An Automated Continuous-Flow Platform for the Estimation of Multistep Reaction Kinetics

Self-Driving Laboratories for Chemistry and Materials Science

Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through the automation of experimental workflows, along with autonomous experimental planning, SDLs hold the potential to greatly accelerate research in chemistry and materials discovery. This review provides an in-depth analysis of the state-of-the-art in SDL technology, its applications across various scientific disciplines, and the potential implications for research and industry. This review additionally provides an overview of the enabling technologies for SDLs, including their hardware, software, and integration with laboratory infrastructure. Most importantly, this review explores the diverse range of scientific domains where SDLs have made significant contributions, from drug discovery and materials science to genomics and chemistry. We provide a comprehensive review of existing real-world examples of SDLs, their different levels of automation, and the challenges and limitations associated with each domain.

Kinetic Aspects of Esterification and Transesterification in Microstructured Reactors

Microstructured reactors offer fast chemical engineering transfer and precise microfluidic control, enabling the determination of reactions' kinetic parameters. This review examines recent advancements in measuring microreaction kinetics. It explores kinetic modeling, reaction mechanisms, and intrinsic kinetic equations pertaining to two types of microreaction: esterification and transesterification reactions involving acids, bases, or biocatalysts. The utilization of a micro packed-bed reactor successfully achieves a harmonious combination of the micro-dispersion state and the reaction kinetic characteristics. Additionally, this review presents micro-process simulation software and explores the advanced integration of microreactors with spectroscopic analyses for reaction monitoring and data acquisition. Furthermore, it elaborates on the control principles of the micro platform. The superiority of online measurement, automation, and the digitalization of the microreaction process for kinetic measurements is highlighted, showcasing the vast prospects of artificial intelligence applications.

Smart manufacturing inspired approach to research, development, and scale-up of electrified chemical manufacturing systems

As renewable electricity becomes cost competitive with fossil fuel energy sources and environmental concerns increase, the transition to electrified chemical and fuel synthesis pathways becomes increasingly desirable. However, electrochemical systems have traditionally taken many decades to reach commercial scales. Difficulty in scaling up electrochemical synthesis processes comes primarily from difficulty in decoupling and controlling simultaneously the effects of intrinsic kinetics and charge, heat, and mass transport within electrochemical reactors. Tackling this issue efficiently requires a shift in research from an approach based on small datasets, to one where digitalization enables rapid collection and interpretation of large, well-parameterized datasets, using artificial intelligence (AI) and multi-scale modeling. In this perspective, we present an emerging research approach that is inspired by smart manufacturing (SM), to accelerate research, development, and scale-up of electrified chemical manufacturing processes. The value of this approach is demonstrated by its application toward the development of CO₂ electrolyzers.

Bayesian based reaction optimization for complex continuous gas–liquid–solid reactions

In recent years, self-optimization strategies have been gradually utilized for the determination of optimal reaction conditions owing to their high convenience and independence from researchers' experience.

Modern advancements in continuous-flow aided kinetic analysis

Although kinetic analysis has traditionally been conducted in a batch vessel, continuous-flow aided kinetic analysis continues to swell in popularity.

Device for automated screening of irradiation wavelength and intensity – investigation of the wavelength dependence of photoreactions with an arylazo sulfone in continuous flow

A system allowing the automatic change of LED arrays (normalized to the number of emitted photons) is presented to study photochemical reactions in continuous flow for their wavelength dependence.

Continuous stirred-tank reactor cascade platform for self-optimization of reactions involving solids

Research-scale fully automated flow platform for reaction self-optimization with solids handling facilitates identification of optimal conditions for continuous manufacturing of pharmaceuticals while reducing amounts of raw materials consumed.

Simultaneous self-optimisation of yield and purity through successive combination of inline FT-IR spectroscopy and online mass spectrometry in flow reactions

Self-optimisation constitutes a very helpful tool for chemical process development, both in lab and in industrial applications. However, research on the application of model-free autonomous optimisation strategies (based on experimental investigation) for complex reactions of high industrial significance, which involve considerable intermediate and by-product formation, is still in an early stage. This article describes the development of an enhanced autonomous microfluidic reactor platform for organolithium and epoxide reactions that incorporates a successive combination of inline FT-IR spectrometer and online mass spectrometer. Experimental data is collected in real-time and used as feedback for the optimisation algorithms (modified Simplex algorithm and Design of Experiments) without time delay. An efficient approach to handle intricate optimisation problems is presented, where the inline FT-IR measurements are used to monitor the reaction’s main components, whereas the mass spectrometer’s high sensitivity permits insights into the formation of by-products. To demonstrate the platform’s flexibility, optimal reaction conditions of two organic syntheses are identified. Both pose several challenges, as complex reaction mechanisms are involved, leading to a large number of variable parameters, and a considerable amount of by-products is generated under non-ideal process conditions. Through multidimensional real-time optimisation, the platform supersedes labor- and cost-intensive work-up procedures, while diminishing waste generation, too. Thus, it renders production processes more efficient and contributes to their overall sustainability.

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Artificial intelligence and automation in computer aided synthesis planning

In this perspective we deal with questions pertaining to the development of synthesis planning technologies over the course of recent years.

From circular synthesis to material manufacturing: advances, challenges, and future steps for using flow chemistry in novel application area

We review the emerging use of flow technologies for circular chemistry and material manufacturing, highlighting advances, challenges, and future directions.

Catalysis-in-a-Box: Robotic Screening of Catalytic Materials in the Time of COVID-19 and Beyond

This work describes the design and implementation of an automated device for catalytic materials testing by direct modifications to a gas chromatograph (GC). The setup can be operated as a plug-flow isothermal reactor and enables the control of relevant parameters such as reaction temperature and reactant partial pressures directly from the GC. High-quality kinetic data (including reaction rates, product distributions, and activation barriers) can be obtained at almost one-tenth of the fabrication cost of analogous commercial setups. With these key benefits including automation, low cost, and limited experimental equipment instrumentation, this implementation is intended as a high-throughput catalyst screening reactor that can be readily utilized by materials synthesis researchers to assess the catalytic properties of their synthesized structures in vapor-phase chemistries.

The Medicinal Chemistry in the Era of Machines and Automation: Recent Advances in Continuous Flow Technology

Medicinal chemistry plays a fundamental and underlying role in chemical biology, pharmacology, and medicine to discover safe and efficacious drugs. Small molecule medicinal chemistry relies on iterative learning cycles composed of compound design, synthesis, testing, and data analysis to provide new chemical probes and lead compounds for novel and druggable targets. Using traditional approaches, the time from hypothesis to obtaining the results can be protracted, thus limiting the number of compounds that can be advanced into clinical studies. This challenge can be tackled with the recourse of enabling technologies that are showing great potential in improving the drug discovery process. In this Perspective, we highlight recent developments toward innovative medicinal chemistry strategies based on continuous flow systems coupled with automation and bioassays. After a discussion of the aims and concepts, we describe equipment and representative examples of automated flow systems and end-to-end prototypes realized to expedite medicinal chemistry discovery cycles.

Self-optimising processes and real-time-optimisation of organic syntheses in a microreactor system using Nelder–Mead and design of experiments

Comparing an enhanced simplex algorithm with model-free design of experiments, this work presents a flexible platform for multi-objective, real-time optimisation.

An automated flow platform for accurate determination of gas–liquid–solid reaction kinetics

An automated flow platform based on a tube-in-tube contactor and micro-packed bed reactor is developed to measure the kinetics of gas–liquid–solid hydrogenation reactions.

Model-based design of transient flow experiments for the identification of kinetic parameters

Rapid and precise estimation of kinetic parameters is facilitated by transient flow experiments designed using model-based design of experiments.

Laboratory of the future: a modular flow platform with multiple integrated PAT tools for multistep reactions

The coupling of a modular microreactor platform, real-time inline analysis by IR and NMR, and online UPLC, leads to efficient optimization of a multistep organolithium transformation to a given product without the need for human intervention.

An autonomous microreactor platform for the rapid identification of kinetic models

Rapid estimation of kinetic parameters with high precision is facilitated by automation combined with online Model-Based Design of Experiments.

Uniting laboratory automation, DoE data, and modeling techniques to accelerate chemical process development

Rapid knowledge building of chemical processes with highly automated DoE (HAD) and statistical analyses and modeling.

Definitive screening designs for multistep kinetic models in flow

A definitive screening design (DSD) combined with reaction profiling was conducted using a flow reactor, in a short time frame, for the accurate estimation of kinetic parameters.

Toward a Comprehensive and Efficient Robust Optimization Framework for (Bio)chemical Processes

Model-based design principles have received considerable attention in biotechnology and the chemical industry over the last two decades. However, parameter uncertainties of first-principle models are critical in model-based design and have led to the development of robustification concepts. Various strategies have been introduced to solve the robust optimization problem. Most approaches suffer from either unreasonable computational expense or low approximation accuracy. Moreover, they are not rigorous and do not consider robust optimization problems where parameter correlation and equality constraints exist. In this work, we propose a highly efficient framework for solving robust optimization problems with the so-called point estimation method (PEM). The PEM has a fair trade-off between computational expense and approximation accuracy and can be easily extended to problems of parameter correlations. From a statistical point of view, moment-based methods are used to approximate robust inequality and equality constraints for a robust process design. We also apply a global sensitivity analysis to further simplify robust optimization problems with a large number of uncertain parameters. We demonstrate the performance of the proposed framework with two case studies: (1) designing a heating/cooling profile for the essential part of a continuous production process; and (2) optimizing the feeding profile for a fed-batch reactor of the penicillin fermentation process. According to the derived results, the proposed framework of robust process design addresses uncertainties adequately and scales well with the number of uncertain parameters. Thus, the described robustification concept should be an ideal candidate for more complex (bio)chemical problems in model-based design.

Efficient kinetic experiments in continuous flow microreactors

Transient temperature and flowrates in continuous flow reaction systems allows for the rapid generation of kinetic data.

Flow reactors integrated with in-line monitoring using benchtop NMR spectroscopy

The state-of-the-art flow reactors integrated with in-line benchtop NMR are thoroughly discussed with highlights on the strengths and weaknesses of this emerging technology.

OpenFlowChem – a platform for quick, robust and flexible automation and self-optimisation of flow chemistry

OpenFlowChem – an open-access platform for automation of process control and monitoring optimised for flexibility.

The Hitchhiker's Guide to Flow Chemistry ∥

Flow chemistry involves the use of channels or tubing to conduct a reaction in a continuous stream rather than in a flask. Flow equipment provides chemists with unique control over reaction parameters enhancing reactivity or in some cases enabling new reactions. This relatively young technology has received a remarkable amount of attention in the past decade with many reports on what can be done in flow. Until recently, however, the question, "Should we do this in flow?" has merely been an afterthought. This review introduces readers to the basic principles and fundamentals of flow chemistry and critically discusses recent flow chemistry accounts.

Automating multistep flow synthesis: approach and challenges in integrating chemistry, machines and logic

The implementation of automation in the multistep flow synthesis is essential for transforming laboratory-scale chemistry into a reliable industrial process. In this review, we briefly introduce the role of automation based on its application in synthesis viz. auto sampling and inline monitoring, optimization and process control. Subsequently, we have critically reviewed a few multistep flow synthesis and suggested a possible control strategy to be implemented so that it helps to reliably transfer the laboratory-scale synthesis strategy to a pilot scale at its optimum conditions. Due to the vast literature in multistep synthesis, we have classified the literature and have identified the case studies based on few criteria viz. type of reaction, heating methods, processes involving in-line separation units, telescopic synthesis, processes involving in-line quenching and process with the smallest time scale of operation. This classification will cover the broader range in the multistep synthesis literature.

Rapid multistep kinetic model generation from transient flow data

S_NAr reaction profiles were generated using an automated reactor, collected in less than 3 hours, and allowed accurate estimation of kinetic parameters.

Today, the generation of kinetic models is still seen as a resource intensive and specialised activity. We report an efficient method of generating reaction profiles from transient flows using a state-of-the-art continuous-flow platform. Experimental data for multistep aromatic nucleophilic substitution reactions are collected from an automated linear gradient flow ramp with online HPLC at the reactor outlet. Using this approach, we generated 16 profiles, at 3 different inlet concentrations and 4 temperatures, in less than 3 hours run time. The kinetic parameters, 4 rate constants and 4 activation energies were fitted with less than 4% uncertainty. We derived an expression for the error in the observed rate constants due to dispersion and showed that such error is 5% or lower. The large range of operational conditions prevented the need to isolate individual reaction steps. Our approach enables early identification of the sensitivity of product quality to parameter changes and early use of unit operation models to identify optimal process-equipment combinations in silico, greatly reducing scale up risks.

Self-optimisation and model-based design of experiments for developing a C-H activation flow process

A recently described C(sp3)-H activation reaction to synthesise aziridines was used as a model reaction to demonstrate the methodology of developing a process model using model-based design of experiments (MBDoE) and self-optimisation approaches in flow. The two approaches are compared in terms of experimental efficiency. The self-optimisation approach required the least number of experiments to reach the specified objectives of cost and product yield, whereas the MBDoE approach enabled a rapid generation of a process model.

Telescoped continuous flow generation of a library of highly substituted 3-thio-1,2,4-triazoles

An integrated continuous flow process for the synthesis of 3-thio-1,2,4-triazoles is reported. A small library of 18 compounds was prepared in just 48 minutes of residence time in moderate to excellent yields.

Suzuki-Miyaura cross-coupling optimization enabled by automated feedback

An automated, droplet-flow microfluidic system explores and optimizes Pd-catalyzed Suzuki-Miyaura cross-coupling reactions. A smart optimal DoE-based algorithm is implemented to increase the turnover number and yield of the catalytic system considering both discrete variables-palladacycle and ligand-and continuous variables-temperature, time, and loading-simultaneously. The use of feedback allows for experiments to be run with catalysts and under conditions more likely to produce an optimum; consequently complex reaction optimizations are completed within 96 experiments. Response surfaces predicting reaction performance near the optima are generated and validated. From the screening results, shared attributes of successful precatalysts are identified, leading to improved understanding of the influence of ligand selection upon transmetalation and oxidative addition in the reaction mechanism. Dialkylbiarylphosphine, trialkylphosphine, and bidentate ligands are assessed.

Feedback in Flow for Accelerated Reaction Development

The pharmaceutical industry is investing in continuous flow and high-throughput experimentation as tools for rapid process development accelerated scale-up. Coupled with automation, these technologies offer the potential for comprehensive reaction characterization and optimization, but with the cost of conducting exhaustive multifactor screens. Automated feedback in flow offers researchers an alternative strategy for efficient characterization of reactions based on the use of continuous technology to control chemical reaction conditions and optimize in lieu of screening. Optimization with feedback allows experiments to be conducted where the most information can be gained from the chemistry, enabling product yields to be maximized and kinetic models to be generated while the total number of experiments is minimized. This Account opens by reviewing select examples of feedback optimization in flow and applications to chemical research. Systems in the literature are classified into (i) deterministic "black box" optimization systems that do not model the reaction system and are therefore limited in the utility of results for scale-up, (ii) deterministic model-based optimization systems from which reaction kinetics and/or mechanisms can be automatically evaluated, and (iii) stochastic systems. Though diverse in application, flow feedback systems have predominantly focused upon the optimization of continuous variables, i.e., variables such as time, temperature, and concentration that can be ramped from one experiment to the next. Unfortunately, this implies that the screening of discrete variables such as catalyst, ligand, or solvent generally does not factor into automated flow optimization, resulting in incomplete process knowledge. Herein, we present a system and strategy developed for optimizing discrete and continuous variables of a chemical reaction simultaneously. The approach couples automated feedback with high-throughput reaction screening in droplet flow microfluidics. This Account details the system configuration for on-demand creation of sub-20 μL droplets with interchangeable reagents and catalysts. These droplets are reacted in a fully automated microfluidic system and analyzed online by LC/MS. Feeding back from the online analytical results, a design of experiments (DoE)-based adaptive response surface algorithm is employed that deductively removes candidate reagents from the optimization as optimal reaction conditions are refined, leading to rapid convergence. Using the automated optimization platform, case studies are presented for solvent selection in a competitive alkylation chemistry and for catalyst-ligand selection in heteroaromatic Suzuki-Miyaura cross-coupling chemistries. For the monoalkylation of trans-1,2-diaminocyclohexane, polar aprotic solvents at moderate temperatures are shown to be favorable, with optimality accurately identified with dimethyl sulfoxide as the solvent in 67 experiments. For Suzuki-Miyaura cross-couplings, the optimality of precatalysts and continuous variable conditions are observed to change in accordance with the coupling reagents, providing insights into catalyst behavior in the context of the reaction mechanism. Future opportunities in automated reaction development include the incorporation of chemoinformatics for faster analysis and machine-learning algorithms to guide and optimize the synthesis. Adoption of this technology stands to reduce graduate student and postdoc time on routine tasks in the laboratory, while feeding back knowledge used to guide new research directions. Moreover, the application of this technology in industry promises to lessen the cost and time associated with advancing pharmaceutical molecules through development and scale-up.

Towards dial-a-molecule by integrating continuous flow, analytics and self-optimisation

The employment of continuous-flow platforms for synthetic chemistry is becoming increasingly popular in research and industrial environments. Integrating analytics in-line enables obtaining a large amount of information in real-time about the reaction progress, catalytic activity and stability, etc. Furthermore, it is possible to influence the reaction progress and selectivity via manual or automated feedback optimisation, thus constituting a dial-a-molecule approach employing digital synthesis. This contribution gives an overview of the most significant contributions in the field to date.

Thermolysis of 1,3-dioxin-4-ones: fast generation of kinetic data using in-line analysis under flow

Rapid acquisition of kinetic data for thermolysis of 1,3-dioxin-4-ones is demonstrated with a commercial meso-scale flow reactor, using a step-change in flow rate or ‘push-out’ from the flow line.

Online quantitative mass spectrometry for the rapid adaptive optimisation of automated flow reactors

An automated continuous reactor for the synthesis of organic compounds, which uses online mass spectrometry (MS) for reaction monitoring and product quantification, is presented.

Continuous-flow technology—a tool for the safe manufacturing of active pharmaceutical ingredients

In the past few years, continuous-flow reactors with channel dimensions in the micro- or millimeter region have found widespread application in organic synthesis. The characteristic properties of these reactors are their exceptionally fast heat and mass transfer. In microstructured devices of this type, virtually instantaneous mixing can be achieved for all but the fastest reactions. Similarly, the accumulation of heat, formation of hot spots, and dangers of thermal runaways can be prevented. As a result of the small reactor volumes, the overall safety of the process is significantly improved, even when harsh reaction conditions are used. Thus, microreactor technology offers a unique way to perform ultrafast, exothermic reactions, and allows the execution of reactions which proceed via highly unstable or even explosive intermediates. This Review discusses recent literature examples of continuous-flow organic synthesis where hazardous reactions or extreme process windows have been employed, with a focus on applications of relevance to the preparation of pharmaceuticals.