Printed polymer platform empowering machine-assisted chemical synthesis in stacked droplets

Efficiently exploring organic molecules through multi-step processes demands a transition from conventional laboratory synthesis to automated systems. Existing platforms for machine-assistant synthetic workflows compatible with multiple liquid-phases require substantial engineering investments for setup, thereby hindering quick customization and throughput increasement. Here we present a droplet-based chip that facilitates the self-organization of various liquid phases into stacked layers for conducting chemical transformations. The chip's precision polymer printing capability, enabled by digital micromirror device (DMD)-maskless photolithography and dual post-chemical modifications, allows it to create customized, sub-10 µm featured patterns to confine diverse liquids, regardless of density, within each droplet. The robustness and open design of surface-templated liquid layers actualize machine-assistant droplet manipulation, synchronous reaction triggering, local oscillation, and real-time monitoring of individual layers into a reality. We propose that, with further integration of machine operation line and self-learning, this droplet-based platform holds the potential to become a valuable addition to the toolkit of chemistry process, operating autonomously and with high-throughput.

Combining Bayesian optimization and automation to simultaneously optimize reaction conditions and routes

Reaching optimal reaction conditions is crucial to achieve high yields, minimal by-products, and environmentally sustainable chemical reactions. With the recent rise of artificial intelligence, there has been a shift from traditional Edisonian trial-and-error optimization to data-driven and automated approaches, which offer significant advantages. Here, we showcase the capabilities of an integrated platform; we conducted simultaneous optimizations of four different terminal alkynes and two reaction routes using an automation platform combined with a Bayesian optimization platform. Remarkably, we achieved a conversion rate of over 80% for all four substrates in 23 experiments, covering ca. 0.2% of the combinatorial space. Further analysis allowed us to identify the influence of different reaction parameters on the reaction outcomes, demonstrating the potential for expedited reaction condition optimization and the prospect of more efficient chemical processes in the future.

3D printing processes in precise drug delivery for personalized medicine

With the advent of personalized medicine, the drug delivery system will be changed significantly. The development of personalized medicine needs the support of many technologies, among which three-dimensional printing (3DP) technology is a novel formulation-preparing process that creates 3D objects by depositing printing materials layer-by-layer based on the computer-aided design method. Compared with traditional pharmaceutical processes, 3DP produces complex drug combinations, personalized dosage, and flexible shape and structure of dosage forms (DFs) on demand. In the future, personalized 3DP drugs may supplement and even replace their traditional counterpart. We systematically introduce the applications of 3DP technologies in the pharmaceutical industry and summarize the virtues and shortcomings of each technique. The release behaviors and control mechanisms of the pharmaceutical DFs with desired structures are also analyzed. Finally, the benefits, challenges, and prospects of 3DP technology to the pharmaceutical industry are discussed.

MicroCycle: An Integrated and Automated Platform to Accelerate Drug Discovery

We herein describe the development and application of a modular technology platform which incorporates recent advances in plate-based microscale chemistry, automated purification, in situ quantification, and robotic liquid handling to enable rapid access to high-quality chemical matter already formatted for assays. In using microscale chemistry and thus consuming minimal chemical matter, the platform is not only efficient but also follows green chemistry principles. By reorienting existing high-throughput assay technology, the platform can generate a full package of relevant data on each set of compounds in every learning cycle. The multiparameter exploration of chemical and property space is hereby driven by active learning models. The enhanced compound optimization process is generating knowledge for drug discovery projects in a time frame never before possible.

An autonomous laboratory for the accelerated synthesis of novel materials

To close the gap between the rates of computational screening and experimental realization of novel materials1,2, we introduce the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind. Synthesis recipes were proposed by natural-language models trained on the literature and optimized using an active-learning approach grounded in thermodynamics. Analysis of the failed syntheses provides direct and actionable suggestions to improve current techniques for materials screening and synthesis design. The high success rate demonstrates the effectiveness of artificial-intelligence-driven platforms for autonomous materials discovery and motivates further integration of computations, historical knowledge and robotics.

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.

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.

A large conserved family of small-molecule carboxyl methyltransferases identified from microorganisms

Small-molecule carboxyl methyltransferases (CbMTs) constitute a small proportion of the reported methyltransferases, but they have received extensive attention due to their important physiological functions. Most of the small-molecule CbMTs isolated to date originate from plants and are members of the SABATH family. In this study, we identified a type of CbMT (OPCMT) from a group of Mycobacteria, which has a distinct catalytic mechanism from the SABATH methyltransferases. The enzyme contains a large hydrophobic substrate-binding pocket (~400 Å3) and utilizes two conserved residues, Thr20 and Try194, to retain the substrate in a favorable orientation for catalytic transmethylation. The OPCMT_like MTs have a broad substrate scope and can accept diverse carboxylic acids enabling efficient production of methyl esters. They are widely (more than 10,000) distributed in microorganisms, including several well-known pathogens, whereas no related genes are found in humans. In vivo experiments implied that the OPCMT_like MTs was indispensable for M. neoaurum, suggesting that these proteins have important physiological functions.

3D-Printing of Capsule Devices as Compartmentalization Tools for Supported Reagents in the Search of Antiproliferative Isatins

The application of high throughput synthesis methodologies in the generation of active pharmaceutical ingredients (APIs) currently requires the use of automated and easily scalable systems, easy dispensing of supported reagents in solution phase organic synthesis (SPOS), and elimination of purification and extraction steps. The recyclability and recoverability of supported reagents and/or catalysts in a rapid and individualized manner is a challenge in the pharmaceutical industry. This objective can be achieved through a suitable compartmentalization of these pulverulent reagents in suitable devices for it. This work deals with the use of customized polypropylene permeable-capsule devices manufactured by 3D printing, using the fused deposition modeling (FDM) technique, adaptable to any type of flask or reactor. The capsules fabricated in this work were easily loaded "in one step" with polymeric reagents for use as scavengers of isocyanides in the work-up process of Ugi multicomponent reactions or as compartmentalized and reusable catalysts in copper-catalyzed cycloadditions (CuAAC) or Heck palladium catalyzed cross-coupling reactions (PCCCRs). The reaction products are different series of diversely substituted isatins, which were tested in cancerous cervical HeLa and murine 3T3 Balb fibroblast cells, obtaining potent antiproliferative activity. This work demonstrates the applicability of 3D printing in chemical processes to obtain anticancer APIs.

Miniature glass-metal coaxial waveguide reactors for microwave-assisted liquid heating

<abstract> <p>Microwave (MW) irradiation is recognized as an effective tool in industries related to pharmaceuticals, chemistry, nanoparticle synthesis, food, etc. In the hardware field, some research efforts are concentrated on creating miniature reactors using low-cost technologies aimed at on-demand chemistry or parallel synthesis of many drugs.</p> <p>This paper reports on the development and characterization of novel miniature chemical-resistant glass-metal coaxial reactors based on a modified Liebig condenser. It is composed of two concentric glass tubes, one for the central conductor carrying MW current, and the other for the copper-foiled cylinder surrounding the first pipe. The gap between them is filled with a liquid that is pumped and evacuated by using shielded thin inlet/outlet glass tubes, which are melted and opened into this cylindrical cavity. The reactor's geometry allows for the direct soldering of miniature MW SMA coaxial connectors of 50-Ω impedance.</p> <p>The developed components are studied analytically, numerically and experimentally. The frequency properties of reactors are measured with a network analyzer. The temperature trends are explored by using a variable high-power MW generator, power meters and temperature sensors.</p> <p>These reactors demonstrate their relative insensitivity toward variations in the permittivity of filling liquids in the range of $3.75 &lt; \varepsilon &lt; 30$, as shown in simulations and measurements. They demonstrate the increase by two orders in the longitudinal modal penetration depth and a more homogeneous heating along reactors as compared to their hollow coaxial prototypes.</p> <p>These glass-metal miniature reactors can be used in on-demand continuous-flow accelerated liquid heating, chemistry and pharmacy.</p> </abstract>

A Multi-Objective Active Learning Platform and Web App for Reaction Optimization

We report the development of an open-source experimental design via Bayesian optimization platform for multi-objective reaction optimization. Using high-throughput experimentation (HTE) and virtual screening data sets containing high-dimensional continuous and discrete variables, we optimized the performance of the platform by fine-tuning the algorithm components such as reaction encodings, surrogate model parameters, and initialization techniques. Having established the framework, we applied the optimizer to real-world test scenarios for the simultaneous optimization of the reaction yield and enantioselectivity in a Ni/photoredox-catalyzed enantioselective cross-electrophile coupling of styrene oxide with two different aryl iodide substrates. Starting with no previous experimental data, the Bayesian optimizer identified reaction conditions that surpassed the previously human-driven optimization campaigns within 15 and 24 experiments, for each substrate, among 1728 possible configurations available in each optimization. To make the platform more accessible to nonexperts, we developed a graphical user interface (GUI) that can be accessed online through a web-based application and incorporated features such as condition modification on the fly and data visualization. This web application does not require software installation, removing any programming barrier to use the platform, which enables chemists to integrate Bayesian optimization routines into their everyday laboratory practices.

Digitizing Chemical Synthesis in 3D Printed Reactionware

Chemistry digitization requires an unambiguous link between experiments and the code used to generate the experimental conditions and outcomes, yet this process is not standardized, limiting the portability of any chemical code. What is needed is a universal approach to aid this process using a well-defined standard that is composed of syntheses that are employed in modular hardware. Herein we present a new approach to the digitization of organic synthesis that combines process chemistry principles with 3D printed reactionware. This approach outlines the process for transforming unit operations into digitized hardware and well-defined instructions that ensure effective synthesis. To demonstrate this, we outline the process for digitizing 3 MIDA boronate building blocks, an ester hydrolysis, a Wittig olefination, a Suzuki-Miyaura coupling reaction, and synthesis of the drug sulfanilamide.

Exploration of Ultralarge Compound Collections for Drug Discovery

Designing new medicines more cheaply and quickly is tightly linked to the quest of exploring chemical space more widely and efficiently. Chemical space is monumentally large, but recent advances in computer software and hardware have enabled researchers to navigate virtual chemical spaces containing billions of chemical structures. This review specifically concerns collections of many millions or even billions of enumerated chemical structures as well as even larger chemical spaces that are not fully enumerated. We present examples of chemical libraries and spaces and the means used to construct them, and we discuss new technologies for searching huge libraries and for searching combinatorially in chemical space. We also cover space navigation techniques and consider new approaches to de novo drug design and the impact of the "autonomous laboratory" on synthesis of designed compounds. Finally, we summarize some other challenges and opportunities for the future.

Digitalization in pharmaceutical industry: What to focus on under the digital implementation process?

Digitalization of any manufacture industry is a key step in any progress of the production process. The process of digitalization includes both increased use of robotics, automatization solutions and computerization, thereby allowing to reduce costs, to improve efficiency and productivity, and to be flexible to changes. Pharmaceutical Industry (PI) has however been resistant to digitalization, mainly due to fair experience and complexity of the entailed development and manufacture processes. Nevertheless, there is a clear need to digitalize PI as the demand in both traditional and new drugs is constantly growing. Contract Development Manufacture Organizations (CDMOs) have a special digitalizing challenge. Digitalization of PI, and CDMO precisely, should be tightly related to the main aspects of Good Manufacture Practice (GMP), and, to succeed in PI digitalizing requires constant focus on GMP. Close collaboration with constantly changing stakeholders is another important factor which should be in focus during digitalization of CDMO. This paper represents an overview over the main aspects of CDMO digitalization and discusses both the opportunities and challenges of the process, focusing on the practical solutions for successive digital implementation.

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.

3D Printing to Increase the Flexibility of the Chemical Synthesis of Biologically Active Molecules: Design of On-Demand Gas Generation Reactors

The development of new drugs is accelerated by rapid access to functionalized and D-labeled molecules with improved activity and pharmacokinetic profiles. Diverse synthetic procedures often involve the usage of gaseous reagents, which can be a difficult task due to the requirement of a dedicated laboratory setup. Here, we developed a special reactor for the on-demand production of gases actively utilized in organic synthesis (C2H2, H2, C2D2, D2, and CO2) that completely eliminates the need for high-pressure equipment and allows for integrating gas generation into advanced laboratory practice. The reactor was developed by computer-aided design and manufactured using a conventional 3D printer with polypropylene and nylon filled with carbon fibers as materials. The implementation of the reactor was demonstrated in representative reactions with acetylene, such as atom-economic nucleophilic addition (conversions of 19-99%) and nickel-catalyzed S-functionalization (yields 74-99%). One of the most important advantages of the reactor is the ability to generate deuterated acetylene (C2D2) and deuterium gas (D2), which was used for highly significant, atom-economic and cost-efficient deuterium labeling of S,O-vinyl derivatives (yield 68-94%). Successful examples of their use in organic synthesis are provided to synthesize building blocks of heteroatom-functionalized and D-labeled biologically active organic molecules.

Current and future trends of additive manufacturing for chemistry applications: a review

Three-dimensional (3-D) printing, also known as additive manufacturing, refers to a method used to generate a physical object by joining materials in a layer-by-layer process from a three-dimensional virtual model. 3-D printing technology has been traditionally employed in rapid prototyping, engineering, and industrial design. More recently, new applications continue to emerge; this is because of its exceptional advantage and flexibility over the traditional manufacturing process. Unlike other conventional manufacturing methods, which are fundamentally subtractive, 3-D printing is additive and, therefore, produces less waste. This review comprehensively summarises the application of additive manufacturing technologies in chemistry, chemical synthesis, and catalysis with particular attention to the production of general laboratory hardware, analytical facilities, reaction devices, and catalytically active substances. It also focuses on new and upcoming applications such as digital chemical synthesis, automation, and robotics in a synthetic environment. While discussing the contribution of this research area in the last decade, the current, future, and economic opportunities of additive manufacturing in chemical research and material development were fully covered.

Expanding the Tool Kit of Automated Flow Synthesis: Development of In-line Flash Chromatography Purification

Recent advancements in in-line extraction and purification technology have enabled complex multistep synthesis in continuous flow reactor systems. However, for the large scope of chemical reactions that yield mixtures of products or residual starting materials, off-line purification is still required to isolate the desired compound. We present the in-line integration of a commercial automated flash chromatography system with a flow reactor for the continuous synthesis and isolation of product(s). A proof-of-principle study was performed to validate the system and test the durability of the column cartridges, performing an automated sequence of 100 runs over 2 days. Three diverse reaction systems that highlight the advantages of flow synthesis were successfully applied with in-line normal- or reversed-phase flash chromatography, continuously isolating products with 97-99% purity. Productivity of up to 9.9 mmol/h was achieved, isolating gram quantities of pure product from a feed of crude reaction mixture. Herein, we describe the development and optimization of the systems and suggest guidelines for selecting reactions well suited to in-line flash chromatography.

Composite Materials Manufactured by Photopolymer 3D Printing with Metal-Organic Frameworks

Abstract

New composite materials containing metal-organic framework (MOF-5) particles were manufactured by 3D printing. The optimal composition of the photopolymer formulation and printing conditions ensuring the highest quality of printing were selected. Retention of the metal-organic framework (MOF) structure in the resulting composite objects was demonstrated by powder X-ray diffraction. The distribution of MOF-5 particles over the whole bulk of the 3D product was studied by X-ray computed tomography. In the future, composite materials of this type containing catalytically active MOFs, with their structure and properties being controllable at the micro and macro levels, could find application as catalysts of various chemical processes.

3D Printing of Monolithic Capillarity-Driven Microfluidic Devices for Diagnostics

Rapid diagnostic testing at the site of the patient is essential when a fully equipped laboratory is not accessible. To maximize the impact of this approach, low-cost, disposable tests that require minimal user-interference and external equipment are desired. Fluid transport by capillary wicking removes the need for bulky ancillary equipment to actuate and control fluid flow. Nevertheless, current microfluidic paper-based analytical devices based on this principle struggle with the implementation of multistep diagnostic protocols because of fabrication-related issues. Here, 3D-printed microfluidic devices are demonstrated in a proof-of-concept enzyme-linked immunosorbent assay in which a multistep assay timeline is completed by precisely engineering capillary wetting within printed porous bodies. 3D printing provides a scalable route to low-cost microfluidic devices and obviates the assembly of discrete components. The resulting rapid and seamless transition between digital data and physical objects allows for rapid design iterations, and opens up perspectives on distributed manufacturing.

High-Throughput Methods in the Discovery and Study of Biomaterials and Materiobiology

The complex interaction of cells with biomaterials (i.e., materiobiology) plays an increasingly pivotal role in the development of novel implants, biomedical devices, and tissue engineering scaffolds to treat diseases, aid in the restoration of bodily functions, construct healthy tissues, or regenerate diseased ones. However, the conventional approaches are incapable of screening the huge amount of potential material parameter combinations to identify the optimal cell responses and involve a combination of serendipity and many series of trial-and-error experiments. For advanced tissue engineering and regenerative medicine, highly efficient and complex bioanalysis platforms are expected to explore the complex interaction of cells with biomaterials using combinatorial approaches that offer desired complex microenvironments during healing, development, and homeostasis. In this review, we first introduce materiobiology and its high-throughput screening (HTS). Then we present an in-depth of the recent progress of 2D/3D HTS platforms (i.e., gradient and microarray) in the principle, preparation, screening for materiobiology, and combination with other advanced technologies. The Compendium for Biomaterial Transcriptomics and high content imaging, computational simulations, and their translation toward commercial and clinical uses are highlighted. In the final section, current challenges and future perspectives are discussed. High-throughput experimentation within the field of materiobiology enables the elucidation of the relationships between biomaterial properties and biological behavior and thereby serves as a potential tool for accelerating the development of high-performance biomaterials.

Natural Products, the Fourth Industrial Revolution, and the Quintuple Helix

The profound interconnectedness of the sciences and technologies embodied in the Fourth Industrial Revolution is discussed in terms of the global role of natural products, and how that interplays with the development of sustainable and climate-conscious practices of cyberecoethnopharmacolomics within the Quintuple Helix for the promotion of a healthier planet and society.

Automatic Generation of 3D-Printed Reactionware for Chemical Synthesis Digitization using ChemSCAD

We describe a system, ChemSCAD, for the creation of digital reactors based on the chemical operations, physical parameters, and synthetic sequence to produce a given target compound, to show that the system can translate the gram-scale batch synthesis of the antiviral compound Ribavirin (yield 43% over three steps), the narcolepsy drug Modafinil (yield 60% over three steps), and both batch and flow instances of the synthesis of the anticancer agent Lomustine (batch yield 65% over two steps) in purities greater than or equal to 96%. The syntheses of compounds developed using the ChemSCAD system, including reactor designs and analytical data, can be stored in a database repository, with the information necessary to critically evaluate and improve upon reactionware syntheses being easily shared and versioned.

Automatic Generation of 3D-Printed Reactionware for Chemical Synthesis Digitization using ChemSCAD

We describe a system, ChemSCAD, for the creation of digital reactors based on the chemical operations, physical parameters, and synthetic sequence to produce a given target compound, to show that the system can translate the gram-scale batch synthesis of the antiviral compound Ribavirin (yield 43% over three steps), the narcolepsy drug Modafinil (yield 60% over three steps), and both batch and flow instances of the synthesis of the anticancer agent Lomustine (batch yield 65% over two steps) in purities greater than or equal to 96%. The syntheses of compounds developed using the ChemSCAD system, including reactor designs and analytical data, can be stored in a database repository, with the information necessary to critically evaluate and improve upon reactionware syntheses being easily shared and versioned.

Automated Experimentation Powers Data Science in Chemistry

Data science has revolutionized chemical research and continues to break down barriers with new interdisciplinary studies. The introduction of computational models and machine learning (ML) algorithms in combination with automation and traditional experimental techniques has enabled scientific advancement across nearly every discipline of chemistry, from materials discovery, to process optimization, to synthesis planning. However, predictive tools powered by data science are only as good as their data sets and, currently, many of the data sets used to train models suffer from several limitations, including being sparse, limited in scope and requiring human curation. Likewise, computational data faces limitations in terms of accurate modeling of nonideal systems and can suffer from low translation fidelity from simulation to real conditions. The lack of diverse data and the need to be able to test it experimentally reduces both the accuracy and scope of the predictive models derived from data science. This Account contextualizes the need for more complex and diverse experimental data and highlights how the seamless integration of robotics, machine learning, and data-rich monitoring techniques can be used to access it with minimal human labor.We propose three broad categories of data in chemistry: data on fundamental properties, data on reaction outcomes, and data on reaction mechanics. We highlight flexible, automated platforms that can be deployed to acquire and leverage these data. The first platform combines solid- and liquid-dosing modules with computer vision to automate solubility screening, thereby gathering fundamental data that are necessary for almost every experimental design. Using computer vision offers the additional benefit of creating a visual record, which can be referenced and used to further interrogate and gain insight on the data collected. The second platform iteratively tests reaction variables proposed by a ML algorithm in a closed-loop fashion. Experimental data related to reaction outcomes are fed back into the algorithm to drive the discovery and optimization of new materials and chemical processes. The third platform uses automated process analytical technology to gather real-time data related to reaction kinetics. This system allows the researcher to directly interrogate the reaction mechanisms in granular detail to determine exactly how and why a reaction proceeds, thereby enabling reaction optimization and deployment.

Representing Catalytic and Processing Space in Methane Oxidation Reaction via Multioutput Machine Learning

Multioutput support vector regression (SVR) is implemented to simultaneously predict the selectivities and the CH₄ conversion against experimental conditions in methane oxidation catalysts. The predictions unveil the details of how each selectivity and CH₄ conversion behaves in each catalyst. In particular, the selectivity and the CH₄ conversion of Mn-Na₂WO₄/SiO₂, Ti-Na₂WO₄/SiO₂, Pd-Na₂WO₄/SiO₂, and Na₂WO ₄/SiO₂ are predicted, and the effects of Mn, Ti, and Pd are unveiled. In addition, the trade-off points of CO and C₂H₆ are identified for each catalyst, leading to maximization of the C₂H₆ yield. Thus the simultaneous prediction of the reaction trend with catalysts not only will help with the understanding of the catalyst activities but also will provide guidance for designing the experimental conditions.

Digitizing Chemistry Using the Chemical Processing Unit: From Synthesis to Discovery

The digitization of chemistry is not simply about using machine learning or artificial intelligence systems to process chemical data, or about the development of ever more capable automation hardware; instead, it is the creation of a hard link between an abstracted process ontology of chemistry and bespoke hardware for performing reactions or exploring reactivity. Chemical digitization is therefore about the unambiguous development of an architecture, a chemical state machine, that uses this ontology to connect precise instruction sets to hardware that performs chemical transformations. This approach enables a universal standard for describing chemistry procedures via a chemical programming language and facilitates unambiguous dissemination of these procedures. We predict that this standard will revolutionize the ability of chemists to collaborate, increase reproducibility and safety, as we all as optimize for cost and efficiency. Most importantly, the digitization of chemistry will dramatically reduce the labor needed to make new compounds and broaden accessible chemical space. In recent years, the developments of automation in chemistry have gone beyond flow chemistry alone, with many bespoke workflows being developed not only for automating chemical synthesis but also for materials, nanomaterials, and formulation production. Indeed, the leap from fixed-configuration synthesis machines like peptide, nucleic acid, or dedicated cross-coupling engines is important for developing a truly universal approach to "dial-a-molecule". In this case, a key conceptual leap is the use of a batch system that can encode the chemical reagents, solvent, and products as packets which can be moved around the system, and a graph-based approach for the description of hardware modules that allows the compilation of chemical code that runs on, in principle, any hardware. Further, the integration of sensor systems for monitoring and controlling the state of the chemical synthesis machine, as well as high resolution spectroscopic tools, is vital if these systems are to facilitate closed-loop autonomous experiments. Systems that not only make molecules and materials, but also optimize their function, and use algorithms to assist with the development of new synthetic pathways and process optimization are also possible. Here, we discuss how the digitization of chemistry is happening, building on the plethora of technological developments in hardware and software. Importantly, digital-chemical robot systems need to integrate feedback from simple sensors, e.g., conductivity or temperature, as well as online analytics in order to navigate process space autonomously. This will open the door to accessing known molecules (synthesis), exploring whether known compounds/reactions are possible under new conditions (optimization), and searching chemical space for unknown and unexpected new molecules, reactions, and modes of reactivity (discovery). We will also discuss the role of chemical knowledge and how this can be used to challenge bias, as well as define and expand synthetically accessible chemical space using programmable robotic chemical state machines.

A Domino Fusion of an Organic Ligand Depended on Metal-Induced and Oxygen Insertion, Unraveled by Crystallography, Mass Spectrometry, and DFT Calculations

Herein, the reaction of (1-methyl-1 H-benzo[d]imidazol-2-yl)methanamine (L1) with Co(H₂ O)₆ Cl₂ , in CH₃ CN at 120 °C, leading to the 2,3,5,6-tetrakis(1-methyl-1 H-benzo[d]imidazol-2-yl)pyrazine (3), isolated as a dimeric cluster {[Co^II ₂ (3)Cl₄ ]⋅2 CH₃ CN} (2), is reported. When O₂ and H₂ O are present, (1-methyl-1 H-benzo[d]imidazole-2-carbonyl)amide (HL1') is first formed and crystallized as [Co^III (L1)₂ (L1')]Cl₂ ⋅2 H₂ O (4) before fusion of HL1' with L1, giving 1-methyl-N-(1-methyl-1 H-benzo[d]imidazol-2-carbonyl)-1 H-benzo[d]imidazol-2-carboxamide (HL2'') forming a one-dimensional (1D) chain of [Co^II ₃ (L2'')₂ Cl₄ ]_n (5). The combination of crystallography and mass spectrometry (ESI-MS) of isolated crystals and the solutions taken from the reaction as a function time reveal seven intermediate steps leading to 2, but six steps for 5, for which a different sequence takes place. Control and isotope labeling experiments confirm the two carbonyl oxygen atoms in 5 originate from both air and water. The dependence on the metals, compared with FeCl₃ ⋅6 H₂ O leading to a stable triheteroarylmethyl radical, is quite astounding, which could be attributed to the different oxidation states of the metals and coordination modes confirmed by DFT calculations. This metal and valence dependent process is a very useful way for selectively obtaining these large molecules, which are unachievable by common organic synthesis.

Chemical Speciation, Plant Uptake, and Toxicity of Heavy Metals in Agricultural Soils

Heavy metals in agricultural soils exist in diverse dissolved (free cations and complexed species of positive, neutral, or negative charges), particulate (sorbed, structural, and coprecipitated), and colloidal (micro- and nanometer-sized particles) species. The fate of different heavy metal species is controlled by the master variables: pH (solubility), ionic strength (activity and charge-shielding), and dissolved organic carbon (complexation). In the rhizosphere, chemical speciation controls toxicokinetics (uptake and transport of metals by plants) while toxicodynamics (interaction between the plant and absorbed species) drives the toxicity outcome. Based on the critical review, the authors recommend omics and data mining techniques to link discrete knowledge bases from the speciation dynamics, soil microbiome, and plant transporter/gene expression relevant to homeostasis conditions of modern agriculture. Such efforts could offer a disruptive application tool to improve and sustain plant tolerance, food safety, and environmental quality.

The Role of Machine Learning in the Understanding and Design of Materials

Developing algorithmic approaches for the rational design and discovery of materials can enable us to systematically find novel materials, which can have huge technological and social impact. However, such rational design requires a holistic perspective over the full multistage design process, which involves exploring immense materials spaces, their properties, and process design and engineering as well as a techno-economic assessment. The complexity of exploring all of these options using conventional scientific approaches seems intractable. Instead, novel tools from the field of machine learning can potentially solve some of our challenges on the way to rational materials design. Here we review some of the chief advancements of these methods and their applications in rational materials design, followed by a discussion on some of the main challenges and opportunities we currently face together with our perspective on the future of rational materials design and discovery.

Digitising chemical synthesis in automated and robotic flow

Continuous flow chemical synthesis is already known to have many attributes that give it superiority over batch processes in several respects. To expand these advantages with those from automation will only drive such enabling technologies further into the faster producing, more efficient 21st century chemical world. In this report we present several examples of algorithmic chemical search, along with flow platforms that link hardware and digital chemical operations on software. This enables organic syntheses to be automatically carried out and optimised with as little human intervention as possible. By applying such enabling technologies to the production of small organic molecules and pharmaceutical compounds in end-to-end multistep processes, a range of reaction types can be accessed and, thus, the flexibility of these single, compact flow designs may be revealed. Automated systems can allow several reactions to take place on the same setup, enabling direct comparison of reactions under different conditions. Moreover, the production of new and known target compounds can be made faster and more efficient, the recipes of which can then be stored as digital files. Some of the automating software has employed machine-powered learning to assist the chemist in developing intelligent algorithms and artificial intelligence (AI) driven synthetic route planning. This ultimately produces a continuous flow platform that can design its own viable pathway to a particular molecule and then carry it out on its own, allowing the chemists, at the same time, to apply their expertise to other pressing challenges in their fields.

Concentric liquid reactors for chemical synthesis and separation

Recent years have witnessed increased interest in systems that are capable of supporting multistep chemical processes without the need for manual handling of intermediates. These systems have been based either on collections of batch reactors¹ or on flow-chemistry designs^2-4, both of which require considerable engineering effort to set up and control. Here we develop an out-of-equilibrium system in which different reaction zones self-organize into a geometry that can dictate the progress of an entire process sequence. Multiple (routinely around 10, and in some cases more than 20) immiscible or pairwise-immiscible liquids of different densities are placed into a rotating container, in which they experience a centrifugal force that dominates over surface tension. As a result, the liquids organize into concentric layers, with thicknesses as low as 150 micrometres and theoretically reaching tens of micrometres. The layers are robust, yet can be internally mixed by accelerating or decelerating the rotation, and each layer can be individually addressed, enabling the addition, sampling or even withdrawal of entire layers during rotation. These features are combined in proof-of-concept experiments that demonstrate, for example, multistep syntheses of small molecules of medicinal interest, simultaneous acid-base extractions, and selective separations from complex mixtures mediated by chemical shuttles. We propose that 'wall-less' concentric liquid reactors could become a useful addition to the toolbox of process chemistry at small to medium scales and, in a broader context, illustrate the advantages of transplanting material and/or chemical systems from traditional, static settings into a rotating frame of reference.

Photothermally Modulatable and Structurally Disintegratable Sub-8-nm Au1Ag9 Embedded Nanoblocks for Combination Cancer Therapy Produced by Plug-in Assembly

As well as the exploration of translatable delivery nanosystems for cancer therapeutic agents, the development of automatable continuous-flow manufacturing technology comprising digitally controlled reactions for the on-demand production of pharmaceuticals is an important challenge in anticancer nanomedicine. Most attempts to resolve these issues have involved the development of alternative reactions, formulations, or constructs containing stimulus components aimed at producing multiple approaches for highly efficacious combination cancer therapies. However, there has been no report of a platform based on plug-in execution that enables continuous-flow manufacture in a compact, reconfigurable manner, although an optimal platform technology may be a prerequisite for the timely translation of recently developed nanomedicines. To this end, we describe the development of a platform toward digitizable, continuous manufacture by a serial combination of plug-in reactionwares (heating plates, a spraying cup, and a photochamber) for single-pass flow fabrication. Specifically, we fabricated three different composite nanoblocks consisting of Au₁Ag₉ (<8 nm; stimulus component), docetaxel (an anticancer drug), and bovine serum albumin (a protective and targeting agent) using our system, with the result of producing nanoblocks with photothermally modulatable and structurally disintegratable properties. These were examined for effectiveness in near-infrared-induced chemothermal cancer therapy and renal excretion of Au₁Ag₉ particles and exhibited high anticancer efficacy and warrantable biosafety.

Metal 3D printing technology for functional integration of catalytic system

Mechanical properties and geometries of printed products have been extensively studied in metal 3D printing. However, chemical properties and catalytic functions, introduced by metal 3D printing itself, are rarely mentioned. Here we show that metal 3D printing products themselves can simultaneously serve as chemical reactors and catalysts (denoted as self-catalytic reactor or SCR) for direct conversion of C1 molecules (including CO, CO₂ and CH₄) into high value-added chemicals. The Fe-SCR and Co-SCR successfully catalyze synthesis of liquid fuel from Fischer-Tropsch synthesis and CO₂ hydrogenation; the Ni-SCR efficiently produces syngas (CO/H₂) by CO₂ reforming of CH₄. Further, the Co-SCR geometrical studies indicate that metal 3D printing itself can establish multiple control functions to tune the catalytic product distribution. The present work provides a simple and low-cost manufacturing method to realize functional integration of catalyst and reactor, and will facilitate the developments of chemical synthesis and 3D printing technology.

Metal 3D printing is a very promising technology to revolutionize catalytic systems. Here the authors show that metal 3D printing products themselves can simultaneously serve as chemical reactors and catalysts for conversion of C1 molecules into high value-added chemicals.

Autonomous intelligent agents for accelerated materials discovery

We present an end-to-end computational system for autonomous materials discovery. The system aims for cost-effective optimization in large, high-dimensional search spaces of materials by adopting a sequential, agent-based approach to deciding which experiments to carry out. In choosing next experiments, agents can make use of past knowledge, surrogate models, logic, thermodynamic or other physical constructs, heuristic rules, and different exploration-exploitation strategies. We show a series of examples for (i) how the discovery campaigns for finding materials satisfying a relative stability objective can be simulated to design new agents, and (ii) how those agents can be deployed in real discovery campaigns to control experiments run externally, such as the cloud-based density functional theory simulations in this work. In a sample set of 16 campaigns covering a range of binary and ternary chemistries including metal oxides, phosphides, sulfides and alloys, this autonomous platform found 383 new stable or nearly stable materials with no intervention by the researchers.

A 3d-printed composite electrode for sustained electrocatalytic oxygen evolution

We report the facile design and fabrication of 3D-printed microstructured electrodes for electrocatalytic oxygen evolution. ABS polymer-based mesh scaffolds are chemically functionalized to enable electroless nickel metal deposition and subsequent catalyst (nickel iron hydroxide) immobilization. The resulting composites show sustained oxygen evolution with low overpotentials and high stability. The modular approach reported enables the scalable on-demand fabrication of microstructured composite electrodes.

Emerging technologies for diagnostics and drug delivery in the fight against COVID-19 and other pandemics

INTRODUCTION

A pandemic is the worst-case scenario in the field of infectious diseases. Innovative technologies have the potential to address the challenges associated with the manufacture of personalized drug delivery systems, biosensors, and medical devices during a pandemic. 3D-Printing, microfluidics, and Microelectromechanical systems (MEMS) can provide an important part on this fight, as are cheap, easy to be operated, capable to provide rapid detection and monitoring of a disease, and deliver medicines.

AREAS COVERED

This manuscript answers the question of how these emerging technologies can save lives during a pandemic by avoiding supply chain delays and also by providing rapid diagnostics, disease monitoring, or by offering personalized treatments. The manuscript covers recent approaches in the topic with a focus in manuscripts published in the last year and by emphasising recent regulatory considerations by regulatory agencies in the manufacturing of 3DP systems or other medical devices during COVID.

EXPERT OPINION

New manufacturing techniques are emerging with the ability to address the challenges associated with the development of medical devices or diagnostics, during a pandemic. Are many challenges in order to achieve this and especially in short times that are required under a pandemic attack, which will also be covered in this manuscript.

Heterogeneous photocatalysis in flow chemical reactors

The synergy between photocatalysis and continuous flow chemical reactors has shifted the paradigms of photochemistry, opening new avenues of research with safer and scalable processes that can be readily implemented in academia and industry. Current state-of-the-art photocatalysts are homogeneous transition metal complexes that have favourable photophysical properties, wide electrochemical redox potentials, and photostability. However, these photocatalysts present serious drawbacks, such as toxicity, limited availability, and the overall cost of rare transition metal elements. This reduces their long-term viability, especially at an industrial scale. Heterogeneous photocatalysts (HPCats) are an attractive alternative, as the requirement for the separation and purification is largely removed, but typically at the cost of efficiency. Flow chemical reactors can, to a large extent, mitigate the loss in efficiency through reactor designs that enhance mass transport and irradiation. Herein, we review some important developments of heterogeneous photocatalytic materials and their application in flow reactors for sustainable organic synthesis. Further, the application of continuous flow heterogeneous photocatalysis in environmental remediation is briefly discussed to present some interesting reactor designs that could be implemented to enhance organic synthesis.

An aurora of natural products-based drug discovery is coming

Natural products (NPs), a nature's reservoir possessing enormous structural and functional diversity far beyond the current ability of chemical synthesis, are now proving themselves as most wonderful gifts from mother nature for human beings. Many of them have been used successfully as medicines, as well as the most important sources of drug leads, food additives, and many industry relevant products for millennia. Most notably, more than half of the antibiotics and anti-cancer drugs currently in use are, or derived from, natural products. However, the speed and outputs of NP-based drug discovery has been slowing down dramatically after the fruitful harvest of the "low-hanging fruit" during the golden age of 1950s-1960s. With recent scientific advances combining metabolic sciences and technology, multi-omics, big data, combinatorial biosynthesis, synthetic biology, genome editing technology (such as CRISPR), artificial intelligence (AI), and 3D printing, the "high-hanging fruit" is becoming more and more accessible with reduced costs. We are now more and more confident that a new age of natural products discovery is dawning.

Self-driving laboratory for accelerated discovery of thin-film materials

Discovering and optimizing commercially viable materials for clean energy applications typically takes more than a decade. Self-driving laboratories that iteratively design, execute, and learn from materials science experiments in a fully autonomous loop present an opportunity to accelerate this research process. We report here a modular robotic platform driven by a model-based optimization algorithm capable of autonomously optimizing the optical and electronic properties of thin-film materials by modifying the film composition and processing conditions. We demonstrate the power of this platform by using it to maximize the hole mobility of organic hole transport materials commonly used in perovskite solar cells and consumer electronics. This demonstration highlights the possibilities of using autonomous laboratories to discover organic and inorganic materials relevant to materials sciences and clean energy technologies.

ChemOS: An orchestration software to democratize autonomous discovery

The current Edisonian approach to discovery requires up to two decades of fundamental and applied research for materials technologies to reach the market. Such a slow and capital-intensive turnaround calls for disruptive strategies to expedite innovation. Self-driving laboratories have the potential to provide the means to revolutionize experimentation by empowering automation with artificial intelligence to enable autonomous discovery. However, the lack of adequate software solutions significantly impedes the development of self-driving laboratories. In this paper, we make progress towards addressing this challenge, and we propose and develop an implementation of ChemOS; a portable, modular and versatile software package which supplies the structured layers necessary for the deployment and operation of self-driving laboratories. ChemOS facilitates the integration of automated equipment, and it enables remote control of automated laboratories. ChemOS can operate at various degrees of autonomy; from fully unsupervised experimentation to actively including inputs and feedbacks from researchers into the experimentation loop. The flexibility of ChemOS provides a broad range of functionality as demonstrated on five applications, which were executed on different automated equipment, highlighting various aspects of the software package.