A Versatile Flow Reactor Platform for Machine Learning Guided RAFT Synthesis, Amidation of Poly(Pentafluorophenyl Acrylate)

Data-driven polymer research has experienced a dramatic upswing in recent years owing to the emergence of artificial intelligence (AI) alongside automated laboratory synthesis. However, the chemical complexity of polymers employed in automated synthesis still lacks in terms of defined functionality to meet the need of next-generation high-performance polymer materials. In this work, the automated self-optimization of the reversible addition-fragmentation chain-transfer (RAFT) polymerization of pentafluorophenyl acrylate (PFPA) is presented, a versatile polymer building-block enabling efficient post-polymerization modifications (PPM). The polymerization system consisted of a computer-operated flow reactor with orthogonal analytics comprising an inline benchtop nuclear magnetic resonance (NMR) spectrometer, and an online size exclusion chromatography (SEC). This setup enabled the automatic determination of optimal polymerization conditions by implementation of a multi-objective Bayesian self-optimization algorithm. The obtained poly(PFPA) is precisely modified by amidation taking advantage of the active pentafluorophenyl (PFP) ester. By controlling the feed ratios of solutions containing different amines, their incorporation ratio into the polymer, and therefore its resulting properties, can be tuned and predicted, which is shown using NMR, differential scanning calorimetry (DSC), and infrared (IR) analysis. The described strategy represents a versatile method to synthesize and modify reactive polymers in continuous flow, expanding the range of functional polymer materials accessible by continuous, high-throughput synthesis.

Exploiting Online Spatially Resolved Dynamic Light Scattering and Flow-NMR for Automated Size Targeting of PISA-Synthesized Block Copolymer Nanoparticles

Programmable synthesis of polymer nanoparticles prepared by polymerization-induced self-assembly (PISA) mediated by reversible addition-fragmentation chain-transfer (RAFT) dispersion polymerization with specified diameter is achieved in an automated flow-reactor platform. Real-time particle size and monomer conversion is obtained via inline spatially resolved dynamic light scattering (SRDLS) and benchtop nuclear magnetic resonance (NMR) instrumentation. An initial training experiment generated a relationship between copolymer block length and particle size for the synthesis of poly(N,N-dimethylacrylamide)-b-poly(diacetone acrylamide) (PDMAm-b-PDAAm) nanoparticles. The training data was used to target the product compositions required for synthesis of nanoparticles with defined diameters of 50, 60, 70, and 80 nm, while inline NMR spectroscopy enabled rapid acquisition of kinetic data to support their scale-up. NMR and SRDLS were used during the continuous manufacture of the targeted products to monitor product consistency while an automated sampling system collected practically useful quantities of the targeted products, thus outlining the potential of the platform as a tool for discovery, development, and manufacture of polymeric nanoparticles.

Real-time autonomous control of a continuous macroscopic process as demonstrated by plastic forming

To meet the need for more adaptable and expedient approaches in research and manufacturing, we present a continuous autonomous system that leverages real-time, in situ characterization and an active-learning-based decision-making processor. This system was applied to a plastic film forming process to demonstrate its capability in autonomously determining process conditions for specified film dimensions without human intervention. Application of the system to nine film dimensions (width and thickness) highlighted its ability to explore the search space and identify appropriate and stable process conditions, with an average of 11 characterization-adjustment iterations and a processing time of 19 minutes per width, thickness combination. The system successfully avoided common pitfalls, such as repetitive over-correction, and demonstrated high accuracy, with R2 values of 0.87 and 0.90 for film width and thickness, respectively. Moreover, the active learning algorithm enabled the system to begin exploration with zero training data, effectively addressing the complex and interdependent relationships between control factors (material supply rate, applied force, material viscosity) in the continuous plastic forming process. Given that the core concept of this autonomous process can, in principle, be transferred to other continuous material processing systems, these results have implications for accelerating progress in both research and industry.

Automated Optimization of a Multistep, Multiphase Continuous Flow Process for Pharmaceutical Synthesis

Flow synthesis is becoming increasingly relevant as a sustainable and safe alternative to traditional batch processes, as reaction conditions that are not usually achievable in batch chemistry can be exploited (for example, higher temperatures and pressures). Telescoped continuous reactions have the potential to reduce waste by decreasing the number of separate unit operations (e.g., crystallization, filtration, washing, and drying), increase safety due to limiting operator interaction with potentially harmful materials that can be reacted in subsequent steps, minimize supply chain disruption, and reduce the need to store large inventories of intermediates as they can be synthesized on demand. Optimization of these flow processes leads to further efficiency when exploring new reactions, as with a higher yield comes higher purity, reduced waste, and a greener synthesis. This project explored a two-step process consisting of a three-phase heterogeneously catalyzed hydrogenation followed by a homogeneous amidation reaction. The steps were optimized individually and as a multistep telescoped process for yield using remote automated control via a Bayesian optimization algorithm and HPLC analysis to assess the performance of a reaction for a given set of experimental conditions. 2-MeTHF was selected as a green solvent throughout the process, and the heterogeneous step provided good atom economy due to the use of pure hydrogen gas as a reagent. This research highlights the benefits of using multistage automated optimization in the development of pharmaceutical syntheses. The combination of telescoping and optimization with automation allows for swift investigation of synthetic processes in a minimum number of experiments, leading to a reduction in the number of experiments performed and a large reduction in process mass intensity values.

Self-Driving Laboratories for Chemistry and Materials Science

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

Real-Time Determination of Molecular Weight: Use of MaDDOSY (Mass Determination Diffusion Ordered Spectroscopy) to Monitor the Progress of Polymerization Reactions

Knowledge of molecular weight is an integral factor in polymer synthesis, and while many synthetic strategies have been developed to help control this, determination of the final molecular weight is often only measured at the end of the reaction. Herein, we provide a technique for the online determination of polymer molecular weight using a universal, solvent-independent diffusion ordered spectroscopy (DOSY) calibration and evidence its use in a variety of polymerization reactions.

Streamlining the Generation of Advanced Polymer Materials Through the Marriage of Automation and Multiblock Copolymer Synthesis in Emulsion

Synthetic polymers are of paramount importance in modern life - an incredibly wide range of polymeric materials possessing an impressive variety of properties have been developed to date. The recent emergence of artificial intelligence and automation presents a great opportunity to significantly speed up discovery and development of the next generation of advanced polymeric materials. We have focused on the high-throughput automated synthesis of multiblock copolymers that comprise three or more distinct polymer segments of different monomer composition bonded in linear sequence. The present work has exploited automation to prepare high molar mass multiblock copolymers (typically>100,000 g mol-1) using reversible addition-fragmentation chain transfer (RAFT) polymerization in aqueous emulsion. A variety of original multiblock copolymers have been synthesised via a Chemspeed robot, exemplified by a multiblock copolymer comprising thirteen constituent blocks. Moreover, libraries of copolymers of randomized monomer compositions (acrylates, acrylamides, methacrylates, and styrenes), block orders, and block lengths were also generated, thereby demonstrating the robustness of our synthetic approach. One multiblock copolymer contained all four monomer families listed in the pool, which is unprecedented in the literature. The present work demonstrates that automation has the power to render complex and laborious syntheses of such unprecedented materials not just possible, but facile and straightforward, thus representing the way forward to the next generation of complex macromolecular architectures.

Data-Driven Design of Polymer-Based Biomaterials: High-throughput Simulation, Experimentation, and Machine Learning

Polymers, with the capacity to tunably alter properties and response based on manipulation of their chemical characteristics, are attractive components in biomaterials. Nevertheless, their potential as functional materials is also inhibited by their complexity, which complicates rational or brute-force design and realization. In recent years, machine learning has emerged as a useful tool for facilitating materials design via efficient modeling of structure-property relationships in the chemical domain of interest. In this Spotlight, we discuss the emergence of data-driven design of polymers that can be deployed in biomaterials with particular emphasis on complex copolymer systems. We outline recent developments, as well as our own contributions and takeaways, related to high-throughput data generation for polymer systems, methods for surrogate modeling by machine learning, and paradigms for property optimization and design. Throughout this discussion, we highlight key aspects of successful strategies and other considerations that will be relevant to the future design of polymer-based biomaterials with target properties.

Performance metrics to unleash the power of self-driving labs in chemistry and materials science

With the rise of self-driving labs (SDLs) and automated experimentation across chemical and materials sciences, there is a considerable challenge in designing the best autonomous lab for a given problem based on published studies alone. Determining what digital and physical features are germane to a specific study is a critical aspect of SDL design that needs to be approached quantitatively. Even when controlling for features such as dimensionality, every experimental space has unique requirements and challenges that influence the design of the optimal physical platform and algorithm. Metrics such as optimization rate are therefore not necessarily indicative of the capabilities of an SDL across different studies. In this perspective, we highlight some of the critical metrics for quantifying performance in SDLs to better guide researchers in implementing the most suitable strategies. We then provide a brief review of the existing literature under the lens of quantified performance as well as heuristic recommendations for platform and experimental space pairings.

Frontiers in nonviral delivery of small molecule and genetic drugs, driven by polymer chemistry and machine learning for materials informatics

Materials informatics (MI) has immense potential to accelerate the pace of innovation and new product development in biotechnology. Close collaborations between skilled physical and life scientists with data scientists are being established in pursuit of leveraging MI tools in automation and artificial intelligence (AI) to predict material properties in vitro and in vivo. However, the scarcity of large, standardized, and labeled materials data for connecting structure-function relationships represents one of the largest hurdles to overcome. In this Highlight, focus is brought to emerging developments in polymer-based therapeutic delivery platforms, where teams generate large experimental datasets around specific therapeutics and successfully establish a design-to-deployment cycle of specialized nanocarriers. Three select collaborations demonstrate how custom-built polymers protect and deliver small molecules, nucleic acids, and proteins, representing ideal use-cases for machine learning to understand how molecular-level interactions impact drug stabilization and release. We conclude with our perspectives on how MI innovations in automation efficiencies and digitalization of data-coupled with fundamental insight and creativity from the polymer science community-can accelerate translation of more gene therapies into lifesaving medicines.

Beauty of Explicit Dispersity (Đ) Equations in Controlled Polymerizations

Dispersity (Đ) as a critical parameter indicates the level of uniformity of the polymer molar mass or chain length. In the past several decades, the development of explicit equations for calculating Đ experiences a continual revolution. This viewpoint tracks the historical evolution of the explicit equations from living to reversible-deactivation polymerization systems. Emphasis is laid on displaying the charm of explicit Đ equations in batch reversible-deactivation radical polymerization (RDRP), with highlights of the relevant elegant mathematical manipulations. Some representative emerging applications enabled by the existing explicit equations are shown, involving nitroxide-mediated polymerization (NMP), atom transfer radical polymerization (ATRP), and reversible addition-fragmentation chain transfer (RAFT) polymerization systems. Stemming from the several outlined challenges and outlooks, sustained concerns about the explicit Đ equations are still highly deserved. It is expected that these equations will continue to play an important role not only in traditional polymerization kinetic simulation and design of experiments but also in modern intelligent manufacturing of precision polymers and classroom education.

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.

Emerging Trends in Machine Learning: A Polymer Perspective

In the last five years, there has been tremendous growth in machine learning and artificial intelligence as applied to polymer science. Here, we highlight the unique challenges presented by polymers and how the field is addressing them. We focus on emerging trends with an emphasis on topics that have received less attention in the review literature. Finally, we provide an outlook for the field, outline important growth areas in machine learning and artificial intelligence for polymer science and discuss important advances from the greater material science community.

Substrate specific closed-loop optimization of carbohydrate protective group chemistry using Bayesian optimization and transfer learning

A new way of performing reaction optimization within carbohydrate chemistry is presented. This is done by performing closed-loop optimization of regioselective benzoylation of unprotected glycosides using Bayesian optimization. Both 6-O-monobenzoylations and 3,6-O-dibenzoylations of three different monosaccharides are optimized. A novel transfer learning approach, where data from previous optimizations of different substrates is used to speed up the optimizations, has also been developed. The optimal conditions found by the Bayesian optimization algorithm provide new insight into substrate specificity, as the conditions found are significantly different. In most cases, the optimal conditions include Et₃N and benzoic anhydride, a new reagent combination for these reactions, discovered by the algorithm, demonstrating the power of this concept to widen the chemical space. Further, the developed procedures include ambient conditions and short reaction times.

Advances in continuous polymer analysis in flow with application towards biopolymers

Biopolymers, polymers derived from renewable biomass sources, have gained increasing attention in recent years due to their potential to replace traditional petroleum-based polymers in a range of applications. Among the many advantages of biopolymers can be included their biocompatibility, excellent mechanical properties, and availability from renewable feedstock. However, the development of biopolymers has been limited by a lack of understanding of their properties and processing behaviours. Continuous analysis techniques have the potential to hasten progress in this area by providing real-time insights into the properties and processing of biopolymers. Significant research in polymer chemistry has focused on petroleum-derived polymers and has thus provided a wealth of synthetic and analytical methodologies which may be applied to the biopolymer field. Of particular note is the application of flow technology in polymer science and its implications for accelerating progress towards more sustainable and environmentally friendly alternatives to traditional petroleum-based polymers. In this mini review we have outlined several of the most prominent use cases for biopolymers along with the current state-of-the art in continuous analysis of polymers in flow, including defining and differentiating atline, inline, online and offline analysis. We have found several examples for continuous flow analysis which have direct application to the biopolymer field, and we demonstrate an atline continuous polymer analysis method using size exclusion chromatography.

Graphical abstract

Quid Pro Flow

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

Bayesian Self-Optimization for Telescoped Continuous Flow Synthesis

The optimization of multistep chemical syntheses is critical for the rapid development of new pharmaceuticals. However, concatenating individually optimized reactions can lead to inefficient multistep syntheses, owing to chemical interdependencies between the steps. Herein, we develop an automated continuous flow platform for the simultaneous optimization of telescoped reactions. Our approach is applied to a Heck cyclization-deprotection reaction sequence, used in the synthesis of a precursor for 1-methyltetrahydroisoquinoline C5 functionalization. A simple method for multipoint sampling with a single online HPLC instrument was designed, enabling accurate quantification of each reaction, and an in-depth understanding of the reaction pathways. Notably, integration of Bayesian optimization techniques identified an 81 % overall yield in just 14 h, and revealed a favorable competing pathway for formation of the desired product.

A fully automated platform for photoinitiated RAFT polymerization

Oxygen tolerant polymerizations including Photoinduced Electron/Energy Transfer-Reversible Addition-Fragmentation Chain-Transfer (PET-RAFT) polymerization allow for high-throughput synthesis of diverse polymer architectures on the benchtop in parallel. Recent developments have further increased throughput using liquid handling robotics to automate reagent handling and dispensing into well plates thus enabling the combinatorial synthesis of large polymer libraries. Although liquid handling robotics can enable automated polymer reagent dispensing in well plates, photoinitiation and reaction monitoring require automation to provide a platform that enables the reliable and robust synthesis of various polymer compositions in high-throughput where polymers with desired molecular weights and low dispersity are obtained. Here, we describe the development of a robotic platform to fully automate PETRAFT polymerizations and provide individual control of reactions performed in well plates. On our platform, reagents are automatically dispensed in well plates, photoinitiated in individual wells with a custom-designed lightbox until the polymerizations are complete, and monitored online in real-time by tracking fluorescence intensities on a fluorescence plate reader, with well plate transfers between instruments occurring via a robotic arm. We found that this platform enabled robust parallel polymer synthesis of both acrylate and acrylamide homopolymers and copolymers, with high monomer conversions and low dispersity. The successful polymerizations obtained on this platform make it an efficient tool for combinatorial polymer chemistry. In addition, with the inclusion of machine learning protocols to help navigate the polymer space towards specific properties of interest, this robotic platform can ultimately become a self-driving lab that can dispense, synthesize, and monitor large polymer libraries.

A versatile non-fouling multi-step flow reactor platform: demonstration for partial oxidation synthesis of iron oxide nanoparticles

In the last decade flow reactors for material synthesis were firmly established, demonstrating advantageous operating conditions, reproducible and scalable production via continuous operation, as well as high-throughput screening of synthetic conditions. Reactor fouling, however, often restricts flow chemistry and the common fouling prevention via segmented flow comes at the cost of inflexibility. Often, the difficulty of feeding reagents into liquid segments (droplets or slugs) constrains flow syntheses using segmented flow to simple synthetic protocols with a single reagent addition step prior or during segmentation. Hence, the translation of fouling prone syntheses requiring multiple reagent addition steps into flow remains challenging. This work presents a modular flow reactor platform overcoming this bottleneck by fully exploiting the potential of three-phase (gas-liquid-liquid) segmented flow to supply reagents after segmentation, hence facilitating fouling free multi-step flow syntheses. The reactor design and materials selection address the operation challenges inherent to gas-liquid-liquid flow and reagent addition into segments allowing for a wide range of flow rates, flow ratios, temperatures, and use of continuous phases (no perfluorinated solvents needed). This "Lego®-like" reactor platform comprises elements for three-phase segmentation and sequential reagent addition into fluid segments, as well as temperature-controlled residence time modules that offer the flexibility required to translate even complex nanomaterial synthesis protocols to flow. To demonstrate the platform's versatility, we chose a fouling prone multi-step synthesis, i.e., a water-based partial oxidation synthesis of iron oxide nanoparticles. This synthesis required I) the precipitation of ferrous hydroxides, II) the addition of an oxidation agent, III) a temperature treatment to initiate magnetite/maghemite formation, and IV) the addition of citric acid to increase the colloidal stability. The platform facilitated the synthesis of colloidally stable magnetic nanoparticles reproducibly at well-controlled synthetic conditions and prevented fouling using heptane as continuous phase. The biocompatible particles showed excellent heating abilities in alternating magnetic fields (ILP values >3 nH m² kg_Fe^-1), hence, their potential for magnetic hyperthermia cancer treatment. The platform allowed for long term operation, as well as screening of synthetic conditions to tune particle properties. This was demonstrated via the addition of tetraethylenepentamine, confirming its potential to control particle morphology. Such a versatile reactor platform makes it possible to translate even complex syntheses into flow, opening up new opportunities for material synthesis.

Materiomically Designed Polymeric Vehicles for Nucleic Acids: Quo Vadis?

Despite rapid advances in molecular biology, particularly in site-specific genome editing technologies, such as CRISPR/Cas9 and base editing, financial and logistical challenges hinder a broad population from accessing and benefiting from gene therapy. To improve the affordability and scalability of gene therapy, we need to deploy chemically defined, economical, and scalable materials, such as synthetic polymers. For polymers to deliver nucleic acids efficaciously to targeted cells, they must optimally combine design attributes, such as architecture, length, composition, spatial distribution of monomers, basicity, hydrophilic-hydrophobic phase balance, or protonation degree. Designing polymeric vectors for specific nucleic acid payloads is a multivariate optimization problem wherein even minuscule deviations from the optimum are poorly tolerated. To explore the multivariate polymer design space rapidly, efficiently, and fruitfully, we must integrate parallelized polymer synthesis, high-throughput biological screening, and statistical modeling. Although materiomics approaches promise to streamline polymeric vector development, several methodological ambiguities must be resolved. For instance, establishing a flexible polymer ontology that accommodates recent synthetic advances, enforcing uniform polymer characterization and data reporting standards, and implementing multiplexed in vitro and in vivo screening studies require considerable planning, coordination, and effort. This contribution will acquaint readers with the challenges associated with materiomics approaches to polymeric gene delivery and offers guidelines for overcoming these challenges. Here, we summarize recent developments in combinatorial polymer synthesis, high-throughput screening of polymeric vectors, omics-based approaches to polymer design, barcoding schemes for pooled in vitro and in vivo screening, and identify materiomics-inspired research directions that will realize the long-unfulfilled clinical potential of polymeric carriers in gene therapy.