Self-immolative polymers are a growing class of degradable polymers that undergo end-to-end depolymerization after the stimuli-responsive cleavage of an end-cap or backbone unit. Their incorporation into amphiphilic block copolymers can lead to functions such as the disintegration of copolymer nanoassemblies when depolymerization is triggered. However, diblock copolymers have not yet been developed where both blocks are self-immolative. Described here is the synthesis, self-assembly, and triggered depolymerization of self-immolative block copolymers with individually triggerable hydrophilic and hydrophobic blocks. Neutral and cationic hydrophilic polyglyxoylamides (PGAm) with acid-responsive end caps were synthesized and coupled to an ultraviolet (UV) light-triggerable poly(ethyl glyoxylate) (PEtG) hydrophobic block. The resulting block copolymers self-assembled to form nanoparticles in aqueous solution, and their depolymerization in response to acid and UV light was studied by techniques including light scattering, NMR spectroscopy, and electron microscopy. Acid led to selective depolymerization of the PGAm blocks, leading to aggregation, while UV light led to selective depolymerization of the PEtG block, leading to disassembly. This self-immolative block copolymer system provides an enhanced level of control over smart copolymer assemblies and their degradation.

With DNA-based nanomaterials being designed for applications in cellular environments, the need arises to accurately understand their surface interactions toward biological targets. As for any material exposed to protein-rich cell culture conditions, a protein corona will establish around DNA nanoparticles, potentially altering the a-priori designed particle function. Here, we first set out to identify the protein corona around DNA origami nanomaterials, taking into account the application of stabilizing block co-polymer coatings (oligolysine-1kPEG or oligolysine-5kPEG) widely used to ensure particle integrity. By implementing a label-free methodology, the distinct polymer coating conditions show unique protein profiles, predominantly defined by differences in the molecular weight and isoelectric point of the adsorbed proteins. Interestingly, none of the applied coatings reduced the diversity of the proteins detected within the specific coronae. We then biased the protein corona through pre-incubation with selected proteins and show significant changes in the cell uptake. Our study contributes to a deeper understanding of the complex interplay between DNA nanomaterials, proteins, and cells at the bio-interface.

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.

It is our pleasure to write this Editorial for the ACS Polymers Au 2022 Rising Stars in Polymers virtual special issue. This virtual special issue is a collection of peer-reviewed Articles, Perspectives, and Reviews presenting impactful research in polymer science and engineering, from laboratories led by 12 outstanding independent early career researchers from around the world. We hope you enjoy learning about these principal investigators and their laboratories’ current research interests. ACS Polymers Au’s 2022 Rising Stars in Polymers. Dr. Shrayesh N. Patel is currently an Assistant Professor in the Pritzker School of Molecular Engineering at the University of Chicago. He holds a joint appointment in the Chemical Sciences and Engineering Division at Argonne National Lab, and is also a member of the Joint Center for Energy Storage Research (JCESR) – a DOE Energy Innovation Hub. Dr. Patel completed his undergraduate degree at the Georgia Institute of Technology in Chemical and Biomolecular Engineering in 2007, then received his Ph.D. in Chemical Engineering from the University of California, Berkeley in 2013 under the supervision of Dr. Nitash P. Balsara. Before joining the University of Chicago, he was a postdoctoral research associate in the Materials Research Laboratory at the University of California, Santa Barbara under the supervision of Dr. Michael Chabinyc and Dr. Edward Kramer. Dr. Patel’s research interests focus on enabling polymers for sustainable energy systems through fundamental understanding of charge and mass transport, relevant to energy storage and conversion devices such as lithium-ion and beyond lithium-ion batteries, redox flow batteries, and thermoelectrics. Overall, his research expertise lies at the interface of polymer science and engineering, electrochemistry, and organic electronics. You can learn about his group’s research by visiting: https://pme.uchicago.edu/group/patel-group. His Article for this issue is titled “Structure–Transport Properties Governing the Interplay in Humidity-Dependent Mixed Ionic and Electronic Conduction of Conjugated Polyelectrolytes”. Article DOI:10.1021/acspolymersau.2c00005. Dr. Miao Hong is currently Full Professor of Chemistry in the Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences. She obtained her B.S. in Chemistry at Northeastern Normal University in 2007 and received a Ph.D. degree in Polymer Chemistry and Physics in 2013 under the supervision of Dr. Yuesheng Li from the Changchun Institute of Applied Chemistry, Chinese Academy of Sciences. After a four-year postdoctoral stint at Colorado State University with Dr. Eugene Y.-X. Chen, Dr. Hong joined the Shanghai Institute of Organic in the Chemistry Chinese Academy of Sciences in 2017. Her group’s research is centered on polymer science, catalytic chemistry, green and sustainable chemistry. One of her main interests is the development of new catalysts and novel methodologies for the synthesis of sustainable polymers with controlled structures from renewable feedstocks. You can learn about Dr. Miao Hong and her research by visiting: http://miaohong.sioc.ac.cn. Her Article for this issue is titled “Zinc-Mediated Allylation-Lactonization One-Pot Reaction to Methylene Butyrolactones: Renewable Monomers for Sustainable Acrylic Polymers with Closed-Loop Recyclability”. Article DOI: 10.1021/acspolymersau.2c00001. Dr. Helen Tran is currently an Assistant Professor at the University of Toronto in the Department of Chemistry (cross-appointed in the Department of Chemical Engineering). She received her B.S. in Chemistry with a minor in Chemical Engineering from UC Berkeley in 2009, conducting undergraduate research with Dr. Tsu-Jae King Liu (Electrical Engineering, Berkeley). In 2009–2011, she was a postbaccalaureate fellow in Dr. Ronald Zuckermann’s research group at the Molecular Foundry at Berkeley National Laboratories, exploring the self-assembly of biomimetic polymers into 2D nanosheets. She completed her Ph.D. at Columbia University in 2016 under the supervision of Dr. Luis Campos, broadly investigating hierarchical ordering and periodic patterning in block copolymer systems. She was an Intelligence Community postdoctoral fellow at Stanford University under the mentorship of Dr. Zhenan Bao in the Chemical Engineering Department 2016–2020, where she worked on stretchable and biodegradable electronics. Dr. Tran is interested in building next-generation electronics that will autonomously respond to local stimuli for applications in environmental monitoring, advanced consumer products, and health diagnostics for personalized therapy. Dr. Tran’s team leverages a rich palette of polymer chemistry to design new materials encoded with information for self-assembly, degradability, and electronic transport. You can learn about Dr. Helen Tran and her research group by visiting: https://helen-t.com/. Her Perspective for this issue is titled “A Field Guide to Optimizing Peptoid Synthesis”. Article DOI: 10.1021/acspolymersau.2c00036. Dr. Lutz Nuhn is currently leading the Chair of Macromolecular Chemistry at the Julius-Maximilians-University in Würzburg, Germany. He studied biomedical chemistry at the Johannes Gutenberg-University in Mainz (Germany) and received his diploma degree in 2010. In 2008–2009, he worked in the laboratories of Dr. Robert Langer (MIT, USA). For his doctoral degree he studied in the group of Dr. Rudolf Zentel, and during summer 2013 also in the group of Dr. Kazunori Kataoka (University of Tokyo, Japan). In 2014, he was awarded a Ph.D. with distinction from the Johannes Gutenberg-University, Mainz. For his postdoctoral research, he worked with Dr. Bruno De Geest and Dr. Richard Hoogenboom at Ghent University. In summer 2017, Dr. Nuhn joined the group of Dr. Tanja Weil at the Max Planck Institute for Polymer Research in Mainz. In April 2022 he was appointed as full Professor at the Julius-Maximilians-University in Würzburg. His research focuses on the synthesis and application of multiresponsive and degradable polymeric nanocarrier systems, especially for immunotherapeutic purposes. You can learn about his group and research at: https://www.chemie.uni-wuerzburg.de/mmc/. His Article for this issue is titled “Nontoxic N-Heterocyclic Olefin Catalyst Systems for Well-Defined Polymerization of Biocompatible Aliphatic Polycarbonates”. Article DOI: 10.1021/acspolymersau.2c00017. Dr. Jian Qin is currently an Assistant Professor in the Department of Chemical Engineering at Stanford University. He received his B.S. (2002) and M.S. (2004) degrees in Materials Science from Tsinghua University, and his Ph.D. (2009) in Materials Science from University of Minnesota under the supervision of Dr. David Morse and Dr. Frank Bates. He worked as postdoctoral fellow with Dr. Scott Milner at Penn State University (2009–2012), and with Dr. Juan de Pablo at the University of Chicago (2012–2015). His research focuses on theoretical modeling of ionic and electronically active polymers, the rheology of entangled polymers, and associative polymers. More information about his group and research can be found at: http://web.stanford.edu/~jianq/. His Article for this issue is titled “Distribution Cutoff for Clusters near the Gel Point”. Article DOI: 10.1021/acspolymersau.2c00020. Dr. Joseph (Joe) P. Patterson is currently an Assistant Professor in the Department of Chemistry at the University of California, Irvine. He received his master’s degree in Chemistry from the University of York, UK in 2009. In 2013, he completed his Ph.D. in Chemistry under the guidance of Dr. Rachel O’Reilly at the University of Warwick, UK. He worked as a postdoctoral researcher at the University of California, San Diego under the guidance of Dr. Nathan Gianneschi. He also worked in the Laboratory of Materials and Interface Chemistry at the Eindhoven University of Technology, under the guidance of Dr. Nico Sommerdijk. His research includes the development of new materials through a deep understanding of their structural dynamics. He is particularly interested in the development and application of advanced electron microscopy methods. You can learn about Dr. Patterson and his research group at http://www.thepattersonlab.com. His Article for this issue is titled “Gaining Structural Control by Modification of Polymerization Rate in Ring-Opening Polymerization-Induced Crystallization-Driven Self-Assembly”. Article DOI: 10.1021/acspolymersau.2c00027. Dr. Xiangcheng Pan is currently an Associate Professor and principal investigator in the State Key Laboratory of Molecular Engineering of Polymers and the Department of Macromolecular Science at Fudan University. He received a B.S. degree (Magna Cum Laude) from Eastern Washington University in 2009. He obtained his Ph.D. in organic chemistry from the University of Pittsburgh under the guidance of Dr. Dennis P. Curran in 2014. He then spent three years doing postdoctoral research with the group of Dr. Krzysztof Matyjaszewski at Carnegie Mellon University. In 2017, he returned to China and joined Fudan University. His research group at Fudan University has been focused on developing novel radical polymerization methods and heteroatom-involved controlled/precise polymer synthesis. More information about Dr. Pan and his research can be found at http://www.panxlab.com. His Review for this issue is titled “N-Coordinated Organoboron in Polymer Synthesis and Material Science”. Article DOI: 10.1021/acspolymersau.2c00046. Dr. Louis M. Pitet is currently an Assistant Professor at Hasselt University, working in the Institute for Materials Research (IMO), located in Hasselt, Belgium. He obtained his bachelor’s degree in chemistry from the Colorado School of Mines working with Dr. Daniel Knauss. He went on to obtain a Ph.D. in 2011 in the Chemistry department at the University of Minnesota under the supervision of Dr. Marc Hillmyer. Dr. Pitet moved to The Netherlands for a postdoctoral fellowship in the Institute for Complex Molecular Systems at the Eindhoven University of Technology, working with Dr. Bert Meijer. Since 2018, he has been leading his research group in Hasselt focusing on understanding processing–structure–property relationships in complex functional polymer constructs. His group is interested in the fundamental relationships that are critical for global challenges in polymer science, including reutilizing plastic waste streams, creating smart scaffolds for tissue engineering, and improving processing–manufacturing efficiency with advanced reactors. You can learn about his group and research at www.uhasselt.be/en/onderzoeksgroepen-en/imo-imomec-afp/people/prof-dr-louis-pitet. His Article for this issue is titled “Utility of Chemical Upcycling in Transforming Postconsumer PET to PBT-Based Thermoplastic Copolyesters Containing a Renewable Fatty-Acid-Derived Soft Block”. Article DOI: 10.1021/acspolymersau.2c00019. Dr. Davide Michieletto is currently a Royal Society University Research Fellow & Reader at the University of Edinburgh. He received a Physics degree from the University of Padova (Italy) in 2009 (BSc) and a MSc in Theoretical Physics again from the University of Padova in 2011. He then moved to the University of Warwick (UK) where he first received a MSc degree in Complexity Science (2012), and then he did a Ph.D. in Physics and Complexity Science under the supervision of Dr. Matthew Turner (2012–2015). He subsequently worked as a postdoctoral researcher with Dr. Davide Marenduzzo and Dr. Nick Gilbert on computational models of genome organization and on super-resolution microscopy of chromatin structure (2016–2019). In 2019, Dr. Michieletto worked with Dr. Dorothy Buck at the University of Bath on DNA topology. Dr. Michieletto’s research is inspired by how sophisticated proteins exert exquisite topological and mechanical control over the organization and function of the DNA in our cells, and his work aims to discover novel topological soft materials and complex fluids with exotic viscoelastic properties. You can learn more about his research from his group Web site: https://www2.ph.ed.ac.uk/http://web.stanford.edu/~jianq/dmichiel/index.html. His Article for this issue is titled “Geometric Predictors of Knotted and Linked Arcs. Article DOI: 10.1021/acspolymersau.2c00021. Dr. Maxwell Robb is currently an Assistant Professor of Chemistry in the Division of Chemistry and Chemical Engineering at Caltech. He obtained his B.S. in Chemistry (2009) from the Colorado School of Mines where he began research in synthetic polymer chemistry under the guidance of Dr. Daniel M. Knauss. Max carried out his doctoral studies in the laboratories of Dr. Craig J. Hawker at the University of California, Santa Barbara, earning his Ph.D. in Chemistry in 2014. Dr. Robb conducted postdoctoral research with Dr. Jeffrey S. Moore at the University of Illinois, Urbana–Champaign as a Beckman Institute Postdoctoral Fellow from 2014–2017. In September 2017, he joined the faculty at Caltech. Research in Dr. Robb’s group seeks to advance fundamental understanding of mechanical force transduction at the molecular level and develop strategies to create force-responsive molecules and functional polymeric materials. You can learn more about Dr. Maxwell Robb and his group webpage at http://robbgroup.caltech.edu. His Article for this issue is titled “Competitive Activation Experiments Reveal Significantly Different Mechanochemical Reactivity of Furan–Maleimide and Anthracene–Maleimide Mechanophores”. Article DOI: 10.1021/acspolymersau.2c00047. Dr. Fiona L. Hatton is currently a Lecturer in Polymer Chemistry in the Department of Materials at Loughborough University. Dr. Hatton obtained a first class MChem degree in Medicinal Chemistry with Pharmacology from the University of Liverpool in 2010. She stayed at the University of Liverpool for her Ph.D. (2010–2014) which focused on the preparation of highly branched dendritic polymers, hyperbranched polydendrons, and using ATRP for biomedical applications, with Dr. Steve Rannard. In 2014, she joined the Division of Coating Technology, KTH Royal Institute of Technology, Stockholm as a postdoctoral researcher with Dr. Anna Carlmark and Dr. Eva Malmström. In 2016, Dr Hatton joined the group of Dr. Steve Armes at the University of Sheffield as a postdoctoral research associate, before taking up a permanent position at Loughborough University. Her research interests are in sustainable polymer science, for example reducing single use plastic by focusing on reuse systems, facilitated by the fluorescent labeling of packaging. Within this theme she also researches renewable monomer synthesis and their polymerization using aqueous radical polymerization techniques and also has interests in block copolymer self-assembly. You can learn about Dr. Hatton and her research group from https://hattonpolymergroup.com/. Her Review for this issue is titled “Enabling the Polymer Circular Economy: Innovations in Photoluminescent Labeling of Plastic Waste for Enhanced Sorting”. Article DOI: 10.1021/acspolymersau.2c00040. Dr. Danielle J. Mai is currently an Assistant Professor of Chemical Engineering at Stanford University. She earned her B.SE in Chemical Engineering from the University of Michigan in 2011 and her Ph.D. in Chemical Engineering from the University of Illinois at Urbana–Champaign in 2016, under the guidance of Dr. Charles M. Schroeder. After that, she became the Arnold O. Beckman Postdoctoral Fellow in Dr. Bradley D. Olsen’s group at MIT from 2016 to 2019. Dr. Mai’s lab integrates precise biopolymer engineering with multiscale experimental characterization to advance biomaterials development and to enhance fundamental understanding of soft matter physics. Her current research interests include engineering calcium-responsive polypeptides as muscle-mimetic materials, investigating graft biopolymers to elucidate bio- lubrication mechanisms, and developing polymer nanocomposites to 3D-print biocompatible hydrogels. You can learn about Dr. Danielle Mai and her research group from https://mailab.stanford.edu. Her Article for this issue is titled “Gelation dynamics during photo-cross-linking of polymer nanocomposite hydrogels”. Article DOI: 10.1021/acspolymersau.2c00051. We extend our sincere thanks and congratulations to these Rising Stars. We are grateful to the referees for their input in reviewing these manuscripts and to you, our readers, for your support. We hope you enjoy reading the contributions from these outstanding investigators and members of their teams as much as we have. This article has not yet been cited by other publications. ACS Polymers Au’s 2022 Rising Stars in Polymers.

Inspired by how certain proteins “sense” knots and entanglements in DNA molecules, here, we ask if local geometric features that may be used as a readout of the underlying topology of generic polymers exist. We perform molecular simulations of knotted and linked semiflexible polymers and study four geometric measures to predict topological entanglements: local curvature, local density, local 1D writhe, and nonlocal 3D writhe. We discover that local curvature is a poor predictor of entanglements. In contrast, segments with maximum local density or writhe correlate as much as 90% of the time with the shortest knotted and linked arcs. We find that this accuracy is preserved across different knot types and also under significant spherical confinement, which is known to delocalize essential crossings in knotted polymers. We further discover that nonlocal 3D writhe is the best geometric readout of the knot location. Finally, we discuss how these geometric features may be used to computationally analyze entanglements in generic polymer melts and gels.

Indacenodithiophene (IDT) copolymers are a class of conjugated polymers that have limited long-range order and high hole mobilities, which makes them promising candidates for use in deformable electronic devices. Key to their high hole mobilities is the coplanar monomer repeat units within the backbone. Poly(indacenodithiophene-benzothiadiazole) (PIDTC16-BT) and poly(indacenodithiophene-thiapyrollodione) (PIDTC16-TPDC1) are two IDT copolymers with planar backbones, but they are brittle at low molecular weight and have unsuitably high elastic moduli. Substitution of the hexadecane (C16) side chains of the IDT monomer with isocane (C20) side chains was performed to generate a new BT-containing IDT copolymer: PIDTC20-BT. Substitution of the methyl (C1) side chain on the TPD monomer for an octyl (C8) and 6-ethylundecane (C13B) afford two new TPD-containing IDT copolymers named PIDTC16-TPDC8 and PIDTC16-TPDC13B, respectively. Both PIDTC16-TPDC8 and PIDTC16-TPDC13B are relatively well deformable, have a low yield strain, and display significantly reduced elastic moduli. These mechanical properties manifest themselves because the lengthened side chains extending from the TPD-monomer inhibit precise intermolecular ordering. In PIDTC16-BT, PIDTC20-BT and PIDTC16-TPDC1 side chain ordering can occur because the side chains are only present on the IDT subunit, but this results in brittle thin films. In contrast, PIDTC16-TPDC8 and PIDTC16-TPDC13B have disordered side chains, which seems to lead to low hole mobilities. These results suggest that disrupting the interdigitation in IDT copolymers through comonomer side chain extension leads to more ductile thin films with lower elastic moduli, but decreased hole mobility because of altered local order in the respective thin films. Our work, thus, highlights the trade-off between molecular packing structure for deformable electronic materials and provides guidance for designing new conjugated polymers for stretchable electronics.

A grand challenge in polymer science lies in the predictive design of new polymeric materials with targeted functionality. However, de novo design of functional polymers is challenging due to the vast chemical space and an incomplete understanding of structure–property relations. Recent advances in deep generative modeling have facilitated the efficient exploration of molecular design space, but data sparsity in polymer science is a major obstacle hindering progress. In this work, we introduce a vast polymer database known as the Open Macromolecular Genome (OMG), which contains synthesizable polymer chemistries compatible with known polymerization reactions and commercially available reactants selected for synthetic feasibility. The OMG is used in concert with a synthetically aware generative model known as Molecule Chef to identify property-optimized constitutional repeating units, constituent reactants, and reaction pathways of polymers, thereby advancing polymer design into the realm of synthetic relevance. As a proof-of-principle demonstration, we show that polymers with targeted octanol–water solubilities are readily generated together with monomer reactant building blocks and associated polymerization reactions. Suggested reactants are further integrated with Reaxys polymerization data to provide hypothetical reaction conditions (e.g., temperature, catalysts, and solvents). Broadly, the OMG is a polymer design approach capable of enabling data-intensive generative models for synthetic polymer design. Overall, this work represents a significant advance, enabling the property targeted design of synthetic polymers subject to practical synthetic constraints.

Polymer materials are multiscale systems by definition. Already the description of a single macromolecule involves a multitude of scales, and cooperative processes in polymer assemblies are governed by their interplay. Polymers have been among the first materials for which systematic multiscale techniques were developed, yet they continue to present extraordinary challenges for modellers. In this Perspective, we review popular models that are used to describe polymers on different scales and discuss scale-bridging strategies such as static and dynamic coarse-graining methods and multiresolution approaches. We close with a list of hard problems which still need to be solved in order to gain a comprehensive quantitative understanding of polymer systems.

Enzyme-responsive polymeric micelles hold great potential as drug delivery systems due to the overexpression of disease-associated enzymes. To achieve selective and efficient delivery of their therapeutic cargo, micelles need to be highly stable and yet disassemble when encountering their activating enzyme at the target site. However, increased micellar stability is accompanied by a drastic decrease in enzymatic degradability. The need to balance between stability and enzymatic degradation has severely limited the therapeutic applicability of enzyme-responsive nanocarriers. Here, we report a general modular approach for designing stable enzyme-responsive micelles whose enzymatic degradation can be enhanced on demand. The control over their response to the activating enzyme is achieved by stimuli-induced splitting of triblock amphiphiles into two identical diblock amphiphiles, which have the same hydrophilic–lipophilic balance as the parent amphiphile. This architectural transition drastically affects the micelle–unimer equilibrium and therefore increases the sensitivity of the micelles toward enzymatic degradation. As a proof of concept, we designed UV- and reduction-activated splitting mechanisms, demonstrating the ability to use architectural transition as a tool for tuning amphiphile–protein interactions, providing a general solution toward overcoming the stability–degradability barrier for enzyme-responsive nanocarriers.

Single-chain nanoparticles (SCNPs) are intriguing materials inspired by proteins that consist of a single precursor polymer chain that has collapsed into a stable structure. In many prospective applications, such as catalysis, the utility of a single-chain nanoparticle will intricately depend on the formation of a mostly specific structure or morphology. However, it is not generally well understood how to reliably control the morphology of single-chain nanoparticles. To address this knowledge gap, we simulate the formation of 7680 distinct single-chain nanoparticles from precursor chains that span a wide range of, in principle, tunable patterning characteristics of cross-linking moieties. Using a combination of molecular simulation and machine learning analyses, we show how the overall fraction of functionalization and blockiness of cross-linking moieties biases the formation of certain local and global morphological characteristics. Importantly, we illustrate and quantify the dispersity of morphologies that arise due to the stochastic nature of collapse from a well-defined sequence as well as from the ensemble of sequences that correspond to a given specification of precursor parameters. Moreover, we also examine the efficacy of precise sequence control in achieving morphological outcomes in different regimes of precursor parameters. Overall, this work critically assesses how precursor chains might be feasibly tailored to achieve given SCNP morphologies and provides a platform to pursue future sequence-based design.

Polyphosphazenes represent a class of intrinsically flexible polyelectrolytes with potent immunoadjuvant activity, which is enabled through non-covalent self-assembly with antigenic proteins by charge complexation. The formation of supramolecular complexes between polyphosphazene adjuvant, poly[di(carboxylatophenoxy)phosphazene] (PCPP), and a model vaccine antigen, hen egg lysozyme, was studied under physiological conditions using automated dynamic light scattering titration, asymmetric flow field flow fractionation (AF4), enzyme-linked immunosorbent assay (ELISA), and fluorescent quenching methods. Three regimes of self-assembly were observed covering complexation of PCPP with lysozyme in the nano-scale range, multi-chain complexes, and larger aggregates with complexes characterized by a maximum loading of over six hundred protein molecules per PCPP chain and dissociation constant in the micromolar range (Kd = 7 × 10–6 mol/L). The antigenicity of PCPP bound lysozyme, when compared to equivalent lysozyme solutions, was largely retained for all complexes, but observed a dramatic reduction for heavily aggregated systems. Routes to control the complexation regimes with elevated NaCl or KCl salt concentrations indicate ion-specific effects, such that more smaller-size complexes are present at higher NaCl, counterintuitive with respect to PCPP solubility arguments. While the order of mixing shows a prominent effect at lower stoichiometries of mixing, higher NaCl salt reduces the effect all together.

It is widely accepted that moving from a linear to circular economy for plastics will be beneficial to reduce plastic pollution in our environment and to prevent loss of material value. However, challenges within the sorting of plastic waste often lead to contaminated waste streams that can devalue recyclates and hinder reprocessing. Therefore, the improvement of the sorting of plastic waste can lead to dramatic improvements in recyclate quality and enable circularity for plastics. Here, we discuss current sorting methods for plastic waste and review labeling techniques to enable enhanced sorting of plastic recyclates. Photoluminescent-based labeling is discussed in detail, including UV–vis organic and inorganic photoluminescent markers, infrared up-conversion, and X-ray fluorescent markers. Methods of incorporating labels within packaging, such as extrusion, surface coatings, and incorporation within external labels are also discussed. Additionally, we highlight some practical models for implementing some of the sorting techniques and provide an outlook for this growing field of research.

BigSMILES, a line notation for encapsulating the molecular structure of stochastic molecules such as polymers, was recently proposed as a compact and readable solution for writing macromolecules. While BigSMILES strings serve as useful identifiers for reconstructing the molecular connectivity for polymers, in general, BigSMILES allows the same polymer to be codified into multiple equally valid representations. Having a canonicalization scheme that eliminates the multiplicity would be very useful in reducing time-intensive tasks like structural comparison and molecular search into simple string-matching tasks. Motivated by this, in this work, two strategies for deriving canonical representations for linear polymers are proposed. In the first approach, a canonicalization scheme is proposed to standardize the expression of BigSMILES stochastic objects, thereby standardizing the expression of overall BigSMILES strings. In the second approach, an analogy between formal language theory and the molecular ensemble of polymer molecules is drawn. Linear polymers can be converted into regular languages, and the minimal deterministic finite automaton uniquely associated with each prescribed language is used as the basis for constructing the unique text identifier associated with each distinct polymer. Overall, this work presents algorithms to convert linear polymers into unique structure-based text identifiers. The derived identifiers can be readily applied in chemical information systems for polymers and other polymer informatics applications.

We present machine learning models for the prediction of thermal and mechanical properties of polymers based on the graph convolutional network (GCN). GCN-based models provide reliable prediction performances for the glass transition temperature (Tg), melting temperature (Tm), density (ρ), and elastic modulus (E) with substantial dependence on the dataset, which is the best for Tg (R2 ∼ 0.9) and worst for E (R2 ∼ 0.5). It is found that the GCN representations for polymers provide prediction performances of their properties comparable to the popular extended-connectivity circular fingerprint (ECFP) representation. Notably, the GCN combined with the neural network regression (GCN-NN) slightly outperforms the ECFP. It is investigated how the GCN captures important structural features of polymers to learn their properties. Using the dimensionality reduction, we demonstrate that the polymers are organized in the principal subspace of the GCN representation spaces with respect to the backbone rigidity. The organization in the representation space adaptively changes with the training and through the NN layers, which might facilitate a subsequent prediction of target properties based on the relationships between the structure and the property. The GCN models are found to provide an advantage to automatically extract a backbone rigidity, strongly correlated with Tg, as well as a potential transferability to predict other properties associated with a backbone rigidity. Our results indicate both the capability and limitations of the GCN in learning to describe polymer systems depending on the property.

The development of novel biomaterials is a challenging process, complicated by a design space with high dimensionality. Requirements for performance in the complex biological environment lead to difficult a priori rational design choices and time-consuming empirical trial-and-error experimentation. Modern data science practices, especially artificial intelligence (AI)/machine learning (ML), offer the promise to help accelerate the identification and testing of next-generation biomaterials. However, it can be a daunting task for biomaterial scientists unfamiliar with modern ML techniques to begin incorporating these useful tools into their development pipeline. This Perspective lays the foundation for a basic understanding of ML while providing a step-by-step guide to new users on how to begin implementing these techniques. A tutorial Python script has been developed walking users through the application of an ML pipeline using data from a real biomaterial design challenge based on group’s research. This tutorial provides an opportunity for readers to see and experiment with ML and its syntax in Python. The Google Colab notebook can be easily accessed and copied from the following URL: www.gormleylab.com/MLcolab

Figure 1. Optical force probe concept and exemplary tunable characteristics, such as scission force threshold, spectral properties, and temporal response for reversibility. Figure 2. Mechanochemical approaches to sustainable polymer chemistry exemplarily shown for life cycle prolongation by secondary bond formation reactions using monomers (M) and for a circular economy by depolymerization to monomers. Figure 3. Principle of sonopharmacology for drug (D) activation (upper panel) and associated standing challenges for its successful future biomedical application, such as reduction in ultrasound doses by reducing sonication time, compatibilization with therapeutic and diagnostic ultrasound frequencies, or increase of loaded drug content (lower panel). The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. A.H. was financially supported by the European Research Council Advanced Grant (694610). R.G. is grateful for support by a Freigeist-Fellowship of the Volkswagen Foundation (92888). We are grateful for fruitful discussions with our colleagues Carsten Bolm, Walter Richtering, Fabian Kießling, and Andrij Pich on the perspectives and potential of polymer mechanochemistry. This article references 64 other publications. This article has not yet been cited by other publications. Figure 1. Optical force probe concept and exemplary tunable characteristics, such as scission force threshold, spectral properties, and temporal response for reversibility. Figure 2. Mechanochemical approaches to sustainable polymer chemistry exemplarily shown for life cycle prolongation by secondary bond formation reactions using monomers (M) and for a circular economy by depolymerization to monomers. Figure 3. Principle of sonopharmacology for drug (D) activation (upper panel) and associated standing challenges for its successful future biomedical application, such as reduction in ultrasound doses by reducing sonication time, compatibilization with therapeutic and diagnostic ultrasound frequencies, or increase of loaded drug content (lower panel). This article references 64 other publications.

The encoded precision of biological polymers enables a few simple monomers (e.g., four nucleotides in nucleic acids) to create complex macromolecular structures that accomplish a myriad of functions. Similar spatial precision in synthetic polymers and oligomers can be harnessed to create macromolecules and materials with rich and tunable properties. Recent exciting advances in iterative solid- and solution-phase synthetic strategies have led to the scalable production of discrete macromolecules, which in turn has enabled the study of sequence-dependent material properties. Our recent example of a scalable synthetic strategy using inexpensive vanillin-based monomers to create sequence-defined oligocarbamates (SeDOCs) enabled the preparation of isomeric oligomers with different thermal and mechanical properties. We show that unimolecular SeDOCs also exhibit sequence-dependent dynamic fluorescence quenching that persists from solution to the solid phase. We detail the evidence for this phenomenon and show that changes in fluorescence emissive properties are dependent on macromolecular conformation, which in turn is driven by sequence.

Recently dithiol-yne click chemistry and its role in the formation of cross-linked polymer networks and postpolymerization functionalizations has been studied; however, no research has considered this technique to form comb polymers with regular side chains. Here we report the first example of a grafted-through step-growth comb polymer via the utilization of dithiol-yne “click chemistry”. First, we exhibited the efficacy of this reaction to produce modest-molecular-weight combs (Mn = 16 kDa). Second, we displayed the ability to precisely control the length of the side chains of these combs along with the space between the side chains by using various molecular weights of propargylated PEG chains and a variety of alkanedithiol backbone spacers. The primary species of these reactions were macrocyclic comb polymers, with a smaller amount of dithiol-terminated comb polymers.

N-Substituted glycines (peptoids) are a class of peptidomimetic molecules used as materials for health, environmental, and drug delivery applications. Automated solid-phase synthesis is the most widely used approach for preparing polypeptoids, with a range of published protocols and modifications for selected synthetic targets. Simultaneously, emerging solution-phase syntheses are being leveraged to overcome limitations in solid-phase synthesis and access high-molecular weight polypeptoids. This Perspective aims to outline strategies for the optimization of both solid- and solution-phase synthesis, provide technical considerations for robotic synthesizers, and offer an outlook on advances in synthetic methodologies. The solid-phase synthesis sections explore steps for protocol optimization, accessing complex side chains, and adaptation to robotic synthesizers; the sections on solution-phase synthesis cover the selection of initiators, side chain compatibility, and strategies for controlling polymerization efficiency and scale. This text acts as a “field guide” for researchers aiming to leverage the flexibility and adaptability of peptoids in their research.

Water existing within thin polyelectrolyte multilayer (PEM) films has significant influence on their physical, chemical, and thermal properties, having implications for applications including energy storage, smart coatings, and biomedical systems. Ionic strength, salt type, and terminating layer are known to influence PEM swelling. However, knowledge of water’s microenvironment within a PEM, whether that water is affiliated with intrinsic or extrinsic ion pairs, remains lacking. Here, we examine the influence of both assembly and post-assembly conditions on the water–ion pair interactions of poly(styrene sulfonate)/poly(diallyldimethylammonium) (PSS/PDADMA) PEMs in NaCl and KBr. This is accomplished by developing a methodology in which quartz crystal microbalance with dissipation monitoring is applied to estimate the number of water molecules affiliated with an ion pair (i), as well as the hydration coefficient, πsaltH2O. PSS/PDADMA PEMs are assembled in varying ionic strengths of either NaCl and KBr and then exposed post-assembly to increasing ionic strengths of matching salt type. A linear relationship between the total amount of water per intrinsic ion pair and the post-assembly salt concentration was obtained at post-assembly salt concentrations >0.5 M, yielding estimates for both i and πsaltH2O. We observe higher values of i and πsaltH2O in KBr-assembled PEMs due to KBr being more effective in doping the assembly because of KBr’s more chaotropic nature as compared to NaCl. Lastly, when PSS is the terminating layer, i decreases in value due to PSS’s hydrophobic nature. Classical and ab initio molecular dynamics provide a microstructural view as to how NaCl and KBr interact with individual polyelectrolytes and the involved water shells. Put together, this study provides further insight into the understanding of existing water microenvironments in PEMs and the effects of both assembly and post-assembly conditions.