Search for: All records

Abstract Sustainable polymers from renewable resources have been gaining importance due to their recyclability and reduced environmental impact. However, their development through conventional trial‐and‐error methods remains inefficient and resource‐intensive. Machine learning (ML) has emerged as a powerful tool in polymer science, enabling rapid prediction, and discovery of new chemicals and materials. In this review, we examine emerging trends in ML applications for sustainable polymer development, focusing on catalyst discovery, property optimization, and new polymer design. We analyze unique challenges in applying ML to sustainable polymers and evaluate proposed solutions, providing insights for future development in this rapidly evolving field. 

Abstract Linear poly(α‐hydroxy acids) are important degradable polymers, and they can be efficiently prepared by ring‐opening polymerization of O‐carboxyanhydrides with pendant functional groups. However, attempts to prepare cyclic poly(α‐hydroxy acids) have been plagued by side reactions, including epimerization and uncontrolled intramolecular chain transfers or termination, that prevent the synthesis of high‐molecular‐weight stereoregular cyclic polyesters. Herein, we report a scalable method for the synthesis of high‐molecular‐weight (>100 kDa) stereoregular functionalized cyclic poly(α‐hydroxy acids) by means of controlled polymerization of O‐carboxyanhydrides using a catalytic system consisting of a lanthanum complex with a sterically bulky ligand and a manganese silylamide. Additionally, using this system, we could readily prepare cyclic block poly(α‐hydroxy acids) by means of sequential addition of O‐carboxyanhydrides. The obtained cyclic polyesters and their cyclic block copolyesters exhibit distinctive physicochemical properties—including elevated phase transition temperature, improved toughness, and reduced viscosity—compared to their linear counterparts. 

Abstract Stereoselective ring-opening polymerization catalysts are used to produce degradable stereoregular poly(lactic acids) with thermal and mechanical properties that are superior to those of atactic polymers. However, the process of discovering highly stereoselective catalysts is still largely empirical. We aim to develop an integrated computational and experimental framework for efficient, predictive catalyst selection and optimization. As a proof of principle, we have developed a Bayesian optimization workflow on a subset of literature results for stereoselective lactide ring-opening polymerization, and using the algorithm, we identify multiple new Al complexes that catalyze either isoselective or heteroselective polymerization. In addition, feature attribution analysis uncovers mechanistically meaningful ligand descriptors, such as percent buried volume (%V_bur) and the highest occupied molecular orbital energy (E_HOMO), that can access quantitative and predictive models for catalyst development. 

Abstract Immune checkpoint blockade antibodies have promising clinical applications but suffer from disadvantages such as severe toxicities and moderate patient–response rates. None of the current delivery strategies, including local administration aiming to avoid systemic toxicities, can sustainably supply drugs over the course of weeks; adjustment of drug dose, either to lower systemic toxicities or to augment therapeutic response, is not possible. Herein, we develop an implantable miniaturized device using electrode-embedded optical fibers with both local delivery and measurement capabilities over the course of a few weeks. The combination of local immune checkpoint blockade antibodies delivery via this device with photodynamic therapy elicits a sustained anti-tumor immunity in multiple tumor models. Our device uses tumor impedance measurement for timely presentation of treatment outcomes, and allows modifications to the delivered drugs and their concentrations, rendering this device potentially useful for on-demand delivery of potent immunotherapeutics without exacerbating toxicities. 

Poly(α-hydroxy acids), as a family of biodegradable polyesters, are valuable materials due to their broad applications in packaging, agriculture, and biomedical engineering. Herein we highlight and explore recent advances of catalysts in controlled ring-opening polymerization of O-carboxyanhydrides towards functionalized poly(α-hydroxy acids), especially metal catalyst-mediated controlled polymerization. Limitations of current polymerization strategies of O-carboxyanhydrides are discussed. 

Photoredox ring-opening polymerization of O -carboxyanhydrides allows for the synthesis of polyesters with precisely controlled molecular weights, molecular weight distributions, and tacticities. While powerful, obviating the use of precious metal-based photocatalysts would be attractive from the perspective of simplifying the protocol and enabling unexpected reactivity. Herein, we report the Co and Zn catalysts that are activated by external light to mediate efficient ring-opening polymerization of O -carboxyanhydrides, without the use of exogenous precious metal-based photocatalysts. Our methods allow for the synthesis of isotactic polyesters with high molecular weights (>200 kDa) and narrow molecular weight distributions ( M w / M n < 1.1). Mechanistic studies indicate that light activates the oxidative status of Co III intermediate that is generated from the regioselective ring-opening of the O -carboxyanhydride. We also demonstrate that the use of Zn or Hf complexes together with Co can allow for stereoselective photoredox ring-opening polymerizations of multiple racemic O -carboxyanhydrides to synthesize syndiotactic and stereoblock copolymers, which vary widely in their glass transition temperatures.