The chemical reversion of polymers via ring-closing depolymerization (RCD) to their monomeric constituents is a highly promising avenue to enable end-of-life recycling and reuse. However, most reported systems using RCD revolve around bespoke monomer designs to facilitate facile depolymerization, and there exists relatively few investigations into the influence of functional groups on the ability of a particular monomer to cleanly undergo depolymerization. Here, we perform computational investigations into the energy barriers for RCD of 6-membered aliphatic carbonates in different solvents. The results corroborate trends observed in prior experimental studies, validating the utility of computational investigations towards understanding RCD. Experimental evaluation of the thermal depolymerization in two of the studied polycarbonates confirmed their ability to undergo RCD. Overall, this work highlights the advantage of high-throughput energy barrier computations to provide meaningful insight into broad reactivity trends that would be highly laborious to access experimentally.

Since the remarkable breakthrough of COVID-19 mRNA vaccines, lipid nanoparticles (LNPs) have gained substantial attention as the most cutting-edge clinical formulations for mRNA delivery. PEGylated lipid (PEG-lipid) has been regarded as an essential constituent of LNPs that helps to prolong their systemic circulation by preventing particle aggregation in the blood and sequestration by the mononuclear phagocyte system. Herein, we synthesized a series of mRNA-loaded nanoparticles by replacing ALC-0159 (a PEG-lipid used in the Comirnaty formulation) with amphiphilic PEG-polycarbonate diblock copolymers (PC-HNPs). Interestingly, variations of polycarbonate block length and structure significantly influenced mRNA encapsulation efficiency, transfection potency, colloidal stability, and PEG shedding rate of PC-HNPs. In vivo and ex vivo bioluminescence imaging revealed that upon subcutaneous administration in mice, the leading candidate PC3-HNP achieved lymph node accumulation comparable to that of the conventional ALC-0159-based LNP formulation while avoiding undesirable liver accumulation. Our findings may provide valuable information for the construction of next-generation nanocarriers for effective mRNA delivery.

The development of a low‐cost photopolymer resin to fabricate optical glass of high refractive index for plastic optics is reported. This new free radically polymerizable photopolymer resin, termed, disulfide methacrylate resin (DSMR) is synthesized by the direct addition of allyl methacrylate to a commodity sulfur petrochemical, sulfur monochloride (S 2 Cl 2 ). The rapid rates of free radical photopolymerization confer significant advantages in preparing high‐quality, bulk optical glass. The low‐cost, optical glass produced from this photopolymer possesses a desirable combination of high refractive index ( n ≈ 1.57–1.59), low birefringence (Δ n < 10 −4 ), high glass transition values ( T g ≈ 100 °C), along with optical transparency rivaling, or exceeding that of poly(methyl methacrylate) (PMMA) as indicated by very low optical absorption coefficients ( α < 0.05 cm −1 at 1310 nm) measured for thick glass DSMR photopolymer samples (diameter ( D ) = 25 mm; thickness = 1–30 mm). The versatile manufacturability of DSMR photopolymers for both molding and diamond turn machining methods is demonstrated to prepare precision optics and nano‐micropatterned arrays. Finally, large‐scale 3D printing vat photopolymerization of DSMR using high‐area rapid printing digital light processing additive manufacturing is demonstrated.

Molecular property prediction and generative design via deep learning models has been the subject of intense research given its potential to accelerate development of new, high-performance materials. More recently, these workflows have been significantly augmented with the advent of large language models (LLMs) and systems of LLM-driven agents capable of utilizing pre-trained models to make predictions in the context of more complex research tasks. While effective, there is still room for substantial improvement within the agentic systems on the retrieval of salient information for material design tasks. Moreover, alternative uses of predictive deep learning models, such as leveraging their latent representations to facilitate cross-modal retrieval augmented generation within agentic systems to enable task-specific materials design, has remained unexplored. Herein, we demonstrate that large, pre-trained chemistry foundation models can serve as a basis for enabling semantic chemistry information retrieval for both small-molecules, complex polymeric materials, and reactions. Additionally, we show the use of chemistry foundation models in conjunction with image models such as OpenCLIP facilitate unprecedented queries and information retrieval across multiple characterization data domains. Finally, we demonstrate the integration of these systems within multi-agent systems to facilitate structure and topological-based natural language queries and information retrieval for complex research tasks.

Organocatalyzed ring-opening polymerization (ROP) is a versatile technique for synthesizing biodegradable polymers, including polyesters and polycarbonates. We introduce o-phenylene bisurea (OPBU) (di)anions as a novel class of organocatalysts that are fast, easily tunable, mildly basic, and exceptionally selective. These catalysts surpass previous generations, such as thiourea, urea, and TBD, in selectivity (k(p)/k(tr)) by 8 to 120 times. OPBU catalysts facilitate the ROP of various monomers, achieving high conversions (>95%) in seconds to minutes, producing polymers with precise molecular weights and very low dispersities (& Dstrok; approximate to 1.01). This performance nearly matches the ideal distribution expected from living polymerization (Poisson distribution). Density functional theory (DFT) calculations reveal that the catalysts stabilize the oxyanion transition state via a hydrogen bond pocket similar to the "oxyanion hole" in enzymatic catalysis. Both experimental and theoretical analyses highlight the critical role of the semirigid o-phenylene linker in creating a hydrogen bond pocket that is tight yet flexible enough to accommodate the oxyanion transition state effectively. These new insights have provided a new class of organic catalysts whose accessibility, moderate basicity, excellent solubility, and unparalleled selectivity and tunability open up new opportunities for controlled polymer synthesis.

Organocatalyzed ring-opening polymerization is a powerful tool for the synthesis of a variety of functional, readily degradable polyesters and polycarbonates. We report the use of (thio)ureas in combination with cyclopropenimine bases as a unique catalyst for the polymerization of cyclic esters and carbonates with a large span of reactivities. Methodologies of exceptionally effective and selective cocatalyst combinations were devised to produce polyesters and polycarbonates with narrow dispersities (Đ = 1.01-1.10). Correlations of the pKa of the various ureas and cyclopropenimine bases revealed the critical importance of matching the pKa of the two cocatalysts to achieve the most efficient polymerization conditions. It was found that promoting strong H-bonding interactions with a noncompetitive organic solvent, such as CH2Cl2, enabled greatly increased polymerization rates. The stereoselective polymerization of rac-lactide afforded stereoblock poly(lactides) that crystallize as stereocomplexes, as confirmed by wide-angle X-ray scattering.

Carbon dioxide (CO2) is an abundant C1 feedstock with tremendous potential to produce versatile building blocks in synthetic applications. Given the adverse impact of CO2 on the atmosphere, it is of paramount importance to devise strategies for upcycling it into useful materials, such as polymers and fine chemicals. To activate such stable molecule, superbases offer viable modes of binding to CO2. In this study, a superbase cyclopropenimine derivative was found to exhibit exceptional proficiency in activating CO2 and mediating its polymerization at ambient temperature and pressure for the synthesis of polyurethanes. The versatility of this reaction can be extended to monofunctional amines and alcohols, yielding a variety of functional carbonates and carbamates.

A library of structurally related heterocycles containing N-H motifs are explored as ring-opening polymerization (ROP) precatalysts. Upon deprotonation of these heterocycles with appropriate bases, the corresponding salts are formed, which catalyze the ROP of various lactones and cyclic carbonates, affording polymers with dispersity values ranging from 1.01 to 1.12. These catalysts exhibit a wide range of catalytic activities, spanning over 7 orders of magnitude (>10(7)), with their relative rates generally correlating to the pK(a) of the N-H group in the neutral heterocycle. Despite apparent structural and electronic similarities, these heterocycle catalysts display markedly different kinetic behaviors, regarding the identity of different cations. Kinetic and NMR studies have revealed two distinct sets of mechanisms: small alkali-metal cations such as Li+ and Na+ reduce the activity of imidazol-(in)-e-derived catalysts due to their tendency to associate with the alkoxide chain end, thus inhibiting its propagation; conversely, these cations form robust cation-pi assemblies with indolocarbazole anions, simultaneously binding and activating monomer carbonyls toward the nucleophilic attack, resulting in a significant rate enhancement. This distinctive activation motif of the indolocarbazole sets it apart from other catalysts by utilizing cations as a potent handle for modulating the polymerization reactivity. Coupled with its high availability, good solubility, high activity, moderate basicity, and high selectivity, the indolocarbazole heterocycle emerges as one of the most versatile organocatalysts for ring-opening polymerization.

Anticancer drug resistance is a large contributing factor to the global mortality rate of cancer patients. Anticancer macromolecules such as polymers have been recently reported to overcome this issue. Anticancer macromolecules have unselective toxicity because they are highly positively charged. Herein, an anionic biodegradable polycarbonate carrier is synthesized and utilized to form nanocomplexes with an anticancer polycarbonate via self-assembly to neutralize its positive charges. Biotin is conjugated to the anionic carrier and serves as cancer cell-targeting moiety. The nanoparticles have sizes of < 130 nm with anticancer polymer loading levels of 38-49%. Unlike the small molecular anticancer drug doxorubicin, the nanocomplexes effectively inhibit the growth of both drug-susceptible MCF7 and drug-resistant MCF7/ADR human breast cancer cell lines with low half maximal inhibitory concentration (IC50). The nanocomplexes increase the anticancer polymer's in vivo half-life from 1 to 6-8 h, and rapidly kill BT474 human breast cancer cells primarily via an apoptotic mechanism. The nanocomplexes significantly increase the median lethal dose (LD50) and reduce the injection site toxicity of the anticancer polymer. They suppress tumor growth by 32-56% without causing any damage to the liver and kidneys. These nanocomplexes may potentially be used for cancer treatment to overcome drug resistance.

Helicobacter pylori, the world's most common chronic infection-causing pathogen, is responsible for causing gastric ulcers, the fourth-leading cause of cancer-related death globally in 2020. In recent years, the effectiveness of the current treatment regimen (two antibiotics and one proton pump inhibitor) has often been plagued with problems such as resistance and the undesired elimination of commensal bacteria. Herein, we report the synthesis of block and random copolycarbonates, functionalized with cationic guanidinium and anionic acetate functional groups, aimed at selectively killing H. pylori in the acidic environment of the stomach, while remaining nontoxic to the commensal bacteria in the gut. The compositions of the polymers were fine-tuned so that the polymers were readily dispersed in water without any difficulty at both pH 3.0 and 7.4. The self-assembly behavior of the polymers at different pH values by dynamic light scattering showed that the random and block copolymers formed stable micelles in a simulated gastric environment (pH 3.0) while aggregated at pH 7.4. Both polymers demonstrated stronger antibacterial activity against H. pylori than the guanidinium-functionalized homopolymer without any acetate functional group at pH 3.0. The block copolymer was significantly more bactericidal at pH 3.0 across the concentrations tested, as compared to the random copolymer, while it did not show significant toxicity toward rat red blood cells (rRBCs) and HK-2 cells or bactericidal effect toward E. coli (a common gut bacterium) and nor caused aggregation of rRBCs at its effective concentration and at physiological pH of 7.4. Additionally, both the block and random copolymers were much more stable against hydrolysis at pH 3.0 than at pH 7.4. This study provides insight into the influence of both polymer architecture and dynamic assembly on the bioactivities of antimicrobial polymers, where the disassembly of coacervates into narrowly dispersed micelles at pH 3 make them potent antimicrobials aided by the protonated carboxylic acid block.

Advances in machine learning (ML) and automated experimentation are poised to vastly accelerate research in polymer science. Data representation is a critical aspect for enabling ML integration in research workflows, yet many data models impose significant rigidity making it difficult to accommodate a broad array of experiment and data types found in polymer science. This inflexibility presents a significant barrier for researchers to leverage their historical data in ML development. Here we show that a domain specific language, termed Chemical Markdown Language (CMDL), provides flexible, extensible, and consistent representation of disparate experiment types and polymer structures. CMDL enables seamless use of historical experimental data to fine-tune regression transformer (RT) models for generative molecular design tasks. We demonstrate the utility of this approach through the generation and the experimental validation of catalysts and polymers in the context of ring-opening polymerization—although we provide examples of how CMDL can be more broadly applied to other polymer classes. Critically, we show how the CMDL tuned model preserves key functional groups within the polymer structure, allowing for experimental validation. These results reveal the versatility of CMDL and how it facilitates translation of historical data into meaningful predictive and generative models to produce experimentally actionable output.

The global pandemic due to the severe acute respiratorysyndromecoronavirus 2 (SARS-CoV-2) has led to more than 661 million infectionsand over 6.6 million deaths worldwide since its inception in 2019.There is an urgent need for safe and effective antiviral solutions,especially those with rapid inactivation kinetics, to help preventviral transmissions. In this study, we report the design and synthesisof amphiphilic sulfonated polycarbonates and investigate the effectof polycarbonate compositions on their antiviral activity. A polycarbonatewith an optimal balance of the hydrophilic anionic sulfonate (& SIM;30%)and the hydrophobic n-butyl (& SIM;70%) pendentgroups inactivates various types of coronaviruses in solution, includinghuCoV-OC43, ancestral wild-type SARS-CoV-2, and the delta variant,within seconds, while exhibiting low cytotoxicity to mammalian cells(i.e., Vero E6) at the virucidal concentration. This amphiphilic sulfonatedpolycarbonate may be a promising solution to inactivate viruses assprays for prevention of viral infection.

Aliphatic polycarbonates represent an important class of materials with diverse applications ranging from battery electrolytes, polyurethane intermediates, and materials for biomedical applications. These materials can be produced via the ring-opening polymerization (ROP) of six- to eight-membered cyclic carbonates derived from precursor 1,3- and 1,5-diols. These diols can contain a range of functional groups depending on the desired thermal, mechanical, and solution properties. Generally, the ring closure to form the cyclic carbonate requires the use of undesirable and hazardous reagents. Advances in synthetic methodologies and catalysis have enabled the use of carbon dioxide (CO2) to perform these transformations with a high conversion of diol to cyclic carbonate, yet modest isolated yields due to oligomerization side reactions. In this Letter, we evaluate a series of bases in the presence of p-toluenesulfonyl chloride and the appropriate diol to better understand their effect on the yield and presence of oligomer byproducts during cyclic carbonate formation from CO2. From this study, N,N-tetramethylethylenediamine (TMEDA) was identified as an optimal base, facilitating the preparation of a diverse array of both six- and eight-membered carbonates from CO2 within 10 to 15 min. The robust conditions for both, the preparation of the diol precursor, and the TMEDA-mediated carbonate synthesis enabled readily telescoping the two-step reaction sequence, greatly simplifying the process of monomer preparation.

A new organocatalyst for the ring-opening polymerization of lactones has been identified. Under the tested conditions, the anions of 2,2'-bisindole promote fast, living polymerizations (as short as 10 ms) which are selective for chain elongation over transesterification (Đ ≤ 1.1). While structurally related to (thio)urea anion catalysts, anions of 2,2'-bisindole activate the monomer via the counterion rather than through hydrogen bonding. This new activation motif enables modulation of the polymerization rate by 2 orders of magnitude by changing the counterion.

Accessing cyclic carbonate monomers on a large scale is critical for the development of any new carbonate-based materials platform. The synthesis of carbonate monomers can be a challenging and tedious endeavor requiring multiple synthetic steps and purifications. To address this, we report a drastically improved process for the synthesis of carbonate monomers via a two-step route that avoids the use of hazardous triphosgene or chloroformate reagents. This process enables rapid access to a broad array of functional groups on the carbonate monomer and the monomers generated from the procedure can readily be polymerized via ring-opening polymerization.

Conducting polymerizations under continuous-flow conditions affords distinct advantages over batch experimentation and has increasingly been employed by the research community for chain-growth polymerizations to accelerate material discovery and to finely tune material properties. This work now expands on the reported advances by demonstrating the utility of continuous flow for polyaddition reactions of polyurethanes (PUs). Various reactor configurations enable the on- demand organocatalytic one-step synthesis of linear PUs with tailored soft-to-hard segment ratios in 3-5 min at room temperature. In-line analytics is implemented for real-time process monitoring and to develop process control mechanisms, increasing the control over monomer conversion and material quality and minimizing batch-to-batch variations. The reported strategies thus provide a guideline for the straightforward preparation of libraries of commercially relevant PU materials.

Chemical recycling of plastic waste represents a greener alternative to landfill and incineration, and potentially offers a solution to the environmental consequences of increased plastic waste. Most plastics that are widely used today are designed for durability, hence currently available depolymerisation methods typically require harsh conditions and when applied to blended and mixed plastic feeds generate a mixture of products. Herein, we demonstrate that the energetic differences for the glycolysis of BPA-PC and PET in the presence of a protic ionic salt TBD:MSA catalyst enables the selective and sequential depolymerisation of these two commonly employed polymers. Employing the same procedure, functionalised cyclic carbonates can be obtained from both mixed plastic wastes and industrial polymer blend. This methodology demonstrates that the concept of catalytic depolymerisation offers great potential for selective polymer recycling and also presents plastic waste as a "greener" alternative feedstock for the synthesis of high added value molecules.

Rationale: Use of traditional anticancer chemotherapeutics has been hindered by the multifactorial nature of multi-drug resistance (MDR) development and metastasis.Recently, cationic polycarbonates were reported as novel unconventional anticancer agents that mitigated MDR and inhibited metastasis.The aim of this study is to explore structure-anticancer activity relationship.Specifically, a series of cationic guanidinium-based random copolymers of varying hydrophobicity was synthesized with a narrow polydispersity (Ð = 1.12-1.27)via organocatalytic ring-opening polymerization (OROP) of functional cyclic carbonate monomers, and evaluated for anticancer activity, killing kinetics, degradability and functional mechanism.Methods: Linear, branched and aromatic hydrophobic side chain units, such as ethyl, benzyl, butyl, isobutyl and hexyl moieties were explored as comonomer units for modulating anticancer activity.As hydrophobicity/hydrophilicity balance of the polymers determines their anticancer efficacy, the feed ratio between the two monomers was varied to tune their hydrophobicity.Results: Notably, incorporating the hexyl moiety greatly enhanced anticancer efficiency and killing kinetics on cancer cells.Degradation studies showed that the polymers degraded completely within 4-6 days.Flow cytometry and lactate dehydrogenase (LDH) release analyses demonstrated that anticancer mechanism of the copolymers containing a hydrophobic co-monomer was concentration dependent, apoptosis at IC50, and both apoptosis and necrosis at 2 × IC50.In contrast, the homopolymer without a hydrophobic comonomer killed cancer cells predominantly via apoptotic mechanism. Conclusion:The hydrophobicity of the polymers played an important role in anticancer efficacy, killing kinetics and anticancer mechanism.This study provides valuable insights into designing novel anticancer agents utilizing polymers.

In this study, a series of guanidinium-functionalized polycarbonate random co-polymers is prepared from organocatalytic ring-opening polymerization to investigate the effect of the hydrophobic side chain (ethyl, propyl, isopropyl, benzyl, and hexyl) on their antimicrobial activity and selectivity. Although the polymers exhibit similar minimum inhibitory concentrations, the more hydrophobic polymers exhibit a faster rate of bacteria elimination. At higher percentage content (20 mol%), polymers with more hydrophobic side chains suffer from poor selectivity due to their high hemolytic activity. The highly hydrophobic co-polymer, containing the hydrophobic hexyl-functionalized cyclic carbonate, kills bacteria via a membrane-disruptive mechanism. Micelle formation leads to a lower extent of membrane disruption. This study unravels the effects of hydrophobic side chains on the activities of the polymers and their killing mechanism, providing insights into the design of new antimicrobial polymers.

The de novo design of antimicrobial therapeutics involves the exploration of a vast chemical repertoire to find compounds with broad-spectrum potency and low toxicity. Here, we report an efficient computational method for the generation of antimicrobials with desired attributes. The method leverages guidance from classifiers trained on an informative latent space of molecules modelled using a deep generative autoencoder, and screens the generated molecules using deep-learning classifiers as well as physicochemical features derived from high-throughput molecular dynamics simulations. Within 48 days, we identified, synthesized and experimentally tested 20 candidate antimicrobial peptides, of which two displayed high potency against diverse Gram-positive and Gram-negative pathogens (including multidrug-resistant Klebsiella pneumoniae ) and a low propensity to induce drug resistance in Escherichia coli . Both peptides have low toxicity, as validated in vitro and in mice. We also show using live-cell confocal imaging that the bactericidal mode of action of the peptides involves the formation of membrane pores. The combination of deep learning and molecular dynamics may accelerate the discovery of potent and selective broad-spectrum antimicrobials.