Living in the Fast Lane—High Throughput Controlled/Living Radical Polymerization

Functional monomer design for synthetically accessible polymers

Machine learning (ML) has emerged as a powerful tool to navigate polymer structure-property relationships. Despite recent progress, data sparsity is a major obstacle hindering the practical application of ML in polymer science. In this work, we explore functional monomer design by developing the first comprehensive database of monomer-level chemical and physical properties for approximately 12M synthetically accessible polymers. We generated diverse monomer-level properties by integrating quantum chemistry calculations with active learning to efficiently probe a vast chemical space of synthetically feasible polymers. Monomer-level property descriptors are benchmarked against both higher level computational predictions and experimental data to the extent possible, demonstrating their relevance to polymer design. Our results show that many monomer-level properties are weakly correlated, implying a strong freedom for functional design such that multiple physical properties can be simultaneously optimized by monomer selection. Moreover, the synthetically accessible nature of this chemical space allows targeted monomers to be considered by common polymerization mechanisms to facilitate their synthetic realization. Overall, this work opens new avenues for creating synthetically accessible polymers and provides new insights for designing next generation polymeric materials.

High-Throughput Synthesis and Evaluation of Antiviral Copolymers for Enveloped Respiratory Viruses

COVID-19 made apparent the devastating impact viral pandemics have had on global health and order. Development of broad-spectrum antivirals to provide early protection upon the inevitable emergence of new viral pandemics is critical. In this work, antiviral polymers are discovered using a combination of high-throughput polymer synthesis and antiviral screening, enabling diverse polymer compositions to be explored. Amphipathic polymers, with ionizable tertiary amine groups, are the most potent antivirals, effective against influenza virus and SARS-CoV-2, with minimal cytotoxicity. It is hypothesized that these polymers interact with the viral membrane as they showed no activity against a nonenveloped virus (rhinovirus). The switchable chemistry of the polymers during endosomal acidification was evaluated using lipid monolayers, indicating that a complex synergy between hydrophobicity and ionization drives polymer-membrane interactions. This new high-throughput methodology can be adapted to continue to engineer the potency of the lead candidates or develop antiviral polymers against other emerging viral classes.

Machine learning prediction of the mechanical properties of injection-molded polypropylene through X-ray diffraction analysis

Predicting the mechanical properties of polymer materials using machine learning is essential for the design of next-generation of polymers. However, the strong relationship between the higher-order structure of polymers and their mechanical properties hinders the mechanical property predictions based on their primary structures. To incorporate information on higher-order structures into the prediction model, X-ray diffraction (XRD) can be used. This study proposes a strategy to generate appropriate descriptors from the XRD analysis of the injection-molded polypropylene samples, which were prepared under almost the same injection molding conditions. To this end, first, Bayesian spectral deconvolution is used to automatically create high-dimensional descriptors. Second, informative descriptors are selected to achieve highly accurate predictions by implementing the black-box optimization method using Ising machine. This approach was applied to custom-built polymer datasets containing data on homo- polypropylene and derived composite polymers with the addition of elastomers. Results show that reasonable accuracy of predictions for seven mechanical properties can be achieved using only XRD.

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.

High-Throughput Synthesis, Purification, and Application of Alkyne-Functionalized Discrete Oligomers

The development of synthetic oligomers as discrete single molecular entities with accurate control over the number and nature of functional groups along the backbone has enabled a variety of new research opportunities. From fundamental studies of self-assembly in materials science to understanding efficacy and safety profiles in biology and pharmaceuticals, future directions are significantly impacted by the availability of discrete, multifunctional oligomers. However, the preparation of diverse libraries of discrete and stereospecific oligomers remains a significant challenge. We report a novel strategy for accelerating the synthesis and isolation of discrete oligomers in a high-throughput manner based on click chemistry and simplified bead-based purification. The resulting synthetic platform allows libraries of discrete polyether oligomers to be prepared and the impact of variables such as chain length, number, and nature of side chain functionalities and molecular dispersity on antibacterial behavior examined. Significantly, discrete oligomers were shown to exhibit enhanced activity with lower toxicity compared with traditional disperse samples. This work provides a practical and scalable methodology for nonexperts to prepare libraries of multifunctional discrete oligomers and demonstrates the advantages of discrete materials in biological applications.

Degradable Block Copolymer Nanoparticles Synthesized by Polymerization-Induced Self-Assembly

Polymerization-induced self-assembly (PISA) combines polymerization and in situ self-assembly of block copolymers in one system and has become a widely used method to prepare block copolymer nanoparticles at high concentrations. The persistence of polymers in the environment poses a huge threat to the ecosystem and represents a significant waste of resources. There is an urgent need to develop novel chemical approaches to synthesize degradable polymers. To meet with this demand, it is crucial to install degradability into PISA nanoparticles. Most recently, degradable PISA nanoparticles have been synthesized by introducing degradation mechanisms into either shell-forming or core-forming blocks. This Minireview summarizes the development in degradable block copolymer nanoparticles synthesized by PISA, including shell-degradable, core-degradable, and all-degradable nanoparticles. Future development will benefit from expansion of polymerization techniques with new degradation mechanisms and adaptation of high-throughput approaches for both PISA syntheses and degradation studies.

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.

Navigating the Expansive Landscapes of Soft Materials: A User Guide for High-Throughput Workflows

Synthetic polymers are highly customizable with tailored structures and functionality, yet this versatility generates challenges in the design of advanced materials due to the size and complexity of the design space. Thus, exploration and optimization of polymer properties using combinatorial libraries has become increasingly common, which requires careful selection of synthetic strategies, characterization techniques, and rapid processing workflows to obtain fundamental principles from these large data sets. Herein, we provide guidelines for strategic design of macromolecule libraries and workflows to efficiently navigate these high-dimensional design spaces. We describe synthetic methods for multiple library sizes and structures as well as characterization methods to rapidly generate data sets, including tools that can be adapted from biological workflows. We further highlight relevant insights from statistics and machine learning to aid in data featurization, representation, and analysis. This Perspective acts as a "user guide" for researchers interested in leveraging high-throughput screening toward the design of multifunctional polymers and predictive modeling of structure-property relationships in soft materials.

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.

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.

Inverse Miniemulsion Enables the Continuous-Flow Synthesis of Controlled Ultra-High Molecular Weight Polymers

We report the controlled synthesis of ultra-high molecular weight (UHMW) polymers (Mn ≥ 106 g/mol) via continuous flow in a tubular reactor. At high monomer conversion, UHMW polymers in homogeneous batch polymerization exhibit high viscosities that pose challenges for employing continuous flow reactors. However, under heterogeneous inverse miniemulsion (IME) conditions, UHMW polymers can be produced within the dispersed phase, while the viscosity of the heterogeneous mixture remains approximately the same as the viscosity of the continuous phase. Conducting such IME polymerizations in flow results in a faster rate of polymerization compared to batch IME polymerizations while still providing excellent control over molecular weight up to 106 g/mol. Crucial emulsion parameters, such as particle size and stability under continuous flow conditions, were examined using dynamic light scattering. A range of poly(N,N-dimethylacrylamide) and poly(4-acryloylmorpholine) polymers with molecular weights of 104-106 g/mol (Đ ≤ 1.31) were produced by this method using water-soluble trithiocarbonates as photoiniferters.

Step-growth polymerization by the RAFT process

Reversible Addition-Fragmentation Chain Transfer (RAFT) step-growth polymerization is an emerging method that synergistically combines the benefits of RAFT polymerization (functional group and user-friendly nature) and step-growth polymerization (versatility of the polymer backbone). This new polymerization method is generally achieved by using bifunctional reagents of monomer and Chain Transfer Agent (CTA), that efficiently yield Single Monomer Unit Insertion (SUMI) adducts under stoichiometrically balanced conditions. This review covers a brief history of the RAFT-SUMI process and its transformation into RAFT step-growth polymerization, followed by a comprehensive discussion of various RAFT step-growth systems. Furthermore, characterizing the molecular weight evolution of step-growth polymerization is elaborated based on the Flory model. Finally, a formula is introduced to describe the efficiency of the RAFT-SUMI process, assuming rapid chain transfer equilibrium. Examples of reported RAFT step-growth and SUMI systems are then categorized based on the driving force.

Examining polymer-protein biophysical interactions with small-angle x-ray scattering and quartz crystal microbalance with dissipation

Polymer-protein hybrids can be deployed to improve protein solubility and stability in denaturing environments. While previous work used robotics and active machine learning to inform new designs, further biophysical information is required to ascertain structure-function behavior. Here, we show the value of tandem small-angle x-ray scattering (SAXS) and quartz crystal microbalance with dissipation (QCMD) experiments to reveal detailed polymer-protein interactions with horseradish peroxidase (HRP) as a test case. Of particular interest was the process of polymer-protein complex formation under thermal stress whereby SAXS monitors formation in solution while QCMD follows these dynamics at an interface. The radius of gyration (Rg ) of the protein as measured by SAXS does not change significantly in the presence of polymer under denaturing conditions, but thickness and dissipation changes were observed in QCMD data. SAXS data with and without thermal stress were utilized to create bead models of the potential complexes and denatured enzyme, and each model fit provided insight into the degree of interactions. Additionally, QCMD data demonstrated that HRP deforms by spreading upon surface adsorption at low concentration as shown by longer adsorption times and smaller frequency shifts. In contrast, thermally stressed and highly inactive HRP had faster adsorption kinetics. The combination of SAXS and QCMD serves as a framework for biophysical characterization of interactions between proteins and polymers which could be useful in designing polymer-protein hybrids.

Automated Multi- and Block-Copolymer Writing in Mesoporous Films Using Visible-Light PET-RAFT and a Microscope

For high throughput applications, e.g., in the context of sensing especially when being combined with machine learning, large sample numbers in acceptable production time are required. This needs automated synthesis and material functionalization concepts ideally combined with high precision. To automate sensing relevant mesopore polymer functionalization while being highly precise in polymer placement, polymer amount control, and polymer sequence design, a process for polymer writing in mesoporous silica films with pore diameter in the range of 13 nm is developed. Mesoporous films are functionalized with different polymers in adjustable polymer amount including block-copolymer functionalization in an automated process using a visible-light induced, controlled photo electron/energy transfer-reversible addition-fragmentation chain-transfer (PET-RAFT) polymerization. While transferring this PET-RAFT to a commercially available microscope, direct, automated laser writing of three different polymers, as well as polymer re-initiation is demonstrated. Using a laser diameter of ≈72 µm, significantly smaller polymer spots of ≈7 µm in diameter are realized. Micrometerscale resolved polymer images including block-copolymers are written into mesoporous layers covering millimeter scale areas requiring a writing time in the range of one second per polymer spot.

Recent developments in visible light induced polymerization towards its application to nanopores

Visible light induced polymerizations are a strongly emerging field in recent years. Besides the often mild reaction conditions, visible light offers advantages of spatial and temporal control over chain growth, which makes visible light ideal for functionalization of surfaces and more specifically of nanoscale pores. Current challenges in nanopore functionalization include, in particular, local and highly controlled polymer functionalizations. Using spatially limited light sources such as lasers or near field modes for light-induced polymer functionalization is envisioned to allow local functionalization of nanopores and thereby improve nanoporous material performance. These light sources are usually providing visible light while classical photopolymerizations are mostly based on UV-irradiation. In this review, we highlight developments in visible light induced polymerizations and especially in visible light induced controlled polymerizations as well as their potential for nanopore functionalization. Existing examples of visible light induced polymerizations in nanopores are emphasized.

Random Heteropolymer Excipients Improve the Colloidal Stability of a Monoclonal Antibody for Subcutaneous Administration

PURPOSE

Developing stable high concentration monoclonal antibody (mAb) formulations is increasingly important to move toward subcutaneous (SC) administration for better patient experience. Challenges stemming from protein-protein interactions in these crowded solutions, such as colloidal instability, limit the feasibility of some formulations because of concerns of safety, product quality, and/or manufacturability. Herein, we report novel random heteropolymer excipients that improve the colloidal stability of a high concentration mAb formulation for SC administration.

METHODS

A library of polymers was synthesized and screened by a high-throughput, absorbance-based assay. The lead polymers were selected and characterized for their ability to alter the precipitation kinetics of a mAb in physiologically relevant conditions using two model systems.

RESULTS

Biophysical testing via surface tension measurements, isothermal titration calorimetry (ITC), microscale thermophoresis (MST), and intrinsic fluorescence quenching indicated that the polymers delayed onset of mAb precipitation from a combination of surfactant behaviour and interactions with the protein to prevent protein-protein interactions leading to colloidal instability.

CONCLUSIONS

The random heteropolymers described are a new class of excipients that may enable development of SC mAb formulations previously inaccessible to patients.

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.

Toward Green Atom Transfer Radical Polymerization: Current Status and Future Challenges

Reversible-deactivation radical polymerizations (RDRPs) have revolutionized synthetic polymer chemistry. Nowadays, RDRPs facilitate design and preparation of materials with controlled architecture, composition, and functionality. Atom transfer radical polymerization (ATRP) has evolved beyond traditional polymer field, enabling synthesis of organic-inorganic hybrids, bioconjugates, advanced polymers for electronics, energy, and environmentally relevant polymeric materials for broad applications in various fields. This review focuses on the relation between ATRP technology and the 12 principles of green chemistry, which are paramount guidelines in sustainable research and implementation. The green features of ATRP are presented, discussing the environmental and/or health issues and the challenges that remain to be overcome. Key discoveries and recent developments in green ATRP are highlighted, while providing a perspective for future opportunities in this area.

Enzyme Catalysis for Reversible Deactivation Radical Polymerization

Enzyme catalysis has been increasingly utilized in reversible deactivation radical polymerization (Enz-RDRP) on account of its mildness, efficiency, and sustainability. In this Minireview we discuss the key roles enzymes play in RDRP, including their ATRPase, initiase, deoxygenation, and photoenzyme activities. We use selected examples to highlight applications of Enz-RDRP in surface brush fabrication, sensing, polymerization-induced self-assembly, and high-throughput synthesis. We also give our reflections on the challenges and future directions of this emerging area.

Machine-Assisted Discovery of Chondroitinase ABC Complexes toward Sustained Neural Regeneration

Among the many molecules that contribute to glial scarring, chondroitin sulfate proteoglycans (CSPGs) are known to be potent inhibitors of neuronal regeneration. Chondroitinase ABC (ChABC), a bacterial lyase, degrades the glycosaminoglycan (GAG) side chains of CSPGs and promotes tissue regeneration. However, ChABC is thermally unstable and loses all activity within a few hours at 37 °C under dilute conditions. To overcome this limitation, the discovery of a diverse set of tailor-made random copolymers that complex and stabilize ChABC at physiological temperature is reported. The copolymer designs, which are based on chain length and composition of the copolymers, are identified using an active machine learning paradigm, which involves iterative copolymer synthesis, testing for ChABC thermostability upon copolymer complexation, Gaussian process regression modeling, and Bayesian optimization. Copolymers are synthesized by automated PET-RAFT and thermostability of ChABC is assessed by retained enzyme activity (REA) after 24 h at 37 °C. Significant improvements in REA in three iterations of active learning are demonstrated while identifying exceptionally high-performing copolymers. Most remarkably, one designed copolymer promotes residual ChABC activity near 30%, even after one week and notably outperforms other common stabilization methods for ChABC. Together, these results highlight a promising pathway toward sustained tissue regeneration.

A High Throughput Approach for Designing Polymers That Mimic the TRAIL Protein

We have leveraged a high throughput approach to design a fully synthetic polymer mimic of the chemotherapeutic protein "TRAIL". Our design enables the synthesis of libraries of star-shaped polymers presenting exactly one receptor binding peptide at the end of each arm with no purification steps. Clear structure-activity relationships in screening for receptor binding and the apoptotic activity on colon cancer lines (COLO205) led us to identify trivalent structures, ∼1.5 nm in hydrodynamic radius as the best mimics. These showed IC₅₀ values ∼2 μM and resulted in the elevated levels of caspase-8 expected from this mechanism of cell death. Our results demonstrate the potential for HTP screening methods to be used in the design of polymers that can mimic a whole range of complex therapeutic proteins.

Development of High Throughput Photopolymerizations Using Micron-Sized Ultrathin Metal-Organic Framework Nanosheets

Polymer syntheses in a high throughput format are still challenging due to the tedious procedures for prior deoxygenation and catalyst removal. Two-dimensional (2D) metal-organic framework (MOF) nanosheets are advantageous for elevating the catalytic efficiency and catalyst recyclability. Polymerization of a wide variety of monomers, including hydrophilic acrylamides and hydrophobic acrylates, was attempted directly in a multi-well plate by employing Zn-ZnPPF-2D nanosheets (PPF = porphyrin paddlewheel framework) as a heterogeneous photocatalyst. Various parameters such as monomer concentration, catalyst concentration and light wavelength were investigated with respect to their effects on polymerization rate and the degree of control over the molecular weight and molecular weight distribution. Due to the larger surface area and more accessible catalytic sites, the top-performing Zn-ZnPPF-2D exhibited fast polymerization kinetics over the Zn-ZnPPF-3D bulk crystals. In addition, the synthesis of triblock copolymers with a single loading of catalysts confirmed the outstanding catalytic performance of these 2D MOF catalysts. Finally, PET-RAFT polymerization was demonstrated to be achievable entirely in a microliter-scale human cell culture medium. As such, this strategy provides high levels of control and precision over macromolecular synthesis outcomes that best align with the requirements of high throughput approaches towards biological applications. This article is protected by copyright. All rights reserved.

Fully Automated Multi-Step Synthesis of Block Copolymers

An automated synthesis protocol is developed for the synthesis of block copolymers in a multi-step approach in a fully automated manner. For this purpose, an automated dialysis setup is combined with robot-based synthesis protocols. Consequently, several block copolymerizations are executed completely automated and compared to the respective manual synthesis. As a result, this study opens up the field of autonomous multi-step reactions without any human interactions.

Fluorescence enables high throughput screening of polyelectrolyte–protein binding affinities

Förster resonance energy transfer (FRET) in combination with high throughput controlled radical polymerisation allows quick identification of polymers that can bind strongly to enzymes such as glucose oxidase.

Machine-Learning-Guided Discovery of 19F MRI Agents Enabled by Automated Copolymer Synthesis

Modern polymer science suffers from the curse of multidimensionality. The large chemical space imposed by including combinations of monomers into a statistical copolymer overwhelms polymer synthesis and characterization technology and limits the ability to systematically study structure-property relationships. To tackle this challenge in the context of 19F magnetic resonance imaging (MRI) agents, we pursued a computer-guided materials discovery approach that combines synergistic innovations in automated flow synthesis and machine learning (ML) method development. A software-controlled, continuous polymer synthesis platform was developed to enable iterative experimental-computational cycles that resulted in the synthesis of 397 unique copolymer compositions within a six-variable compositional space. The nonintuitive design criteria identified by ML, which were accomplished by exploring <0.9% of the overall compositional space, lead to the identification of >10 copolymer compositions that outperformed state-of-the-art materials.

Where Biology and Traditional Polymers Meet: The Potential of Associating Sequence-Defined Polymers for Materials Science

Polymers with precisely defined monomeric sequences present an exquisite tool for controlling material properties by harnessing both the robustness of synthetic polymers and the ability to tailor the inter- and intramolecular interactions so crucial to many biological materials. While polymer scientists traditionally synthesized and studied the physics of long molecules best described by their statistical nature, many biological polymers derive their highly tailored functions from precisely controlled sequences. Therefore, significant effort has been applied toward developing new methods of synthesizing, characterizing, and understanding the physics of non-natural sequence-defined polymers. This perspective considers the synergistic advantages that can be achieved via tailoring both precise sequence control and attributes of traditional polymers in a single system. Here, we focus on the potential of sequence-defined polymers in highly associating systems, with a focus on the unique properties, such as enhanced proton conductivity, that can be attained by incorporating sequence. In particular, we examine these materials as key model systems for studying previously unresolvable questions in polymer physics including the role of chain shape near interfaces and how to tailor compatibilization between dissimilar polymer blocks. Finally, we discuss the critical challenges-in particular, truly scalable synthetic approaches, characterization and modeling tools, and robust control and understanding of assembly pathways-that must be overcome for sequence-defined polymers to attain their potential and achieve ubiquity.

High-Throughput and Combinatorial Approaches for the Development of Multifunctional Polymers

High-throughput (HT) development of new multifunctional polymers is accomplished by the combination of different HT tools established in polymer sciences in the last decade. Important advances are robotic/HT synthesis of polymer libraries, the HT characterization of polymers, and the application of spatially resolved polymer library formats, explicitly microarray and gradient libraries. HT polymer synthesis enables the generation of material libraries with combinatorial design motifs. Polymer composition, molecular weight, macromolecular architecture, etc. may be varied in a systematic, fine-graded manner to obtain libraries with high chemical diversity and sufficient compositional resolution as model systems for the screening of these materials for the functions aimed. HT characterization allows a fast assessment of complementary properties, which are employed to decipher quantitative structure-properties relationships. Moreover, these methods facilitate the HT determination of important surface parameters by spatially resolved characterization methods, including time-of-flight secondary ion mass spectrometry and X-ray photoelectron spectroscopy. Here current methods for the high-throughput robotic synthesis of multifunctional polymers as well as their characterization are presented and advantages as well as present limitations are discussed.

Light-Mediated Polymerization Induced by Semiconducting Nanomaterials: State-of-the-Art and Future Perspectives

Direct capture of solar energy for chemical transformation via photocatalysis proves to be a cost-effective and energy-saving approach to construct organic compounds. With the recent growth in photosynthesis, photopolymerization has been established as a robust strategy for the production of specialty polymers with complex structures, precise molecular weight, and narrow dispersity. A key challenge in photopolymerization is the scarcity of effective photomediators (photoinitiators, photocatalysts, etc.) that can provide polymerization with high yield and well-defined polymer products. Current efforts on developing photomediators have mainly focused on organic dyes and metal complexes. On the other hand, nanomaterials (NMs), particularly semiconducting nanomaterials (SNMs), are suitable candidates for photochemical reactions due to their unique optical and electrical properties, such as high absorption coefficients, large charge diffusion lengths, and broad absorption spectra. This review provides a comprehensive insight into SNMs' photomediated polymerizations and highlights the roles SNMs play in photopolymerizations, types of polymerizations, applications in producing advanced materials, and the future directions.

High-Throughput Routes to Biomaterials Discovery

Many existing clinical treatments are limited in their ability to completely restore decreased or lost tissue and organ function, an unenviable situation only further exacerbated by a globally aging population. As a result, the demand for new medical interventions has increased substantially over the past 20 years, with the burgeoning fields of gene therapy, tissue engineering, and regenerative medicine showing promise to offer solutions for full repair or replacement of damaged or aging tissues. Success in these fields, however, inherently relies on biomaterials that are engendered with the ability to provide the necessary biological cues mimicking native extracellular matrixes that support cell fate. Accelerating the development of such "directive" biomaterials requires a shift in current design practices toward those that enable rapid synthesis and characterization of polymeric materials and the coupling of these processes with techniques that enable similarly rapid quantification and optimization of the interactions between these new material systems and target cells and tissues. This manuscript reviews recent advances in combinatorial and high-throughput (HT) technologies applied to polymeric biomaterial synthesis, fabrication, and chemical, physical, and biological screening with targeted end-point applications in the fields of gene therapy, tissue engineering, and regenerative medicine. Limitations of, and future opportunities for, the further application of these research tools and methodologies are also discussed.

Automation and data-driven design of polymer therapeutics

Polymers are uniquely suited for drug delivery and biomaterial applications due to tunable structural parameters such as length, composition, architecture, and valency. To facilitate designs, researchers may explore combinatorial libraries in a high throughput fashion to correlate structure to function. However, traditional polymerization reactions including controlled living radical polymerization (CLRP) and ring-opening polymerization (ROP) require inert reaction conditions and extensive expertise to implement. With the advent of air-tolerance and automation, several polymerization techniques are now compatible with well plates and can be carried out at the benchtop, making high throughput synthesis and high throughput screening (HTS) possible. To avoid HTS pitfalls often described as "fishing expeditions," it is crucial to employ intelligent and big data approaches to maximize experimental efficiency. This is where the disruptive technologies of machine learning (ML) and artificial intelligence (AI) will likely play a role. In fact, ML and AI are already impacting small molecule drug discovery and showing signs of emerging in drug delivery. In this review, we present state-of-the-art research in drug delivery, gene delivery, antimicrobial polymers, and bioactive polymers alongside data-driven developments in drug design and organic synthesis. From this insight, important lessons are revealed for the polymer therapeutics community including the value of a closed loop design-build-test-learn workflow. This is an exciting time as researchers will gain the ability to fully explore the polymer structural landscape and establish quantitative structure-property relationships (QSPRs) with biological significance.

Automated PET-RAFT Polymerization Towards Pharmaceutical Amorphous Solid Dispersion Development

In pharmaceutical oral drug delivery development, about 90% of drugs in the pipeline have poor aqueous solubility leading to severe challenges with oral bioavailability and translation to effective and safe drug products. Amorphous solid dispersions (ASDs) have been utilized to enhance the oral bioavailability of poorly soluble active pharmaceutical ingredients (APIs). However, a limited selection of regulatory-approved polymer excipients exists for the development and further understanding of tailor-made ASDs. Thus, a significant need exists to better understand how polymers can be designed to interact with specific API moieties. Here, we demonstrate how an automated combinatorial library approach can be applied to the synthesis and screening of polymer excipients for the model drug probucol. We synthesized a library of 25 random heteropolymers containing one hydrophilic monomer (2-hydroxypropyl acrylate (HPA)) and four hydrophobic monomers at varied incorporation. The performance of ASDs made by a rapid film casting method was evaluated by dissolution using ultra-performance liquid chromatography (UPLC) sampling at various time points. This combinatorial library and rapid screening strategy enabled us to identify a relationship between polymer hydrophobicity, monomer hydrophobic side group geometry, and API dissolution performance. Remarkably, the most effective synthesized polymers displayed slower drug release kinetics compared to industry standard polymer excipients, showing the ability to modulate the drug release profile. Future coupling of high throughput polymer synthesis, high throughput screening (HTS), and quantitative modeling would enable specification of designer polymer excipients for specific API functionalities.

Digital Transformation in Materials Science: A Paradigm Change in Material's Development

The ongoing digitalization is rapidly changing and will further revolutionize all parts of life. This statement is currently omnipresent in the media as well as in the scientific community; however, the exact consequences of the proceeding digitalization for the field of materials science in general and the way research will be performed in the future are still unclear. There are first promising examples featuring the potential to change discovery and development approaches toward new materials. Nevertheless, a wide range of open questions have to be solved in order to enable the so-called digital-supported material research. The current state-of-the-art, the present and future challenges, as well as the resulting perspectives for materials science are described.

More than skin deep: using polymers to facilitate topical delivery of nitric oxide

Skin, the largest organ in the human body, provides several important functions, including providing protection from mechanical impacts, micro-organisms, radiation and chemicals; regulation of body temperature; the sensations of touch and temperature; and the synthesis of several substances including vitamin D, melanin, and keratin. Common dermatological disorders (CDDs) include inflammatory or immune-mediated skin diseases, skin infection, skin cancer, and wounds. In the treatment of skin disorders, topical administration has advantages over other routes of administration, and polymers are widely used as vehicles to facilitate the delivery of topical therapeutic agents, serving as matrices to keep therapeutic agents in contact with the skin. Nitric oxide (NO), a cellular signalling molecule, has attracted significant interest in treating a broad spectrum of diseases, including various skin disorders. However, there are a number of challenges in effectively delivering NO. It must be delivered in a controlled manner at sufficient concentrations to be efficacious and the delivery system must be stable during storage. The use of polymer-based systems to deliver NO topically can be an effective strategy to overcome these challenges. There are three main approaches for incorporating NO with polymers in topical delivery systems: (i) physical incorporation of NO donors into polymer bases; (ii) covalent attachment of NO donors to polymers; and (iii) encapsulation of NO donors in polymer-based particles. The latter two approaches provide the greatest control over NO release and have been used by numerous researchers in treating CDDs, including chronic wounds and skin cancer.

How does the single unit monomer insertion technique promote kinetic analysis of activation and initiation in photo-RAFT processes?

The single unit monomer insertion technique provides a simple platform for the kinetic investigation of early stage of photo-RAFT process that comprises photo-activation of initial RAFT agents and addition of RAFT leaving radicals to the monomers.

Ring opening polymerization of ε-caprolactone through water

Ring opening polymerization (ROP) through water is used to synthesize biodegradable polymers such as polycaprolactone (PCL).

Benchtop Preparation of Polymer Brushes by SI-PET-RAFT: The Effect of the Polymer Composition and Structure on Inhibition of a Pseudomonas Biofilm

We report a high-throughput method for producing surface-tethered polymeric brushes on glass substrates via surface-initiated photoinduced electron transfer-reversible addition-fragmentation chain transfer polymerization (SI-PET-RAFT). Due to its excellent oxygen tolerance, SI-PET-RAFT allows brush growth using low reagent volumes (30 μL) without prior degassing. An initial 28 homopolymer brush library was successfully prepared and screened with respect to their antifouling performance. The high-throughput approach was further exploited to expand the library to encompass statistical, gradient, and block architectures to investigate the effect of monomer composition and distribution using two monomers of disparate performance. In this manner, the degree of attachment from Gram-negative Pseudomonas aeruginosa (PA) bacterial biofilms could be tuned between the bounds set by the homopolymer brushes.

Targeted sequence design within the coarse-grained polymer genome

The chemical design of polymers with target structural and/or functional properties represents a grand challenge in materials science. While data-driven design approaches are promising, success with polymers has been limited, largely due to limitations in data availability. Here, we demonstrate the targeted sequence design of single-chain structure in polymers by combining coarse-grained modeling, machine learning, and model optimization. Nearly 2000 unique coarse-grained polymers are simulated to construct and analyze machine learning models. We find that deep neural networks inexpensively and reliably predict structural properties with limited sequence information as input. By coupling trained ML models with sequential model-based optimization, polymer sequences are proposed to exhibit globular, swollen, or rod-like behaviors, which are verified by explicit simulations. This work highlights the promising integration of coarse-grained modeling with data-driven design and represents a necessary and crucial step toward more complex polymer design efforts.

Progress and Perspectives Beyond Traditional RAFT Polymerization

The development of advanced materials based on well-defined polymeric architectures is proving to be a highly prosperous research direction across both industry and academia. Controlled radical polymerization techniques are receiving unprecedented attention, with reversible-deactivation chain growth procedures now routinely leveraged to prepare exquisitely precise polymer products. Reversible addition-fragmentation chain transfer (RAFT) polymerization is a powerful protocol within this domain, where the unique chemistry of thiocarbonylthio (TCT) compounds can be harnessed to control radical chain growth of vinyl polymers. With the intense recent focus on RAFT, new strategies for initiation and external control have emerged that are paving the way for preparing well-defined polymers for demanding applications. In this work, the cutting-edge innovations in RAFT that are opening up this technique to a broader suite of materials researchers are explored. Emerging strategies for activating TCTs are surveyed, which are providing access into traditionally challenging environments for reversible-deactivation radical polymerization. The latest advances and future perspectives in applying RAFT-derived polymers are also shared, with the goal to convey the rich potential of RAFT for an ever-expanding range of high-performance applications.

Automated Polymer Purification Using Dialysis

The automated dialysis of polymers in synthetic robots is described as a first approach for the purification of polymers using an automated protocol. For this purpose, a dialysis apparatus was installed within a synthesis robot. Therein, the polymer solution could be transferred automatically into the dialysis tube. Afterwards, a permanent running dialysis could be started, enabling the removal of residual monomer. Purification efficiency was studied using chromatography and NMR spectroscopy, showing that the automated dialysis requires less solvent and is faster compared to the classical manual approach.

Autonomous Discovery in the Chemical Sciences Part II: Outlook

This two-part Review examines how automation has contributed to different aspects of discovery in the chemical sciences. In this second part, we reflect on a selection of exemplary studies. It is increasingly important to articulate what the role of automation and computation has been in the scientific process and how that has or has not accelerated discovery. One can argue that even the best automated systems have yet to "discover" despite being incredibly useful as laboratory assistants. We must carefully consider how they have been and can be applied to future problems of chemical discovery in order to effectively design and interact with future autonomous platforms. The majority of this Review defines a large set of open research directions, including improving our ability to work with complex data, build empirical models, automate both physical and computational experiments for validation, select experiments, and evaluate whether we are making progress towards the ultimate goal of autonomous discovery. Addressing these practical and methodological challenges will greatly advance the extent to which autonomous systems can make meaningful discoveries.

Combinatorial Biomaterials Discovery Strategy to Identify New Macromolecular Cryoprotectants

Cryoprotective agents (CPAs) are typically solvents or small molecules, but there is a need for innovative CPAs to reduce toxicity and increase cell yield, for the banking and transport of cells. Here we use a photochemical high-throughput discovery platform to identify macromolecular cryoprotectants, as rational design approaches are currently limited by the lack of structure-property relationships. Using liquid handling systems, 120 unique polyampholytes were synthesized using photopolymerization with RAFT agents. Cryopreservation screening identified "hit" polymers and nonlinear trends between composition and function, highlighting the requirement for screening, with polymer aggregation being a key factor. The most active polymers reduced the volume of dimethyl sulfoxide (DMSO) required to cryopreserve a nucleated cell line, demonstrating the potential of this approach to identify materials for cell storage and transport.