expertise is apparent: e.g., process chemists, chemical engineers, computer scientists, mathematicians, data scientists, equipment developers and, of course, people with expertise in the types of chemistries being conducted.

Conclusions

The field of flow chemistry has developed from engineering and retains a strong flavor of industrial research that is perhaps unusual in chemical research communities. Because of that history, chemists may feel barriers to entry in unfamiliar terminology, differing training philosophies, or even how conferences are badged or advertised. However, diversity of mindsets, experiences, and backgrounds is a strength that pushes the field forward, and that relies on welcoming people into the community.

When developers of new technology work in partnership with end users, the feedback and iteration cycle can be extremely rapid. The fast adoption and proliferation of technology can be seen in a number of settings across chemistry and is true for flow too: for example, flow electrochemistry, photochemistry, inline analysis, automation, and autonomous optimization are all developing at a fast pace.

Cross-disciplinary communication is key. We are from very different disciplines and use flow in different ways for different reasons, but the most fruitful discussions come from understanding each other’s perspectives to shed new light on the challenges we face, which are often shared. As more researchers exploit flow, we look forward to seeing the expansion and development of the field into new spaces, and training new generations to benefit from the many opportunities on offer—paying forward the help we have received to join the flow community ourselves and helping to develop flow as a central technique for chemistry.

Acknowledgments

We thank Dr. Alex Padgham for useful discussions and input for Figure 2. We gratefully acknowledge the people whose invaluable support allowed us to make the transition into flow chemistry, including Prof. Harris Makatsoris, Prof. Steven V. Ley, Dr. Claudio Battilocchio, Dr. Ricardo Labes, Duncan Guthrie, Dr. Ryan Skilton, Dr. Chris Butters, Prof. Richard A. Bourne, Prof. Dr. ing. Timothy Noël, Prof. Jan Sefcick, Dr. Victor Sans, Martyn Fordham, Tony Tang, Joseph M. Charney, the EPSRC CMAC Future Manufacturing Hub, the EPSRC Dial-a-Molecule and Directed Assembly Networks, and many more.

A.G.S. gratefully acknowledges funding from a Royal Society University Research Fellowship (URF\R1\201168).

The authors declare no competing financial interest.

References

  1. Tokaty G. A.A history and philosophy of fluid mechanics; Dover: New York, 1994. [Google Scholar]
  2. Douglas J. F.Fluid mechanics. 6th ed.; Prentice Hall: Harlow, England; New York, 2011. [Google Scholar]
  3. May S. A. Flow Chemistry, Continuous Processing, and Continuous Manufacturing: A Pharmaceutical Perspective. J. Flow Chem. 2017, 7 (3), 137–145. 10.1556/1846.2017.00029. [DOI] [Google Scholar]
  4. Goršek A.; Glavič P. Design of Batch Versus Continuous Processes: Part I: Single-Purpose Equipment. Chem. Eng. Res. Des. 1997, 75 (7), 709–717. 10.1205/026387697524209. [DOI] [Google Scholar]
  5. Löwe H.; Ehrfeld W. State-of-the-art in microreaction technology: concepts, manufacturing and applications. Electrochim. Acta 1999, 44 (21), 3679–3689. 10.1016/S0013-4686(99)00071-7. [DOI] [Google Scholar]
  6. Webb D.; Jamison T. F. Continuous flow multi-step organic synthesis. Chem. Sci. 2010, 1 (6), 675–680. 10.1039/c0sc00381f. [DOI] [Google Scholar]
  7. Wegner J.; Ceylan S.; Kirschning A. Flow Chemistry - A Key Enabling Technology for (Multistep) Organic Synthesis. Adv. Synth. Catal. 2012, 354 (1), 17–57. 10.1002/adsc.201100584. [DOI] [Google Scholar]
  8. Baxendale I. R. The integration of flow reactors into synthetic organic chemistry. J. Chem. Technol. & Biotechnol. 2013, 88 (4), 519–552. 10.1002/jctb.4012. [DOI] [Google Scholar]
  9. Darvas F.; Gyorgy D.. Fundamentals of flow chemistry; Walter de Gruyter GmbH: 2014; pp 9–58. [Google Scholar]
  10. Sans V.; Cronin L. Towards dial-a-molecule by integrating continuous flow, analytics and self-optimization. Chem. Soc. Rev. 2016, 45 (8), 2032–2043. 10.1039/C5CS00793C. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Plutschack M. B.; Pieber B.; Gilmore K.; Seeberger P. H. The Hitchhiker’s Guide to Flow Chemistry. Chem. Rev. 2017, 117 (18), 11796–11893. 10.1021/acs.chemrev.7b00183. [DOI] [PubMed] [Google Scholar]
  12. Flow Chemistry - Fundamentals, 2nd ed.; Darvas F., Dormán G., Hessel V., Ley S. V., Eds.; De Gruyter: 2021. [Google Scholar]
  13. Flow Chemistry - Applications, 2nd ed.; Darvas F., Dormán G., Hessel V., Ley S. V., Eds.; De Gruyter: 2021. [Google Scholar]
  14. Glasnov T.Continuous-flow chemistry in the research laboratory: modern organic chemistry in dedicated reactors at the dawn of the 21st century; Springer: Berlin, Heidelberg, New York, 2016. [Google Scholar]
  15. Sharma U. K.; Van der Eycken E. V.. Flow Chemistry for the Synthesis of Heterocycles. Topics in Heterocyclic Chemistry, [Online] 1st ed.; Springer, 2018; Vol. 56. [Google Scholar]
  16. Pastre J. C.; Browne D. L.; Ley S. V. Flow chemistry syntheses of natural products. Chem. Soc. Rev. 2013, 42 (23), 8849–8869. 10.1039/c3cs60246j. [DOI] [PubMed] [Google Scholar]
  17. Porta R.; Benaglia M.; Puglisi A. Flow Chemistry: Recent Developments in the Synthesis of Pharmaceutical Products. Org. Process Res. Dev. 2016, 20 (1), 2–25. 10.1021/acs.oprd.5b00325. [DOI] [Google Scholar]
  18. Gutmann B.; Cantillo D.; Kappe C. O. Continuous-Flow Technology—A Tool for the Safe Manufacturing of Active Pharmaceutical Ingredients. Angew. Chem., Int. Ed. 2015, 54 (23), 6688–6728. 10.1002/anie.201409318. [DOI] [PubMed] [Google Scholar]
  19. Cole K. P.; Johnson M. D. Continuous flow technology vs. the batch-by-batch approach to produce pharmaceutical compounds. Expert Rev. Clin. Pharm. 2018, 11 (1), 5–13. 10.1080/17512433.2018.1413936. [DOI] [PubMed] [Google Scholar]
  20. Długosz O.; Banach M. Inorganic nanoparticle synthesis in flow reactors - applications and future directions. React. Chem. Eng. 2020, 5 (9), 1619–1641. 10.1039/D0RE00188K. [DOI] [Google Scholar]
  21. Crawford D. E.; Miskimmin C. K. G.; Albadarin A. B.; Walker G.; James S. L. Organic synthesis by Twin Screw Extrusion (TSE): continuous, scalable and solvent-free. Green Chem. 2017, 19 (6), 1507–1518. 10.1039/C6GC03413F. [DOI] [Google Scholar]
  22. Guidi M.; Seeberger P. H.; Gilmore K. How to approach flow chemistry. Chem. Soc. Rev. 2020, 49 (24), 8910–8932. 10.1039/C9CS00832B. [DOI] [PubMed] [Google Scholar]
  23. McQuade D. T.; Seeberger P. H. Applying Flow Chemistry: Methods, Materials, and Multistep Synthesis. J. Org. Chem. 2013, 78 (13), 6384–6389. 10.1021/jo400583m. [DOI] [PubMed] [Google Scholar]
  24. Hartman R. L.; McMullen J. P.; Jensen K. F. Deciding whether to go with the flow: evaluating the merits of flow reactors for synthesis. Angew. Chem., Int. Ed. 2011, 50 (33), 7502–19. 10.1002/anie.201004637. [DOI] [PubMed] [Google Scholar]
  25. Movsisyan M.; Delbeke E. I. P.; Berton J. K. E. T.; Battilocchio C.; Ley S. V.; Stevens C. V. Taming hazardous chemistry by continuous flow technology. Chem. Soc. Rev. 2016, 45 (18), 4892–4928. 10.1039/C5CS00902B. [DOI] [PubMed] [Google Scholar]
  26. Sagmeister P.; Lebl R.; Castillo I.; Rehrl J.; Kruisz J.; Sipek M.; Horn M.; Sacher S.; Cantillo D.; Williams J. D.; Kappe C. O. Advanced Real-Time Process Analytics for Multistep Synthesis in Continuous Flow. Angew. Chem., Int. Ed. 2021, 60 (15), 8139–8148. 10.1002/anie.202016007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. König B.; Kreitmeier P.; Hilgers P.; Wirth T. Flow Chemistry in Undergraduate Organic Chemistry Education. J. Chem. Educ. 2013, 90 (7), 934–936. 10.1021/ed3006083. [DOI] [Google Scholar]
  28. Leibfarth F. A.; Russell M. G.; Langley D. M.; Seo H.; Kelly L. P.; Carney D. W.; Sello J. K.; Jamison T. F. Continuous-Flow Chemistry in Undergraduate Education: Sustainable Conversion of Reclaimed Vegetable Oil into Biodiesel. J. Chem. Educ. 2018, 95 (8), 1371–1375. 10.1021/acs.jchemed.7b00719. [DOI] [Google Scholar]
  29. Kuijpers K. P. L.; Weggemans W. M. A.; Verwijlen C. J. A.; Noël T. Flow chemistry experiments in the undergraduate teaching laboratory: synthesis of diazo dyes and disulfides. J. Flow Chem. 2021, 11 (1), 7–12. 10.1007/s41981-020-00118-1. [DOI] [Google Scholar]
  30. Kilpin K. J.; Whitby R. J. Chemistry Central Journal themed issue: Dial-a-Molecule. Chem. Cent. J. 2015, 9, 43. 10.1186/s13065-015-0122-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Britton J.; Jamison T. F. The assembly and use of continuous flow systems for chemical synthesis. Nat. Protoc. 2017, 12 (11), 2423–2446. 10.1038/nprot.2017.102. [DOI] [PubMed] [Google Scholar]
  32. Baas S.; Saggiomo V. Ender3 3D printer kit transformed into Open, Programmable Syringe Pump set. HardwareX 2021, 10, e00219 10.1016/j.ohx.2021.e00219. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Waheed S.; Cabot J. M.; Macdonald N. P.; Lewis T.; Guijt R. M.; Paull B.; Breadmore M. C. 3D printed microfluidic devices: enablers and barriers. Lab Chip 2016, 16 (11), 1993–2013. 10.1039/C6LC00284F. [DOI] [PubMed] [Google Scholar]
  34. Sumpter B. G.; Hong K.; Vasudevan R. K.; Ivanov I.; Advincula R. Autonomous Continuous Flow Reactor Synthesis for Scalable Atom-Precision. Carbon Trends 2023, 10, 100234. 10.1016/j.cartre.2022.100234. [DOI] [Google Scholar]
  35. Cortés-Borda D.; Wimmer E.; Gouilleux B.; Barré E.; Oger N.; Goulamaly L.; Peault L.; Charrier B.; Truchet C.; Giraudeau P.; Rodriguez-Zubiri M.; Le Grognec E.; Felpin F.-X. An Autonomous Self-Optimizing Flow Reactor for the Synthesis of Natural Product Carpanone. J. Org. Chem. 2018, 83 (23), 14286–14299. 10.1021/acs.joc.8b01821. [DOI] [PubMed] [Google Scholar]
  36. Jeraal M. I.; Sung S.; Lapkin A. A. A Machine Learning-Enabled Autonomous Flow Chemistry Platform for Process Optimization of Multiple Reaction Metrics. Chemistry-Methods 2021, 1 (1), 71–77. 10.1002/cmtd.202000044. [DOI] [Google Scholar]
  37. Rubens M.; Vrijsen J. H.; Laun J.; Junkers T. Precise Polymer Synthesis by Autonomous Self-Optimizing Flow Reactors. Angew. Chem., Int. Ed. 2019, 58 (10), 3183–3187. 10.1002/anie.201810384. [DOI] [PubMed] [Google Scholar]
  38. Kehl F.; Cretu V. F.; Willis P. A. Open-source lab hardware: A versatile microfluidic control and sensor platform. HardwareX 2021, 10, e00229 10.1016/j.ohx.2021.e00229. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Deadman B.; Ingham R.; Browne D. L.; Turner R.; Baxendale I. R.; Ley S. V.. ChemDraw Template for Flow Chemistry Icons; figshare: 2014. https://figshare.com/articles/figure/ChemDraw_Template_for_Flow_Chemistry_Icons/1170073 (accessed 2022-12-22).
  40. Britton J.; Raston C. L. Multi-step continuous-flow synthesis. Chem. Soc. Rev. 2017, 46 (5), 1250–1271. 10.1039/C6CS00830E. [DOI] [PubMed] [Google Scholar]
  41. Browne D. L.; Deadman B. J.; Ashe R.; Baxendale I. R.; Ley S. V. Continuous Flow Processing of Slurries: Evaluation of an Agitated Cell Reactor. Org. Process Res. Dev. 2011, 15 (3), 693–697. 10.1021/op2000223. [DOI] [Google Scholar]
  42. Filipponi P.; Venturoni F.; Suremann R.; Streit A.; Schoenebeck S.; Schenkel B.; Polenk J.; Piccioni L.; O’Meadhra R.; Mostarda S.; Haber J.; Guelat B.; Wegmann S. Fouling of Flow Reactors in Organolithium Mediated Transformations: Experience on Scale-up and Proposed Solution. Chimia 2019, 73 (10), 809–816. 10.2533/chimia.2019.809. [DOI] [PubMed] [Google Scholar]
  43. Baumann M.; Moody T. S.; Smyth M.; Wharry S. Overcoming the Hurdles and Challenges Associated with Developing Continuous Industrial Processes. Eur. J. Org. Chem. 2020, 2020 (48), 7398–7406. 10.1002/ejoc.202001278. [DOI] [Google Scholar]
  44. Reckamp J. M.; Bindels A.; Duffield S.; Liu Y. C.; Bradford E.; Ricci E.; Susanne F.; Rutter A. Mixing Performance Evaluation for Commercially Available Micromixers Using Villermaux-Dushman Reaction Scheme with the Interaction by Exchange with the Mean Model. Org. Process Res. Dev. 2017, 21 (6), 816–820. 10.1021/acs.oprd.6b00332. [DOI] [Google Scholar]
  45. Otálvaro-Marín H. L.; Machuca-Martínez F. Sizing of reactors by charts of Damköhler’s number for solutions of dimensionless design equations. Heliyon 2020, 6 (11), e05386 10.1016/j.heliyon.2020.e05386. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Bedard A.-C.; Regnier S.; Collins S. K. Continuous flow macrocyclization at high concentrations: synthesis of macrocyclic lipids. Green Chem. 2013, 15 (7), 1962–1966. 10.1039/c3gc40872h. [DOI] [Google Scholar]
  47. Briggs M. E.; Slater A. G.; Lunt N.; Jiang S.; Little M. A.; Greenaway R. L.; Hasell T.; Battilocchio C.; Ley S. V.; Cooper A. I. Dynamic flow synthesis of porous organic cages. Chem. Commun. 2015, 51 (98), 17390–17393. 10.1039/C5CC07447A. [DOI] [PubMed] [Google Scholar]
  48. Singh M. K.; Anderson P. D.; Meijer H. E. H. Understanding and Optimizing the SMX Static Mixer. Macromol. Rapid Commun. 2009, 30 (4–5), 362–376. 10.1002/marc.200800710. [DOI] [PubMed] [Google Scholar]
  49. Laybourn A.; López-Fernández A. M.; Thomas-Hillman I.; Katrib J.; Lewis W.; Dodds C.; Harvey A. P.; Kingman S. W. Combining continuous flow oscillatory baffled reactors and microwave heating: Process intensification and accelerated synthesis of metal-organic frameworks. Chem. Eng. J. 2019, 356, 170–177. 10.1016/j.cej.2018.09.011. [DOI] [Google Scholar]
  50. Wernik M.; Sipos G.; Buchholcz B.; Darvas F.; Novák Z.; Ötvös S. B.; Kappe C. O. Continuous flow heterogeneous catalytic reductive aminations under aqueous micellar conditions enabled by an oscillatory plug flow reactor. Green Chem. 2021, 23 (15), 5625–5632. 10.1039/D1GC02039K. [DOI] [Google Scholar]
  51. Walsh D.; Patureau P.; Robertson K.; Reeksting S.; Lubben A.; Eslava S.; Weller M. T. Exploring effects of intermittent light upon visible light promoted water oxidations. Sus. Ener. Fuels 2017, 1 (10), 2101–2109. 10.1039/C7SE00304H. [DOI] [Google Scholar]
  52. Levenstein M. A.; Kim Y.-Y.; Hunter L.; Anduix-Canto C.; González Niño C.; Day S. J.; Li S.; Marchant W. J.; Lee P. A.; Tang C. C.; Burghammer M.; Meldrum F. C.; Kapur N. Evaluation of microflow configurations for scale inhibition and serial X-ray diffraction analysis of crystallization processes. Lab Chip 2020, 20 (16), 2954–2964. 10.1039/D0LC00239A. [DOI] [PubMed] [Google Scholar]
  53. Elliott L. D.; Berry M.; Harji B.; Klauber D.; Leonard J.; Booker-Milburn K. I. A Small-Footprint, High-Capacity Flow Reactor for UV Photochemical Synthesis on the Kilogram Scale. Org. Process Res. Dev. 2016, 20 (10), 1806–1811. 10.1021/acs.oprd.6b00277. [DOI] [Google Scholar]
  54. Gilmore K.; Seeberger P. H. Continuous Flow Photochemistry. Chem. Record 2014, 14 (3), 410–418. 10.1002/tcr.201402035. [DOI] [PubMed] [Google Scholar]
  55. Cambie D.; Bottecchia C.; Straathof N. J. W.; Hessel V.; Noel T. Applications of Continuous-Flow Photochemistry in Organic Synthesis, Material Science, and Water Treatment. Chem. Rev. 2016, 116 (17), 10276–10341. 10.1021/acs.chemrev.5b00707. [DOI] [PubMed] [Google Scholar]
  56. Huang Z.; Shanmugam M.; Liu Z.; Brookfield A.; Bennett E. L.; Guan R.; Vega Herrera D. E.; Lopez-Sanchez J. A.; Slater A. G.; McInnes E. J. L.; Qi X.; Xiao J. Chemical Recycling of Polystyrene to Valuable Chemicals via Selective Acid-Catalyzed Aerobic Oxidation under Visible Light. J. Am. Chem. Soc. 2022, 144 (14), 6532–6542. 10.1021/jacs.2c01410. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Noël T.; Cao Y.; Laudadio G. The Fundamentals Behind the Use of Flow Reactors in Electrochemistry. Acc. Chem. Res. 2019, 52 (10), 2858–2869. 10.1021/acs.accounts.9b00412. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Baxendale I. R.; Hornung C.; Ley S. V.; Molina J. D. M.; Wikstrom A. Flow Microwave Technology and Microreactors in Synthesis. Aust. J. Chem. 2013, 66 (2), 131–144. 10.1071/CH12365. [DOI] [Google Scholar]
  59. Glasnov T. N.; Kappe C. O. The Microwave-to-Flow Paradigm: Translating High-Temperature Batch Microwave Chemistry to Scalable Continuous-Flow Processes. Chem. - Eur. J. 2011, 17 (43), 11956–11968. 10.1002/chem.201102065. [DOI] [PubMed] [Google Scholar]
  60. Dong Z.; Delacour C.; Mc Carogher K.; Udepurkar A. P.; Kuhn S. Continuous Ultrasonic Reactors: Design, Mechanism and Application. Materials 2020, 13 (2), 344. 10.3390/ma13020344. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Ceylan S.; Friese C.; Lammel C.; Mazac K.; Kirschning A. Inductive heating for organic synthesis by using functionalized magnetic nanoparticles inside microreactors. Angew. Chem., Int. Ed. 2008, 47 (46), 8950–8953. 10.1002/anie.200801474. [DOI] [PubMed] [Google Scholar]
  62. De Santis P.; Meyer L.-E.; Kara S. The rise of continuous flow biocatalysis - fundamentals, very recent developments and future perspectives. React. Chem. Eng. 2020, 5 (12), 2155–2184. 10.1039/D0RE00335B. [DOI] [Google Scholar]
  63. Munirathinam R.; Huskens J.; Verboom W. Supported Catalysis in Continuous-Flow Microreactors. Adv. Synth Catal. 2015, 357 (6), 1093–1123. 10.1002/adsc.201401081. [DOI] [Google Scholar]
  64. Vaccaro L.; Lanari D.; Marrocchi A.; Strappaveccia G. Flow approaches towards sustainability. Green Chem. 2014, 16 (8), 3680–3704. 10.1039/C4GC00410H. [DOI] [Google Scholar]
  65. Dallinger D.; Kappe C. O. Why flow means green - Evaluating the merits of continuous processing in the context of sustainability. Curr. Opin. Green Sust. 2017, 7, 6–12. 10.1016/j.cogsc.2017.06.003. [DOI] [Google Scholar]
  66. Fodi T.; Didaskalou C.; Kupai J.; Balogh G. T.; Huszthy P.; Szekely G. Nanofiltration-Enabled In Situ Solvent and Reagent Recycle for Sustainable Continuous-Flow Synthesis. ChemSusChem 2017, 10 (17), 3435–3444. 10.1002/cssc.201701120. [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Kitching M. O.; Dixon O. E.; Baumann M.; Baxendale I. R. Flow-Assisted Synthesis: A Key Fragment of SR 142948A. Eur. J. Org. Chem. 2017, 2017 (44), 6540–6553. 10.1002/ejoc.201700904. [DOI] [Google Scholar]
  68. Bourne R. A.; Skilton R. A.; Parrott A. J.; Irvine D. J.; Poliakoff M. Adaptive Process Optimization for Continuous Methylation of Alcohols in Supercritical Carbon Dioxide. Org. Process Res. Dev. 2011, 15 (4), 932–938. 10.1021/op200109t. [DOI] [Google Scholar]
  69. Cambie D.; Zhao F.; Hessel V.; Debije M. G.; Noel T. A Leaf-Inspired Luminescent Solar Concentrator for Energy-Efficient Continuous-Flow Photochemistry. Angew. Chem., Int. Ed. 2017, 56 (4), 1050–1054. 10.1002/anie.201611101. [DOI] [PubMed] [Google Scholar]
  70. Wengler J.; Ognier S.; Zhang M. X.; Levernier E.; Guyon C.; Ollivier C.; Fensterbank L.; Tatoulian M. Microfluidic chips for plasma flow chemistry: application to controlled oxidative processes. React. Chem. & Eng. 2018, 3 (6), 930–941. 10.1039/C8RE00122G. [DOI] [Google Scholar]
  71. Ogunyinka O.; Wright A.; Bolognesi G.; Iza F.; Bandulasena H. C. H. An integrated microfluidic chip for generation and transfer of reactive species using gas plasma. Microfluid. Nanofluid. 2020, 24 (2), 13. 10.1007/s10404-019-2316-9. [DOI] [Google Scholar]
  72. Roszkowska P.; Dickenson A.; Higham J.; Easun T.; Walsh J.; Slater A. G. Enabling batch and microfluidic non-thermal plasma chemistry: reactor design and testing. ChemRxiv 2022, [note - this content has not been peer-reviewed] 10.26434/chemrxiv-2022-d2hzk-v2. [DOI] [PubMed] [Google Scholar]
  73. Thomson C. G.; Banks C.; Allen M.; Barker G.; Coxon C. R.; Lee A.-L.; Vilela F. Expanding the Tool Kit of Automated Flow Synthesis: Development of In-line Flash Chromatography Purification. J. Org. Chem. 2021, 86 (20), 14079–14094. 10.1021/acs.joc.1c01151. [DOI] [PubMed] [Google Scholar]
  74. Sivo A.; Kim T. K.; Ruta V.; Luisi R.; Osorio-Tejada J.; Escriba-Gelonch M.; Hessel V.; Sponchioni M.; Vilé G. Enhanced flow synthesis of small molecules by in-line integration of sequential catalysis and benchtop twin-column continuous chromatography. React. Chem. Eng. 2022, 7 (12), 2650–2658. 10.1039/D2RE00242F. [DOI] [Google Scholar]
  75. Sultana M.; Jensen K. F. Microfluidic Continuous Seeded Crystallization: Extraction of Growth Kinetics and Impact of Impurity on Morphology. Cryst. Growth Des. 2012, 12 (12), 6260–6266. 10.1021/cg301538y. [DOI] [Google Scholar]
  76. Hartman R. L.; Naber J. R.; Buchwald S. L.; Jensen K. F. Multistep Microchemical Synthesis Enabled by Microfluidic Distillation. Angew. Chem., Int. Ed. 2010, 49 (5), 899–903. 10.1002/anie.200904634. [DOI] [PubMed] [Google Scholar]
  77. Weeranoppanant N.; Adamo A.; Saparbaiuly G.; Rose E.; Fleury C.; Schenkel B.; Jensen K. F. Design of Multistage Counter-Current Liquid-Liquid Extraction for Small-Scale Applications. Ind. Eng. Chem. Res. 2017, 56 (14), 4095–4103. 10.1021/acs.iecr.7b00434. [DOI] [Google Scholar]
  78. Verstraete K.; Buckinx A.-L.; Zaquen N.; Junkers T. Micelle Purification in Continuous Flow via Inline Dialysis. Macromolecules 2021, 54 (8), 3865–3872. 10.1021/acs.macromol.1c00242. [DOI] [Google Scholar]
  79. Note: increasing the flow rate can also achieve the same effect as moving the analysis point, but here the effect on mixing intensity must also be considered.
  80. Haas C. P.; Biesenroth S.; Buckenmaier S.; van de Goor T.; Tallarek U. Automated generation of photochemical reaction data by transient flow experiments coupled with online HPLC analysis. React. Chem. Eng. 2020, 5 (5), 912–920. 10.1039/D0RE00066C. [DOI] [Google Scholar]
  81. Stevens J. G.; Bourne R. A.; Poliakoff M. The continuous self aldol condensation of propionaldehyde in supercritical carbon dioxide: a highly selective catalytic route to 2-methylpentenal. Green Chem. 2009, 11 (3), 409–416. 10.1039/b819687g. [DOI] [Google Scholar]
  82. Ke J.; Gao C.; Folgueiras-Amador A. A.; Jolley K. E.; de Frutos O.; Mateos C.; Rincón J. A.; Brown R. C. D.; Poliakoff M.; George M. W. Self-Optimization of Continuous Flow Electrochemical Synthesis Using Fourier Transform Infrared Spectroscopy and Gas Chromatography. Appl. Spectrosc. 2022, 76 (1), 38–50. 10.1177/00037028211059848. [DOI] [PubMed] [Google Scholar]
  83. Browne D. L.; Wright S.; Deadman B. J.; Dunnage S.; Baxendale I. R.; Turner R. M.; Ley S. V. Continuous flow reaction monitoring using an on-line miniature mass spectrometer. Rapid Commun. Mass. Spectrom. 2012, 26 (17), 1999–2010. 10.1002/rcm.6312. [DOI] [PubMed] [Google Scholar]
  84. Jones C. D.; Kershaw Cook L. J.; Marquez-Gamez D.; Luzyanin K. V.; Steed J. W.; Slater A. G. High-Yielding Flow Synthesis of a Macrocyclic Molecular Hinge. J. Am. Chem. Soc. 2021, 143 (19), 7553–7565. 10.1021/jacs.1c02891. [DOI] [PMC free article] [PubMed] [Google Scholar]
  85. Meulendijks N.; Van Ee R.; Stevens R.; Mourad M.; Verheijen M.; Kambly N.; Armenta R.; Buskens P. Flow Cell Coupled Dynamic Light Scattering for Real-Time Monitoring of Nanoparticle Size during Liquid Phase Bottom-Up Synthesis. Applied Sciences 2018, 8 (1), 108. 10.3390/app8010108. [DOI] [Google Scholar]
  86. Carter C. F.; Baxendale I. R.; O’Brien M.; Pavey J. B. J.; Ley S. V. Synthesis of acetal protected building blocks using flow chemistry with flow IR analysis: preparation of butane-2,3-diacetal tartrates. Org. Biomol. Chem. 2009, 7 (22), 4594–4597. 10.1039/b917289k. [DOI] [PubMed] [Google Scholar]
  87. Carter C. F.; Lange H.; Ley S. V.; Baxendale I. R.; Wittkamp B.; Goode J. G.; Gaunt N. L. ReactIR Flow Cell: A New Analytical Tool for Continuous Flow Chemical Processing. Org. Process Res. Dev. 2010, 14 (2), 393–404. 10.1021/op900305v. [DOI] [Google Scholar]
  88. Thorne M. F.; Simkovic F.; Slater A. G. Production of monodisperse polyurea microcapsules using microfluidics. Sci. Rep. 2019, 9 (1), 17983. 10.1038/s41598-019-54512-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  89. Sans V.; Porwol L.; Dragone V.; Cronin L. A self optimizing synthetic organic reactor system using real-time in-line NMR spectroscopy. Chem. Sci. 2015, 6 (2), 1258–1264. 10.1039/C4SC03075C. [DOI] [PMC free article] [PubMed] [Google Scholar]
  90. Van Herck J.; Abeysekera I.; Buckinx A.-L.; Cai K.; Hooker J.; Thakur K.; Van de Reydt E.; Voorter P.-J.; Wyers D.; Junkers T. Operator-independent high-throughput polymerization screening based on automated inline NMR and online SEC. Digital Discovery 2022, 1 (4), 519–526. 10.1039/D2DD00035K. [DOI] [Google Scholar]
  91. Levenstein M. A.; Wayment L.; Scott C. D.; Lunt R.; Flandrin P.-B.; Day S. J.; Tang C. C.; Wilson C. C.; Meldrum F. C.; Kapur N.; Robertson K. Dynamic Crystallization Pathways of Polymorphic Pharmaceuticals Revealed in Segmented Flow with Inline Powder X-ray Diffraction. Anal. Chem. 2020, 92 (11), 7754–7761. 10.1021/acs.analchem.0c00860. [DOI] [PubMed] [Google Scholar]
  92. Levenstein M. A.; Anduix-Canto C.; Kim Y.-Y.; Holden M. A.; González Niño C.; Green D. C.; Foster S. E.; Kulak A. N.; Govada L.; Chayen N. E.; Day S. J.; Tang C. C.; Weinhausen B.; Burghammer M.; Kapur N.; Meldrum F. C. Droplet Microfluidics XRD Identifies Effective Nucleating Agents for Calcium Carbonate. Adv. Funct. Mater. 2019, 29 (19), 1808172. 10.1002/adfm.201808172. [DOI] [Google Scholar]
  93. Mekki-Berrada F.; Xie J.; Khan S. A. High-throughput and High-speed Absorbance Measurements in Microfluidic Droplets using Hyperspectral Imaging. Chemistry-Methods 2022, 2 (5), e202100086 10.1002/cmtd.202100086. [DOI] [Google Scholar]
  94. Levenstein M. A.; Robertson K.; Turner T. D.; Hunter L.; O’Brien C.; O’Shaughnessy C.; Kulak A. N.; Le Magueres P.; Wojciechowski J.; Mykhaylyk O. O.; Kapur N.; Meldrum F. C. Serial small- and wide-angle X-ray scattering with laboratory sources. IUCrJ. 2022, 9 (5), 538–543. 10.1107/S2052252522007631. [DOI] [PMC free article] [PubMed] [Google Scholar]
  95. Kirby N.; Cowieson N.; Hawley A. M.; Mudie S. T.; McGillivray D. J.; Kusel M.; Samardzic-Boban V.; Ryan T. M. Improved radiation dose efficiency in solution SAXS using a sheath flow sample environment. Acta Cryst. D 2016, 72 (12), 1254–1266. 10.1107/S2059798316017174. [DOI] [PMC free article] [PubMed] [Google Scholar]
  96. Alison H. G.; Davey R. J.; Garside J.; Quayle M. J.; Tiddy G. J. T.; Clarke D. T.; Jones G. R. Using a novel plug flow reactor for the in situ, simultaneous, monitoring of SAXS and WAXD during crystallisation from solution. Phys. Chem. Chem. Phys. 2003, 5 (22), 4998–5000. 10.1039/b309309c. [DOI] [Google Scholar]
  97. Rajagopalan A. K.; Schneeberger J.; Salvatori F.; Bötschi S.; Ochsenbein D. R.; Oswald M. R.; Pollefeys M.; Mazzotti M. A comprehensive shape analysis pipeline for stereoscopic measurements of particulate populations in suspension. Powder Technol. 2017, 321, 479–493. 10.1016/j.powtec.2017.08.044. [DOI] [Google Scholar]
  98. Besenhard M. O.; Jiang D.; Pankhurst Q. A.; Southern P.; Damilos S.; Storozhuk L.; Demosthenous A.; Thanh N. T. K.; Dobson P.; Gavriilidis A. Development of an in-line magnetometer for flow chemistry and its demonstration for magnetic nanoparticle synthesis. Lab Chip 2021, 21 (19), 3775–3783. 10.1039/D1LC00425E. [DOI] [PubMed] [Google Scholar]
  99. Monteiro D. C. F.; von Stetten D.; Stohrer C.; Sans M.; Pearson A. R.; Santoni G.; van der Linden P.; Trebbin M. 3D-MiXD: 3D-printed X-ray-compatible microfluidic devices for rapid, low-consumption serial synchrotron crystallography data collection in flow. IUCrJ. 2020, 7 (2), 207–219. 10.1107/S2052252519016865. [DOI] [PMC free article] [PubMed] [Google Scholar]
  100. Rodriguez-Zubiri M.; Felpin F.-X. Analytical Tools Integrated in Continuous-Flow Reactors: Which One for What?. Org. Process Res. Dev. 2022, 26 (6), 1766–1793. 10.1021/acs.oprd.2c00102. [DOI] [Google Scholar]
  101. Deadman B. J.; Browne D. L.; Baxendale I. R.; Ley S. V. Back Pressure Regulation of Slurry-Forming Reactions in Continuous Flow. Chem. Eng. Technol. 2015, 38 (2), 259–264. 10.1002/ceat.201400445. [DOI] [Google Scholar]
  102. Crawford D.; Casaban J.; Haydon R.; Giri N.; McNally T.; James S. L. Synthesis by extrusion: continuous, large-scale preparation of MOFs using little or no solvent. Chem. Sci. 2015, 6 (3), 1645–1649. 10.1039/C4SC03217A. [DOI] [PMC free article] [PubMed] [Google Scholar]
  103. Bolt R. R. A.; Leitch J. A.; Jones A. C.; Nicholson W. I.; Browne D. L. Continuous flow mechanochemistry: reactive extrusion as an enabling technology in organic synthesis. Chem. Soc. Rev. 2022, 51 (11), 4243–4260. 10.1039/D1CS00657F. [DOI] [PubMed] [Google Scholar]
  104. Hu C.; Shores B. T.; Derech R. A.; Testa C. J.; Hermant P.; Wu W.; Shvedova K.; Ramnath A.; Al Ismaili L. Q.; Su Q.; Sayin R.; Born S. C.; Takizawa B.; O’Connor T. F.; Yang X.; Ramanujam S.; Mascia S. Continuous reactive crystallization of an API in PFR-CSTR cascade with in-line PATs. React. Chem. Eng. 2020, 5 (10), 1950–1962. 10.1039/D0RE00216J. [DOI] [Google Scholar]
  105. Park N. H.; Senter T. J.; Buchwald S. L. Rapid Synthesis of Aryl Fluorides in Continuous Flow through the Balz-Schiemann Reaction. Angew. Chem., Int. Ed. 2016, 55 (39), 11907–11911. 10.1002/anie.201606601. [DOI] [PubMed] [Google Scholar]
  106. Li B.; Widlicka D.; Boucher S.; Hayward C.; Lucas J.; Murray J. C.; O’Neil B. T.; Pfisterer D.; Samp L.; VanAlsten J.; Xiang Y.; Young J. Telescoped Flow Process for the Syntheses of N-Aryl Pyrazoles. Org. Proc. Res. Dev. 2012, 16 (12), 2031–2035. 10.1021/op300209p. [DOI] [Google Scholar]
  107. Rice H. P.; He Y.; Muller F. L.; Bayly A. E.; Ashe R.; Karras A.; Hassanpour A.; Bourne R. A.; Fairweather M.; Hunter T. N. Physical and numerical characterisation of an agitated tubular reactor (ATR) for intensification of chemical processes. Chem. Eng. and Processing - Process Intensification 2022, 179, 109067. 10.1016/j.cep.2022.109067. [DOI] [Google Scholar]
  108. Majumder A.; Nagy Z. K. Dynamic Modeling of Encrust Formation and Mitigation Strategy in a Continuous Plug Flow Crystallizer. Cryst. Growth Des. 2015, 15 (3), 1129–1140. 10.1021/cg501431c. [DOI] [Google Scholar]
  109. Jiang M.; Braatz R. D. Designs of continuous-flow pharmaceutical crystallizers: developments and practice. CrystEngComm 2019, 21 (23), 3534–3551. 10.1039/C8CE00042E. [DOI] [Google Scholar]
  110. Briggs N. E. B.; Schacht U.; Raval V.; McGlone T.; Sefcik J.; Florence A. J. Seeded Crystallization of β-l-Glutamic Acid in a Continuous Oscillatory Baffled Crystallizer. Org. Process Res. Dev. 2015, 19 (12), 1903–1911. 10.1021/acs.oprd.5b00206. [DOI] [Google Scholar]
  111. Scott C. D.; Labes R.; Depardieu M.; Battilocchio C.; Davidson M. G.; Ley S. V.; Wilson C. C.; Robertson K. Integrated plug flow synthesis and crystallisation of pyrazinamide. React. Chem. Eng. 2018, 3 (5), 631–634. 10.1039/C8RE00087E. [DOI] [Google Scholar]
  112. Mascia S.; Heider P. L.; Zhang H.; Lakerveld R.; Benyahia B.; Barton P. I.; Braatz R. D.; Cooney C. L.; Evans J. M. B.; Jamison T. F.; Jensen K. F.; Myerson A. S.; Trout B. L. End-to-End Continuous Manufacturing of Pharmaceuticals: Integrated Synthesis, Purification, and Final Dosage Formation. Angew. Chem., Int. Ed. 2013, 52 (47), 12359–12363. 10.1002/anie.201305429. [DOI] [PubMed] [Google Scholar]
  113. Cruz P.; Alvarez C.; Rocha F.; Ferreira A. Tailoring the crystal size distribution of an active pharmaceutical ingredient by continuous antisolvent crystallization in a planar oscillatory flow crystallizer. Chem. Eng. Res. Des. 2021, 175, 115–123. 10.1016/j.cherd.2021.08.030. [DOI] [Google Scholar]
  114. Jiang M.; Zhu Z.; Jimenez E.; Papageorgiou C. D.; Waetzig J.; Hardy A.; Langston M.; Braatz R. D. Continuous-Flow Tubular Crystallization in Slugs Spontaneously Induced by Hydrodynamics. Cryst. Growth Des. 2014, 14 (2), 851–860. 10.1021/cg401715e. [DOI] [Google Scholar]
  115. Hussain M. N.; Jordens J.; John J. J.; Braeken L.; Van Gerven T. Enhancing pharmaceutical crystallization in a flow crystallizer with ultrasound: Anti-solvent crystallization. Ultrasonics Sonochemistry 2019, 59, 104743. 10.1016/j.ultsonch.2019.104743. [DOI] [PubMed] [Google Scholar]
  116. Rimez B.; Conté J.; Lecomte-Norrant E.; Cognet P.; Gourdon C.; Scheid B. Continuous-Flow Tubular Crystallization To Discriminate between Two Competing Crystal Polymorphs. 2. Antisolvent Crystallization. Cryst. Growth Des. 2018, 18 (11), 6440–6447. 10.1021/acs.cgd.8b00930. [DOI] [Google Scholar]
  117. Génot V.; Desportes S.; Croushore C.; Lefèvre J.-P.; Pansu R. B.; Delaire J. A.; von Rohr P. R. Synthesis of organic nanoparticles in a 3D flow focusing microreactor. Chem. Eng. J. 2010, 161 (1), 234–239. 10.1016/j.cej.2010.04.029. [DOI] [Google Scholar]
  118. Robertson K.; Seeberger P. H.; Gilmore K. Rapid optimization of API crystallisation in a segmented flow reactor with a continuous, variable temperature gradient. React. Chem. Eng. 2022, 8 (1), 77–83. 10.1039/D2RE00183G. [DOI] [Google Scholar]
  119. Han B.; Ezeanowi N. C.; Koiranen T. O.; Häkkinen A. T.; Louhi-Kultanen M. Insights into Design Criteria for a Continuous, Sonicated Modular Tubular Cooling Crystallizer. Cryst. Growth Des. 2018, 18 (12), 7286–7295. 10.1021/acs.cgd.8b00700. [DOI] [Google Scholar]
  120. Alvarez A. J.; Myerson A. S. Continuous Plug Flow Crystallization of Pharmaceutical Compounds. Cryst. Growth. Des. 2010, 10 (5), 2219–2228. 10.1021/cg901496s. [DOI] [Google Scholar]
  121. Mathew Thomas K.; Nyande B. W.; Lakerveld R. Design and characterization of Kenics static mixer crystallizers. Chem. Eng. Res. Des. 2022, 179, 549–563. 10.1016/j.cherd.2022.01.025. [DOI] [Google Scholar]
  122. Lawton S.; Steele G.; Shering P.; Zhao L.; Laird I.; Ni X.-W. Continuous Crystallization of Pharmaceuticals Using a Continuous Oscillatory Baffled Crystallizer. Org. Process Res. Dev. 2009, 13 (6), 1357–1363. 10.1021/op900237x. [DOI] [Google Scholar]
  123. McGlone T.; Briggs N. E. B.; Clark C. A.; Brown C. J.; Sefcik J.; Florence A. J. Oscillatory Flow Reactors (OFRs) for Continuous Manufacturing and Crystallization. Org. Process Res. Dev. 2015, 19 (9), 1186–1202. 10.1021/acs.oprd.5b00225. [DOI] [Google Scholar]
  124. Zhang S.; Ferté N.; Candoni N.; Veesler S. Versatile Microfluidic Approach to Crystallization. Org. Process Res. Dev. 2015, 19 (12), 1837–1841. 10.1021/acs.oprd.5b00122. [DOI] [Google Scholar]
  125. Robertson K.; Flandrin P.-B.; Klapwijk A. R.; Wilson C. C. Design and Evaluation of a Mesoscale Segmented Flow Reactor (KRAIC). Cryst. Growth Des. 2016, 16 (8), 4759–4764. 10.1021/acs.cgd.6b00885. [DOI] [Google Scholar]
  126. Gao Y.; Pinho B.; Torrente-Murciano L. Recent progress on the manufacturing of nanoparticles in multi-phase and single-phase flow reactors. Curr. Op. Chem. Eng. 2020, 29, 26–33. 10.1016/j.coche.2020.03.008. [DOI] [Google Scholar]
  127. Niculescu A.-G.; Mihaiescu D. E.; Grumezescu A. M. A Review of Microfluidic Experimental Designs for Nanoparticle Synthesis. Int. J. Mol. Sci. 2022, 23 (15), 8293. 10.3390/ijms23158293. [DOI] [PMC free article] [PubMed] [Google Scholar]
  128. Koswara A.; Nagy Z. K. Anti-Fouling Control of Plug-Flow Crystallization via Heating and Cooling Cycle. IFAC-PapersOnLine 2015, 48 (8), 193–198. 10.1016/j.ifacol.2015.08.180. [DOI] [Google Scholar]
  129. Sheridan R.; Cardona J.; Tachtatzis C.; Chen Y.-C.; Cleary A.; Briggs N.; Florence A.; Atkinson R.; Michie C.; Andonovic I.; Sefcik J. Effect of oscillatory flow conditions on crystalliser fouling investigated through non-invasive imaging. Chem. Eng. Sci. 2022, 252, 117188. 10.1016/j.ces.2021.117188. [DOI] [Google Scholar]
  130. Pittaway P. M.; Ghasemi G.; Knox S. T.; Cayre O. J.; Kapur N.; Warren N. J. Continuous synthesis of block copolymer nanoparticles via telescoped RAFT solution and dispersion polymerisation in a miniature CSTR cascade. React. Chem. Eng. 2023, 10.1039/D2RE00475E. [DOI] [Google Scholar]
  131. O’Nolan D.; Huang G.; Kamm G. E.; Grenier A.; Liu C.-H.; Todd P. K.; Wustrow A.; Thinh Tran G.; Montiel D.; Neilson J. R.; Billinge S. J. L.; Chupas P. J.; Thornton K. S.; Chapman K. W. A thermal-gradient approach to variable-temperature measurements resolved in space. J. Appl. Crystallogr. 2020, 53 (3), 662–670. 10.1107/S160057672000415X. [DOI] [PMC free article] [PubMed] [Google Scholar]
  132. Wu Z.; Seok S.; Kim D. H.; Kim W.-S. Control of Crystal Size Distribution using Non-Isothermal Taylor Vortex Flow. Cryst. Growth Des. 2015, 15 (12), 5675–5684. 10.1021/acs.cgd.5b00431. [DOI] [Google Scholar]
  133. Note: please contact A.G.S. if you are aware of such a solution!
  134. Numata M. Supramolecular Chemistry in Microflow Fields: Toward a New Material World of Precise Kinetic Control. Chem.—Asian J. 2015, 10 (12), 2574–2588. 10.1002/asia.201500555. [DOI] [PubMed] [Google Scholar]
  135. Ollerton K.; Greenaway R. L.; Slater A. G. Enabling Technology for Supramolecular Chemistry. Frontiers in Chemistry 2021, 9, 774987 10.3389/fchem.2021.774987. [DOI] [PMC free article] [PubMed] [Google Scholar]
  136. Brocken L.; Price P. D.; Whittaker J.; Baxendale I. R. Continuous flow synthesis of poly(acrylic acid) via free radical polymerisation. React. Chem. & Eng. 2017, 2 (5), 662–668. 10.1039/C7RE00063D. [DOI] [Google Scholar]
  137. Parkinson S.; Knox S. T.; Bourne R. A.; Warren N. J. Rapid production of block copolymer nano-objects via continuous-flow ultrafast RAFT dispersion polymerisation. Polym. Chem. 2020, 11 (20), 3465–3474. 10.1039/D0PY00276C. [DOI] [Google Scholar]
  138. Knox S. T.; Parkinson S. J.; Wilding C. Y. P.; Bourne R. A.; Warren N. J. Autonomous polymer synthesis delivered by multi-objective closed-loop optimization. Polym. Chem. 2022, 13 (11), 1576–1585. 10.1039/D2PY00040G. [DOI] [Google Scholar]
  139. Reis M. H.; Leibfarth F. A.; Pitet L. M. Polymerizations in Continuous Flow: Recent Advances in the Synthesis of Diverse Polymeric Materials. Macro Lett. 2020, 9 (1), 123–133. 10.1021/acsmacrolett.9b00933. [DOI] [PubMed] [Google Scholar]
  140. O’Brien A. G.; Levesque F.; Suzuki Y.; Seeberger P. H. Safe use of azides in continuous flow. Chim. Oggi 2011, 29 (3), 57. [Google Scholar]
  141. Cantillo D.; Kappe C. O. Halogenation of organic compounds using continuous flow and microreactor technology. React. Chem. Eng. 2017, 2 (1), 7–19. 10.1039/C6RE00186F. [DOI] [Google Scholar]
  142. Baumann M.; Baxendale I. R.; Martin L. J.; Ley S. V. Development of fluorination methods using continuous-flow microreactors. Tetrahedron 2009, 65 (33), 6611–6625. 10.1016/j.tet.2009.05.083. [DOI] [Google Scholar]
  143. Bonner A.; Loftus A.; Padgham A. C.; Baumann M. Forgotten and forbidden chemical reactions revitalised through continuous flow technology. Org. Biomol. Chem. 2021, 19 (36), 7737–7753. 10.1039/D1OB01452H. [DOI] [PubMed] [Google Scholar]
  144. McMullen J. P.; Jensen K. F. Rapid Determination of Reaction Kinetics with an Automated Microfluidic System. Org. Process Res. Dev. 2011, 15 (2), 398–407. 10.1021/op100300p. [DOI] [Google Scholar]
  145. Zang H. Y.; de la Oliva A. R.; Miras H. N.; Long D. L.; McBurney R. T.; Cronin L. Discovery of gigantic molecular nanostructures using a flow reaction array as a search engine. Nat. Commun. 2014, 5, 3715. 10.1038/ncomms4715. [DOI] [PMC free article] [PubMed] [Google Scholar]
  146. McMullen J. P.; Wyvratt B. M. Automated optimization under dynamic flow conditions. React. Chem. Eng. 2022, 8 (1), 137–151. 10.1039/D2RE00256F. [DOI] [Google Scholar]
  147. Carson N. Rise of the Robots. Chem.—Eur. J. 2020, 26 (15), 3194–3196. 10.1002/chem.202000656. [DOI] [PubMed] [Google Scholar]
  148. King R. D.; Rowland J.; Oliver S. G.; Young M.; Aubrey W.; Byrne E.; Liakata M.; Markham M.; Pir P.; Soldatova L. N.; Sparkes A.; Whelan K. E.; Clare A. The Automation of Science. Science 2009, 324 (5923), 85–89. 10.1126/science.1165620. [DOI] [PubMed] [Google Scholar]
  149. Collins N.; Stout D.; Lim J.-P.; Malerich J. P.; White J. D.; Madrid P. B.; Latendresse M.; Krieger D.; Szeto J.; Vu V.-A.; Rucker K.; Deleo M.; Gorfu Y.; Krummenacker M.; Hokama L. A.; Karp P.; Mallya S. Fully Automated Chemical Synthesis: Toward the Universal Synthesizer. Org. Process Res. Dev. 2020, 24 (10), 2064–2077. 10.1021/acs.oprd.0c00143. [DOI] [Google Scholar]
  150. Bourne R. A.; Hii K. K.; Reizman B. J. Introduction to Synthesis 4.0: towards an internet of chemistry. React. Chem. Eng. 2019, 4 (9), 1504–1505. 10.1039/C9RE90048A. [DOI] [Google Scholar]
  151. Kershaw O. J.; Clayton A. D.; Manson J. A.; Barthelme A.; Pavey J.; Peach P.; Mustakis J.; Howard R. M.; Chamberlain T. W.; Warren N. J.; Bourne R. A. Machine learning directed multi-objective optimization of mixed variable chemical systems. Chem. Eng. J. 2023, 451, 138443. 10.1016/j.cej.2022.138443. [DOI] [Google Scholar]
  152. Hall B. L.; Taylor C. J.; Labes R.; Massey A. F.; Menzel R.; Bourne R. A.; Chamberlain T. W. Autonomous optimization of a nanoparticle catalysed reduction reaction in continuous flow. Chem. Commun. 2021, 57 (40), 4926–4929. 10.1039/D1CC00859E. [DOI] [PubMed] [Google Scholar]
  153. Simon K.; Sagmeister P.; Munday R.; Leslie K.; Hone C. A.; Kappe C. O. Automated flow and real-time analytics approach for screening functional group tolerance in heterogeneous catalytic reactions. Catal. Sci. Technol. 2022, 12 (6), 1799–1811. 10.1039/D2CY00059H. [DOI] [Google Scholar]
  154. Fitzpatrick D. E.; Maujean T.; Evans A. C.; Ley S. V. Across-the-World Automated Optimization and Continuous-Flow Synthesis of Pharmaceutical Agents Operating Through a Cloud-Based Server. Angew. Chem., Int. Ed. 2018, 57 (46), 15128–15132. 10.1002/anie.201809080. [DOI] [PMC free article] [PubMed] [Google Scholar]
  155. Parrott A. J.; Bourne R. A.; Akien G. R.; Irvine D. J.; Poliakoff M. Self-optimizing continuous reactions in supercritical carbon dioxide. Angew. Chem., Int. Ed. 2011, 50, 3788–3792. 10.1002/anie.201100412. [DOI] [PubMed] [Google Scholar]
  156. Moore J. S.; Jensen K. F. Automated Multitrajectory Method for Reaction Optimization in a Microfluidic System using Online IR Analysis. Org. Process Res. Dev. 2012, 16 (8), 1409–1415. 10.1021/op300099x. [DOI] [Google Scholar]
  157. Fabry D. C.; Sugiono E.; Rueping M. Self-Optimizing Reactor Systems: Algorithms, On-line Analytics, Setups, and Strategies for Accelerating Continuous Flow Process Optimization. Isr. J. Chem. 2014, 54 (4), 341–350. 10.1002/ijch.201300080. [DOI] [Google Scholar]
  158. Cortes-Borda D.; Kutonova K. V.; Jamet C.; Trusova M. E.; Zammattio F.; Truchet C.; Rodriguez-Zubiri M.; Felpin F.-X. Optimizing the Heck-Matsuda Reaction in Flow with a Constraint-Adapted Direct Search Algorithm. Org. Process Res. Dev. 2016, 20 (11), 1979–1987. 10.1021/acs.oprd.6b00310. [DOI] [Google Scholar]
  159. Schweidtmann A. M.; Clayton A. D.; Holmes N.; Bradford E.; Bourne R. A.; Lapkin A. A. Machine learning meets continuous flow chemistry: Automated optimization towards the Pareto front of multiple objectives. Chem. Eng. J. 2018, 352, 277–282. 10.1016/j.cej.2018.07.031. [DOI] [Google Scholar]
  160. Mateos C.; Nieves-Remacha M. J.; Rincón J. A. Automated platforms for reaction self-optimization in flow. React. Chem. Eng. 2019, 4, 1536–1544. 10.1039/C9RE00116F. [DOI] [Google Scholar]
  161. Clayton A. D.; Manson J. A.; Taylor C. J.; Chamberlain T. W.; Taylor B. A.; Clemens G.; Bourne R. A. Algorithms for the self-optimization of chemical reactions. React. Chem. Eng. 2019, 4 (9), 1545–1554. 10.1039/C9RE00209J. [DOI] [Google Scholar]
  162. Clayton A. D.; Schweidtmann A. M.; Clemens G.; Manson J. A.; Taylor C. J.; Niño C. G.; Chamberlain T. W.; Kapur N.; Blacker A. J.; Lapkin A. A.; Bourne R. A. Automated self-optimization of multi-step reaction and separation processes using machine learning. Chem. Eng. J. 2020, 384, 123340. 10.1016/j.cej.2019.123340. [DOI] [Google Scholar]
  163. Taylor C. J.; Booth M.; Manson J. A.; Willis M. J.; Clemens G.; Taylor B. A.; Chamberlain T. W.; Bourne R. A. Rapid, automated determination of reaction models and kinetic parameters. Chem. Eng. J. 2021, 413, 127017. 10.1016/j.cej.2020.127017. [DOI] [Google Scholar]
  164. Clayton A. D.; Pyzer-Knapp E. O.; Purdie M.; Jones M. F.; Barthelme A.; Pavey J.; Kapur N.; Chamberlain T. W.; Blacker A. J.; Bourne R. A. Bayesian Self-Optimization for Telescoped Continuous Flow Synthesis. Angew. Chem., Int. Ed. 2023, 62, e202214511 10.1002/anie.202214511. [DOI] [PMC free article] [PubMed] [Google Scholar]
  165. Aroh K. C.; Jensen K. F. Efficient kinetic experiments in continuous flow microreactors. React. Chem. Eng. 2018, 3 (1), 94–101. 10.1039/C7RE00163K. [DOI] [Google Scholar]
  166. Waldron C.; Pankajakshan A.; Quaglio M.; Cao E.; Galvanin F.; Gavriilidis A. Model-based design of transient flow experiments for the identification of kinetic parameters. React. Chem. Eng. 2020, 5 (1), 112–123. 10.1039/C9RE00342H. [DOI] [Google Scholar]
  167. Van Herck J.; Junkers T. Rapid Kinetic Screening via Transient Timesweep Experiments in Continuous Flow Reactors. Chemistry-Methods 2022, 2 (1), e202100090 10.1002/cmtd.202100090. [DOI] [Google Scholar]
  168. Schrecker L.; Dickhaut J.; Holtze C.; Staehle P.; Vranceanu M.; Hellgardt K.; Hii K. K. Discovery of unexpectedly complex reaction pathways for the Knorr pyrazole synthesis via transient flow. React. Chem. Eng. 2022, 8 (1), 41–46. 10.1039/D2RE00271J. [DOI] [Google Scholar]
  169. Sun A. C.; Steyer D. J.; Allen A. R.; Payne E. M.; Kennedy R. T.; Stephenson C. R. J. A droplet microfluidic platform for high-throughput photochemical reaction discovery. Nat. Commun. 2020, 11 (1), 6202. 10.1038/s41467-020-19926-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  170. Reizman B. J.; Wang Y.-M.; Buchwald S. L.; Jensen K. F. Suzuki-Miyaura cross-coupling optimization enabled by automated feedback. React. Chem. Eng. 2016, 1 (6), 658–666. 10.1039/C6RE00153J. [DOI] [PMC free article] [PubMed] [Google Scholar]
  171. Bédard A. C.; Adamo A.; Aroh K. C.; Russell M. G.; Bedermann A. A.; Torosian J.; Yue B.; Jensen K. F.; Jamison T. F. Reconfigurable system for automated optimization of diverse chemical reactions. Science 2018, 361 (6408), 1220–1225. 10.1126/science.aat0650. [DOI] [PubMed] [Google Scholar]
  172. Process intensification - an overview. In Process Intensification, Reay D., Ramshaw C., Harvey A., Eds.; Butterworth-Heinemann: Oxford, 2008; Chapter 2. [Google Scholar]
  173. Newman S. G.; Jensen K. F. The role of flow in green chemistry and engineering. Green Chem. 2013, 15 (6), 1456–1472. 10.1039/c3gc40374b. [DOI] [Google Scholar]
  174. Kirchherr J.; Reike D.; Hekkert M. Conceptualizing the circular economy: An analysis of 114 definitions. Resources, Conservation and Recycling 2017, 127, 221–232. 10.1016/j.resconrec.2017.09.005. [DOI] [Google Scholar]
  175. Moosavi S. M.; Chidambaram A.; Talirz L.; Haranczyk M.; Stylianou K. C.; Smit B. Capturing chemical intuition in synthesis of metal-organic frameworks. Nat. Commun. 2019, 10 (1), 539. 10.1038/s41467-019-08483-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  176. Luo Y.; Bag S.; Zaremba O.; Cierpka A.; Andreo J.; Wuttke S.; Friederich P.; Tsotsalas M. MOF Synthesis Prediction Enabled by Automatic Data Mining and Machine Learning. Angew. Chem., Int. Ed. 2022, 61 (19), e202200242 10.1002/anie.202200242. [DOI] [PMC free article] [PubMed] [Google Scholar]

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