Organic synthesis: march of the machines

Button-Push On-Demand Synthesis for Rapid Optimization of Antiviral Peptidomimetics

The optimization of hit compounds into drug candidates is a pivotal phase in drug discovery but often hampered by cumbersome manual synthesis of derivatives. While automated organic molecule synthesis has enhanced efficiency, safety, and cost-effectiveness, achieving fully automated multistep synthesis remains a formidable challenge due to issues such as solvent and reagent incompatibilities and the accumulation of side-products. We herein demonstrate an automated solid-phase flow platform for synthesizing α-keto-amides and nitrile peptidomimetics, guided by docking simulations, to identify potent broad-spectrum antiviral leads. A compact parallel synthesizer was built in-house, capable of producing 5 distinct molecules per cycle; 525 reactions could be finished within three months to generate 42 derivatives for a structure-activity relationship (SAR) investigation. Among these, ten derivatives exhibited promising target inhibitory activity (IC50 < 100 nM) including two with antiviral activity (EC50 < 250 nM). The platform, coupled with digital chemical recipe files, offers rapid access to a wide range of peptidomimetics, serving as a valuable reservoir for broad-spectrum antiviral candidates. This automated solid-phase flow synthesis approach expedites the generation of previously difficult complex molecular scaffolds. By integration of SPS-flow synthesis with medicinal chemistry campaign, >10-fold target inhibitory activity was achieved from a small set of derivatives, which indicates the potential to shift the paradigm of drug discovery.

A Critical View on the Use of DNA Hydrogels in Cell-Free Protein Synthesis

Numerous studies have reported in the past that the use of protein-encoding DNA hydrogels as templates for cell-free protein synthesis (CFPS) leads to better yields than the use of conventional templates such as plasmids or PCR fragments. Systematic investigation of different types of bulk materials from pure DNA hydrogels and DNA hydrogel composites using a commercially available CFPS kit showed no evidence of improved expression efficiency. However, protein-coding DNA hydrogels were advantageously used in microfluidic reactors as immobilized templates for repetitive protein production, suggesting that DNA-based materials offer potential for future developments in high-throughput profiling or rapid in situ characterization of proteins.

Roboticized AI-assisted microfluidic photocatalytic synthesis and screening up to 10,000 reactions per day

The current throughput of conventional organic chemical synthesis is usually a few experiments for each operator per day. We develop a robotic system for ultra-high-throughput chemical synthesis, online characterization, and large-scale condition screening of photocatalytic reactions, based on the liquid-core waveguide, microfluidic liquid-handling, and artificial intelligence techniques. The system is capable of performing automated reactant mixture preparation, changing, introduction, ultra-fast photocatalytic reactions in seconds, online spectroscopic detection of the reaction product, and screening of different reaction conditions. We apply the system in large-scale screening of 12,000 reaction conditions of a photocatalytic [2 + 2] cycloaddition reaction including multiple continuous and discrete variables, reaching an ultra-high throughput up to 10,000 reaction conditions per day. Based on the data, AI-assisted cross-substrate/photocatalyst prediction is conducted.

Simultaneous reaction- and analytical model building using dynamic flow experiments to accelerate process development

In modern pharmaceutical research, the demand for expeditious development of synthetic routes to active pharmaceutical ingredients (APIs) has led to a paradigm shift towards data-rich process development. Conventional methodologies encompass prolonged timelines for the development of both a reaction model and analytical models. The development of both methods are often separated into different departments and can require an iterative optimization process. Addressing this issue, we introduce an innovative dual modeling approach, combining the development of a Process Analytical Technology (PAT) strategy with reaction optimization. This integrated approach is exemplified in diverse amidation reactions and the synthesis of the API benznidazole. The platform, characterized by a high degree of automation and minimal operator involvement, achieves PAT calibration through a "standard addition" approach. Dynamic experiments are executed to screen a broad process space and gather data for fitting kinetic parameters. Employing an open-source software program facilitates rapid kinetic parameter fitting and additional in silico optimization within minutes. This highly automated workflow not only expedites the understanding and optimization of chemical processes, but also holds significant promise for time and resource savings within the pharmaceutical industry.

Automated self-optimization, intensification, and scale-up of photocatalysis in flow

The optimization, intensification, and scale-up of photochemical processes constitute a particular challenge in a manufacturing environment geared primarily toward thermal chemistry. In this work, we present a versatile flow-based robotic platform to address these challenges through the integration of readily available hardware and custom software. Our open-source platform combines a liquid handler, syringe pumps, a tunable continuous-flow photoreactor, inexpensive Internet of Things devices, and an in-line benchtop nuclear magnetic resonance spectrometer to enable automated, data-rich optimization with a closed-loop Bayesian optimization strategy. A user-friendly graphical interface allows chemists without programming or machine learning expertise to easily monitor, analyze, and improve photocatalytic reactions with respect to both continuous and discrete variables. The system's effectiveness was demonstrated by increasing overall reaction yields and improving space-time yields compared with those of previously reported processes.

When Yield Prediction Does Not Yield Prediction: An Overview of the Current Challenges

Machine Learning (ML) techniques face significant challenges when predicting advanced chemical properties, such as yield, feasibility of chemical synthesis, and optimal reaction conditions. These challenges stem from the high-dimensional nature of the prediction task and the myriad essential variables involved, ranging from reactants and reagents to catalysts, temperature, and purification processes. Successfully developing a reliable predictive model not only holds the potential for optimizing high-throughput experiments but can also elevate existing retrosynthetic predictive approaches and bolster a plethora of applications within the field. In this review, we systematically evaluate the efficacy of current ML methodologies in chemoinformatics, shedding light on their milestones and inherent limitations. Additionally, a detailed examination of a representative case study provides insights into the prevailing issues related to data availability and transferability in the discipline.

AI-driven robotic chemist for autonomous synthesis of organic molecules

The automation of organic compound synthesis is pivotal for expediting the development of such compounds. In addition, enhancing development efficiency can be achieved by incorporating autonomous functions alongside automation. To achieve this, we developed an autonomous synthesis robot that harnesses the power of artificial intelligence (AI) and robotic technology to establish optimal synthetic recipes. Given a target molecule, our AI initially plans synthetic pathways and defines reaction conditions. It then iteratively refines these plans using feedback from the experimental robot, gradually optimizing the recipe. The system performance was validated by successfully determining synthetic recipes for three organic compounds, yielding that conversion rates that outperform existing references. Notably, this autonomous system is designed around batch reactors, making it accessible and valuable to chemists in standard laboratory settings, thereby streamlining research endeavors.

The Power of Automation in Polymer Chemistry: Precision Synthesis of Multiblock Copolymers with Block Sequence Control

Machines can revolutionize the field of chemistry and material science, driving the development of new chemistries, increasing productivity, and facilitating reaction scale up. The incorporation of automated systems in the field of polymer chemistry has however proven challenging owing to the demanding reaction conditions, rendering the automation setup complex and costly. There is an imminent need for an automation platform which uses fast and simple polymerization protocols, while providing a high level of control on the structure of macromolecules via precision synthesis. This work combines an oxygen tolerant, room temperature polymerization method with a simple liquid handling robot to automatically prepare precise and high order multiblock copolymers with unprecedented livingness even after many chain extensions. The highest number of blocks synthesized in such a system is reported, demonstrating the capabilities of this automated platform for the rapid synthesis and complex polymer structure formation.

Transcription between human-readable synthetic descriptions and machine-executable instructions: an application of the latest pre-training technology

AI has been widely applied in scientific scenarios, such as robots performing chemical synthetic actions to free researchers from monotonous experimental procedures. However, there exists a gap between human-readable natural language descriptions and machine-executable instructions, of which the former are typically in numerous chemical articles, and the latter are currently compiled manually by experts. We apply the latest technology of pre-trained models and achieve automatic transcription between descriptions and instructions. We design a concise and comprehensive schema of instructions and construct an open-source human-annotated dataset consisting of 3950 description-instruction pairs, with 9.2 operations in each instruction on average. We further propose knowledgeable pre-trained transcription models enhanced by multi-grained chemical knowledge. The performance of recent popular models and products showing great capability in automatic writing (e.g., ChatGPT) has also been explored. Experiments prove that our system improves the instruction compilation efficiency of researchers by at least 42%, and can generate fluent academic paragraphs of synthetic descriptions when given instructions, showing the great potential of pre-trained models in improving human productivity.

Robotic Modules for the Programmable Chemputation of Molecules and Materials

Before leveraging big data methods like machine learning and artificial intelligence (AI) in chemistry, there is an imperative need for an affordable, universal digitization standard. This mirrors the foundational requisites of the digital revolution, which demanded standard architectures with precise specifications. Recently, we have developed automated platforms tailored for chemical AI-driven exploration, including the synthesis of molecules, materials, nanomaterials, and formulations. Our focus has been on designing and constructing affordable standard hardware and software modules that serve as a blueprint for chemistry digitization across varied fields. Our platforms can be categorized into four types based on their applications: (i) discovery systems for the exploration of chemical space and novel reactivity, (ii) systems for the synthesis and manufacture of fine chemicals, (iii) platforms for formulation discovery and exploration, and (iv) systems for materials discovery and synthesis. We also highlight the convergent evolution of these platforms through shared hardware, firmware, and software alongside the creation of a unique programming language for chemical and material systems. This programming approach is essential for reliable synthesis, designing experiments, discovery, optimization, and establishing new collaboration standards. Furthermore, it is crucial for verifying literature findings, enhancing experimental outcome reliability, and fostering collaboration and sharing of unsuccessful experiments across different research labs.

The central role of density functional theory in the AI age

Density functional theory (DFT) plays a pivotal role in chemical and materials science because of its relatively high predictive power, applicability, versatility, and computational efficiency. We review recent progress in machine learning (ML) model developments, which have relied heavily on DFT for synthetic data generation and for the design of model architectures. The general relevance of these developments is placed in a broader context for chemical and materials sciences. DFT-based ML models have reached high efficiency, accuracy, scalability, and transferability and pave the way to the routine use of successful experimental planning software within self-driving laboratories.

Compact capillary high performance liquid chromatography system for pharmaceutical on-line reaction monitoring

Due to their size, conventional high performance liquid chromatographs (HPLCs) are difficult to place close to a reaction vessel within a pharmaceutical manufacturing or development site. Typically, long transfer lines are required to move sample from the reactor to the HPLC for analysis and high solvent usage is required. However, herein a compact and modular separation system has been developed to enable co-location of an HPLC with a small-scale reactor for reaction monitoring in the synthesis of active pharmaceutical ingredients. Using a framework based on capillary HPLC, a compact gradient separation system with a fully modular architecture is described. A custom miniature diode-array detector with a linear dynamic range (up to 1500 mAU at 210 nm) was integrated and evaluated for on-line reaction monitoring. In evaluating system suitability, average peak area %RSD of <3%, and an average retention time %RSD of <0.7%, were achieved. To demonstrate practical utility, the compact system was coupled directly to an on-line lab-scale flow through reactor for continuous reaction monitoring in the laboratory fume hood, where a study of the 3rd Bourne reaction was used to compare the performance of the compact system with a commercially available process HPLC instrument (Waters PATROL UPLC). Further, 33 off-line samples from a continuous crystallization reactor were analysed and it was found that the developed compact HPLC system showed equivalent quantitative performance to an Agilent 1290 Infinity II HPLC system.

Process Intensification of a Napabucasin Manufacturing Method Utilizing Microflow Chemistry

Microflow chemistry is one of the newest and most efficient technologies used today for the safe and effective production of medicines. In this paper, we show the use of this technology in the development of a manufacturing method for napabucasin, which has potential in the treatment of colorectal and pancreatic cancers. In conventional "batch-type" reactor systems, the generation of side products can be controlled with traditional techniques such as reagent reverse-addition and temperature control. However, there is a limitation to which the yield and purity can be improved by these methods, as both are constrained by the efficiency of heat/mass transfer. Applying microflow chemistry technology alters the parameters of the constraint through the use of precise mixing in a microchannel, which offers increased possibility for improving yields and process intensification of the napabucasin process. Reported herein is a proof-of-concept study for the scale-up production of napabucasin using microflow chemistry techniques for manufacturing at the kilogram scale.

A Review on Artificial Intelligence Enabled Design, Synthesis, and Process Optimization of Chemical Products for Industry 4.0

With the development of Industry 4.0, artificial intelligence (AI) is gaining increasing attention for its performance in solving particularly complex problems in industrial chemistry and chemical engineering. Therefore, this review provides an overview of the application of AI techniques, in particular machine learning, in chemical design, synthesis, and process optimization over the past years. In this review, the focus is on the application of AI for structure-function relationship analysis, synthetic route planning, and automated synthesis. Finally, we discuss the challenges and future of AI in making chemical products.

In-line Multidimensional NMR Monitoring of Photochemical Flow Reactions

This work demonstrates the in-line monitoring of a flow photochemical reaction using 1D and ultrafast 2D NMR methods at high magnetic field. The reaction mixture exiting the flow reactor is flown through the NMR spectrometer and directly analyzed. In the case of simple substrates, suitable information can be obtained through 1D ¹ H spectra, but for molecules of higher complexity the use of 2D experiments is key to address signal overlaps and assignment issues. Here we show the usefulness of ultrafast 2D COSY experiments acquired in 70 s or less, for the in-line monitoring of photochemical reactions, and the possibility to obtain reliable quantitative information. This is a powerful framework to, for example, efficiently screen reaction conditions.

In Situ Quench Reactions of Enantioenriched Secondary Alkyllithium Reagents in Batch and Continuous Flow Using an I/Li-Exchange

We report a practical in situ quench (ISQ) procedure involving the generation of chiral secondary alkyllithiums from secondary alkyl iodides (including functionalized iodides bearing an ester or a nitrile) in the presence of various electrophiles such as aldehydes, ketones, Weinreb amides, isocyanates, sulfides, or boronates. This ISQ-reaction allowed the preparation of a broad range of optically enriched ketones, alcohols, amides, sulfides and boronic acid esters in typically 90-98 % ee. Remarkably, these reactions were performed at -78 °C or -40 °C in batch. A continuous flow set-up permitted reaction temperatures between -20 °C and 0 °C and allowed a scale-up up to a 40-fold without further optimization.

Microreactor Technology: Identifying Focus Fields and Emerging Trends by Using CiteSpace II

Microreactors have gained widespread attention from academia and industrial researchers due to their exceptionally fast mass and heat transfer and flexible control. In this work, CiteSpace software was used to systematically analyze the relevant literature to gain a comprehensively understand on the research status of microreactors in various fields. The results show that the research depth and application scope of microreactors are continuing to expand. The top 10 most popular research fields are photochemistry, pharmaceutical intermediates, multistep flow synthesis, mass transfer, computational fluid dynamics, μ-TAS (micro total analysis system), nanoparticles, biocatalysis, hydrogen production, and solid-supported reagents. The evolution trends of current focus areas are examined, including photochemistry, mass transfer, biocatalysis and hydrogen production and their milestone literature is analyzed in detail. This article demonstrates the development of different fields of microreactors technology and highlights the unending opportunities and challenges offered by this fascinating technology.

Inline purification in continuous flow synthesis – opportunities and challenges

Continuous flow technology has become the method of choice for many academic and industrial researchers when developing new routes to chemical compounds of interest. With this technology maturing over the last decades, robust and oftentimes automated processes are now commonly exploited to generate fine chemical building blocks. The integration of effective inline analysis and purification tools is thereby frequently exploited to achieve effective and reliable flow processes. This perspective article summarizes recent applications of different inline purification techniques such as chromatography, extractions, and crystallization from academic and industrial laboratories. A discussion of the advantages and drawbacks of these tools is provided as a guide to aid researchers in selecting the most appropriate approach for future applications. It is hoped that this perspective contributes to new developments in this field in the context of process and cost efficiency, sustainability and industrial uptake of new flow chemistry tools developed in academia.

On-the-Fly Mass Spectrometry in Digital Microfluidics Enabled by a Microspray Hole: Toward Multidimensional Reaction Monitoring in Automated Synthesis Platforms

We report an approach for the online coupling of digital microfluidics (DMF) with mass spectrometry (MS) using a chip-integrated microspray hole (μSH). The technique uses an adapted electrostatic spray ionization (ESTASI) method to spray a portion of a sample droplet through a microhole in the cover plate, allowing its chemical content to be analyzed by MS. This eliminates the need for chip disassembly or the introduction of capillary emitters for MS analysis, as required by state-of-the-art. For the first time, this allows the essential advantage of a DMF device─free droplet movement─to be retained during MS analysis. The broad applicability of the developed seamless coupling of DMF and mass spectrometry was successfully applied to the study of various on-chip organic syntheses as well as protein and peptide analysis. In the case of a Hantzsch synthesis, we were able to show that the method is very well suited for monitoring even rapid chemical reactions that are completed in a few seconds. In addition, the strength of the low resource consumption in such on-chip microsyntheses was demonstrated by the example of enzymatic brominations, for which only a minute amount of a special haloperoxidase is required in the droplet. The unique selling point of this approach is that the analyzed droplet remains completely movable after the MS measurement and is available for subsequent on-DMF chip processes. This is illustrated here for the example of MS analysis of the starting materials in the corresponding droplets before they are combined to investigate the reaction progress by DMF-MS further. This technology enables the ongoing and almost unlimited tracking of multistep chemical processes in a DMF chip and offers exciting prospects for transforming digital microfluidics into automated synthesis platforms.

Dataset of solution-based inorganic materials synthesis procedures extracted from the scientific literature

The development of a materials synthesis route is usually based on heuristics and experience. A possible new approach would be to apply data-driven approaches to learn the patterns of synthesis from past experience and use them to predict the syntheses of novel materials. However, this route is impeded by the lack of a large-scale database of synthesis formulations. In this work, we applied advanced machine learning and natural language processing techniques to construct a dataset of 35,675 solution-based synthesis procedures extracted from the scientific literature. Each procedure contains essential synthesis information including the precursors and target materials, their quantities, and the synthesis actions and corresponding attributes. Every procedure is also augmented with the reaction formula. Through this work, we are making freely available the first large dataset of solution-based inorganic materials synthesis procedures.

Measurement(s)	solution-based inorganic synthesis data
Technology Type(s)	natural language processing

Ketones as strategic building blocks for the synthesis of natural product-inspired compounds

Natural product-inspired compound collections serve as excellent sources for the identification of new bioactive compounds to treat disease. However, such compounds must necessarily be more structurally-enriched than traditional screening compounds, therefore inventive synthetic strategies and reliable methods are needed to prepare them. Amongst the various possible starting materials that could be considered for the synthesis of natural product-inspired compounds, ketones can be especially valuable due to the vast variety of complexity-building synthetic transformations that they can take part in, their high prevalence as commercial building blocks, and relative ease of synthesis. With a view towards developing a unified synthetic strategy for the preparation of next generation bioactive compound collections, this review considers whether ketones could serve as general precursors in this regard, and summarises the opulence of synthetic transformations available for the annulation of natural product ring-systems to ketone starting materials.

Understanding flow chemistry for the production of active pharmaceutical ingredients

Multi-step organic syntheses of various drugs, active pharmaceutical ingredients, and other pharmaceutically and agriculturally important compounds have already been reported using flow synthesis. Compared to batch, hazardous and reactive reagents can be handled safely in flow. This review discusses the pros and cons of flow chemistry in today’s scenario and recent developments in flow devices. The review majorly emphasizes on the recent developments in the flow synthesis of pharmaceutically important products in last five years including flibanserin, imatinib, buclizine, cinnarizine, cyclizine, meclizine, ribociclib, celecoxib, SC-560 and mavacoxib, efavirenz, fluconazole, melitracen HCl, rasagiline, tamsulosin, valsartan, and hydroxychloroquine. Critical steps and new development in the flow synthesis of selected compounds are also discussed.

From Platform to Knowledge Graph: Evolution of Laboratory Automation

High-fidelity computer-aided experimentation is becoming more accessible with the development of computing power and artificial intelligence tools. The advancement of experimental hardware also empowers researchers to reach a level of accuracy that was not possible in the past. Marching toward the next generation of self-driving laboratories, the orchestration of both resources lies at the focal point of autonomous discovery in chemical science. To achieve such a goal, algorithmically accessible data representations and standardized communication protocols are indispensable. In this perspective, we recategorize the recently introduced approach based on Materials Acceleration Platforms into five functional components and discuss recent case studies that focus on the data representation and exchange scheme between different components. Emerging technologies for interoperable data representation and multi-agent systems are also discussed with their recent applications in chemical automation. We hypothesize that knowledge graph technology, orchestrating semantic web technologies and multi-agent systems, will be the driving force to bring data to knowledge, evolving our way of automating the laboratory.

Sonochemical Protocols for Heterocyclic Synthesis: A Representative Review

In the present era of the industrial revolution, we all are familiar with ever-increasing environmental pollution released from various chemical processes. Chemical production has had a severe impact on the environment and human health. For the betterment of our environment, the chemical community has turned their interest to developing green, harmless and sustainable synthetic processes. To accomplish these goals of green chemistry, the extraordinary properties of sonication play an important role. It is well known that sonochemistry can make decisive contributions to creating high pressures of almost 1000 atm and very high temperatures in the range of 4500-5000 °C. The implementation of ultrasound in chemical transformations somehow fulfils the measures of green chemistry, as it reduces energy consumption, enhances product selectivity, and uses lesser amounts of hazardous chemicals and solvents. Furthermore, heterocyclic synthesis under ultrasonication offers several environmental and process-related advantages compared with conventional methods. The remarkable contribution of ultrasonics to the development of green and sustainable synthetic routes inspired us to write this article. Herein, we have discussed only some of the various synthetic methodologies developed for the construction of heterocyclic cores under ultrasonic irradiation, accompanied by mechanistic insights. In some cases, a comparison between sonochemical conditions and conventional conditions has also been investigated. We emphasized principally 'up to date' developments on various sono-accelerated chemical transformations comprising aza-Michael, aldol reactions, C-C couplings, oxidation, cycloadditions, multi-component reactions, etc. for the synthesis of heterocycles.

Machine Learning at the Interface of Polymer Science and Biology: How Far Can We Go?

This Perspective outlines recent progress and future directions for using machine learning (ML), a data-driven method, to address critical questions in the design, synthesis, processing, and characterization of biomacromolecules. The achievement of these tasks requires the navigation of vast and complex chemical and biological spaces, difficult to accomplish with reasonable speed. Using modern algorithms and supercomputers, quantum physics methods are able to examine systems containing a few hundred interacting species and determine the probability of finding them in a particular region of phase space, thereby anticipating their properties. Likewise, modern approaches in chemistry and biomolecular simulation, supported by high performance computing, have culminated in producing data sets of escalating size and intrinsically high complexity. Hence, using ML to extract relevant information from these fields is of paramount importance to advance our understanding of chemical and biomolecular systems. At the heart of ML approaches lie statistical algorithms, which by evaluating a portion of a given data set, identify, learn, and manipulate the underlying rules that govern the whole data set. The assembly of a quality model to represent the data followed by the predictions and elimination of error sources are the key steps in ML. In addition to a growing infrastructure of ML tools to address complex problems, an increasing number of aspects related to our understanding of the fundamental properties of biomacromolecules are exposed to ML. These fields, including those residing at the interface of polymer science and biology (i.e., structure determination, de novo design, folding, and dynamics), strive to adopt and take advantage of the transformative power offered by approaches in the ML domain, which clearly has the potential of accelerating research in the field of biomacromolecules.

Automated Multi-Objective Reaction Optimisation: Which Algorithm Should I Use?

Multi-objective optimisation algorithms (MOOAs) are, of increasing interest for the efficient optimisation of chemical processes. However, an algorithms performance can vary on a case-by-case basis, depending on the complexity of...

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.

Autonomous polymer synthesis delivered by multi-objective closed-loop optimisation

Application of artificial intelligence and machine learning for polymer discovery offers an opportunity to meet the drastic need for the next generation high performing and sustainable polymer materials. Here, these...

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.

Towards 4th industrial revolution efficient and sustainable continuous flow manufacturing of active pharmaceutical ingredients

The convergence of end-to-end continuous flow synthesis with downstream processing, process analytical technology (PAT), artificial intelligence (AI), machine learning and automation in ensuring improved accessibility of quality medicines on demand.

Autonomous model-based experimental design for rapid reaction development

To automate and democratize model-based experimental design for flow chemistry applications, we report the development of open-source software, Optipus. Reaction models are built in an iterative and automated fashion, for rapid reaction development.

Accelerating Manufacturing for Biomass Conversion via Integrated Process and Bench Digitalization: A Perspective

We present a perspective for accelerating biomass manufacturing via digitalization. We summarize the challenges for manufacturing and identify areas where digitalization can help. A profound potential in using lignocellulosic biomass...

An integrated resource-efficient microfluidic device for parallelised studies of immobilised chiral catalysts in continuous flow via miniaturized LC/MS-analysis

Dual-μReactor catalysis screening: a novel method combining multiple miniaturized packed-bed reactors and on-line HPLC/MS-analysis on one single microfluidic device.

Is Aromaticity a Driving Force in Catalytic Cycles? A Case from the Cycloisomerization of Enynes Catalyzed by All-Metal Aromatic Pd3+ Clusters and Carboxylic Acids

The work details a mechanistic study based on density functional theory modeling on the cycloisomerization of polyunsaturated substrates catalyzed by all-metal aromatic tripalladium complexes and carboxylic acids. These clusters are an emerging class of catalysts for a variety of relevant transformations, including C-C forming processes that occur under mild conditions and display synthetic features complementary to those of established mononuclear complexes. This study is the first computational one devoted to the comprehension of the series of elementary steps involved in a synthetic transformation catalyzed by an all-metal aromatic complex. Present results confirm previous experimental hints on the striking mechanistic differences exerted by these clusters with respect to the usual cyclization pathways of related substrates. Moreover, the catalytic cycle involving present all-metal aromatic clusters closely parallels the mechanism of the aromatic substitution of regular arenes.

Regioselective Magnesiations and Zincations of Aromatics and Heterocycles Triggered by Lewis Acids

Mixed TMP‐bases (TMP=2,2,6,6‐tetramethylpiperidyl), such as TMPMgCl ⋅ LiCl, TMP₂Mg ⋅ 2LiCl, TMPZnCl ⋅ LiCl and TMP₂Zn ⋅ 2LiCl, are outstanding reagents for the metalation of functionalized aromatics and heterocycles. In the presence of Lewis acids, such as BF₃ ⋅ OEt₂ or MgCl₂, the metalation scope of such bases was dramatically increased, and regioselectivity switches were achieved in the presence or absence of these Lewis acids. Furthermore, highly reactive lithium bases, such as TMPLi or Cy₂NLi, are also compatible with various Lewis acids, such as MgCl₂ ⋅ 2LiCl, ZnCl₂ ⋅ 2LiCl or CuCN ⋅ 2LiCl. Performing such metalations in continuous flow using commercial setups permitted practical and convenient reaction conditions.

Chemputation and the Standardization of Chemical Informatics

The explosion in the use of machine learning for automated chemical reaction optimization is gathering pace. However, the lack of a standard architecture that connects the concept of chemical transformations universally to software and hardware provides a barrier to using the results of these optimizations and could cause the loss of relevant data and prevent reactions from being reproducible or unexpected findings verifiable or explainable. In this Perspective, we describe how the development of the field of digital chemistry or chemputation, that is the universal code-enabled control of chemical reactions using a standard language and ontology, will remove these barriers allowing users to focus on the chemistry and plug in algorithms according to the problem space to be explored or unit function to be optimized. We describe a standard hardware (the chemical processing programming architecture-the ChemPU) to encompass all chemical synthesis, an approach which unifies all chemistry automation strategies, from solid-phase peptide synthesis, to HTE flow chemistry platforms, while at the same time establishing a publication standard so that researchers can exchange chemical code (χDL) to ensure reproducibility and interoperability. Not only can a vast range of different chemistries be plugged into the hardware, but the ever-expanding developments in software and algorithms can also be accommodated. These technologies, when combined will allow chemistry, or chemputation, to follow computation-that is the running of code across many different types of capable hardware to get the same result every time with a low error rate.

Forgotten and forbidden chemical reactions revitalised through continuous flow technology

Continuous flow technology has played an undeniable role in enabling modern chemical synthesis, whereby a myriad of reactions can now be performed with greater efficiency, safety and control. As flow chemistry furthermore delivers more sustainable and readily scalable routes to important target structures a growing number of industrial applications are being reported. In this review we highlight the impact of flow chemistry on revitalising important chemical reactions that were either forgotten soon after their initial report as necessary improvements were not realised due to a lack of available technology, or forbidden due to unacceptable safety concerns relating to the experimental procedure. In both cases flow processing in combination with further reaction optimisation has rendered a powerful set of tools that make such transformations not only highly efficient but moreover very desirable due to a more streamlined construction of desired scaffolds. This short review highlights important contributions from academic and industrial laboratories predominantly from the last 5 years allowing the reader to gain an appreciation of the impact of flow chemistry.

Automated High-Pressure Atline Analysis of Photo-High-P,T Vitamin D3 Microfluidic Synthesis

Process analytical technology has become a relevant topic in both industry and academia as a mechanism to control process quality by measuring critical parameters; being mainly applied in pharmaceutical industry. An emerging topic is process monitoring with subsequent process automation in flow chemistry using inline, online and atline analyzers. Flow chemistry often deliberately and favorably uses harsh conditions (termed Novel Process Windows) to achieve process intensification which raises the need for sampling under these conditions. This demands for setting in place a stabilization of the sample before exposing it to the processing. Ignoring this may result in being unable to use inline/online analytics and posing the need for a separation step before quantitative analysis, leaving atline analysis as the only feasible option. That means that sampling and connected operations need also to be automated. This is where this study sets in, and this is enabled by a modified high-performance liquid chromatography (HPLC) autosampler coupled to the photo-high-p,T flow synthesis of vitamin D3. It shows that sampling variables, such as decompression speed, can be even more critical in terms of variability of results than process variables such as concentration, pressure, and temperature. The modification enabled the autosampler fully automated and unattended sampling from the reactor and enabled pressure independent measurements with 89% accuracy, &gt;95% reproducibility, and &gt;96% repeatability, stating decompression speed as the primary responsibility for measurements’ uncertainty.