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Degradable polymers are promising materials for use to reduce plastic waste and advance biomedical applications. However, to meet the demands of specific applications, tailoring the properties of degradable polymers through precise modification of their chemical structures is critical. Herein, we present a new class of degradable and functionalizable polyacetals synthesized by the ring-opening metathesis copolymerization (ROMP) of two commercially available monomers: dimethyl oxanorbornadiene-2,3-dicarboxylate (OND) and 4,7-dihydro-1,3-dioxepin (DXP). The resulting polyacetals are not only acid-degradable but also readily and efficiently functionalizable via thia–Michael addition, yielding degradable polymer materials with various functional groups and tunable thermal properties. 

Here we report the design and synthesis of acid-degradable and functionalizable polymers via alternating ring-opening metathesis copolymerization of oxanorbornadiene dicarboxylate and 2,3-dihydrofuran. The resulting polymers can undergo post-polymerization modification through... 

Under the $\mathrm{AdS}/\mathrm{CFT}$correspondence, asymptotically anti–de Sitter geometries with backreaction can be viewed as conformal field theory states subject to a renormalization group (RG) flow from an ultraviolet (UV) description toward an infrared (IR) sector. For black holes, however, the IR point is the horizon, so one way to interpret the interior is as an analytic continuation to a “trans-IR” imaginary-energy regime. In this paper, we demonstrate that this analytic continuation preserves some imprints of the UV physics, particularly near its “end point” at the classical singularity. We focus on holographic phase transitions of geometric objects in round black holes. We first assert the consistency of interpreting such black holes, including their interiors, as RG flows by constructing a monotonic $a$function. We then explore how UV phase transitions of entanglement entropy and scalar two-point functions, each of which are encoded by bulk geometry under the holographic mapping, are related to the structure of the near-singularity geometry, which is quantified by Kasner exponents. Using 2D holographic flows triggered by relevant scalar deformations as test beds, we find that the 3D bulk’s near-singularity Kasner exponents can be viewed as functions of the UV physics precisely when the deformation is nonzero. Published by the American Physical Society2024 

Biomass-derived polymer materials are emerging as sustainable and low-carbon footprint alternatives to the current petroleum-based commodity plastics. In the past decade, the ring-opening metathesis polymerization (ROMP) technique has been widely used for the polymerization of cyclic olefin monomers derived from biorenewable resources, giving rise to a diverse set of biobased polymer materials. However, most synthetic biobased polymers made by ROMP are nondegradable because of their all-carbon backbones. Herein, we present a modular synthetic strategy to acid-degradable poly(enol ether)s via ring-opening metathesis copolymerization of biorenewable oxanorbornenes and 3,4-dihydropyran (DHP). 1H NMR analysis reveals that the percentage of DHP units in the resulting copolymers gradually increases as the feed ratio of DHP to oxanorbornene increases. The composition of the copolymers plays a pivotal role in governing their thermal properties. Thermogravimetric analysis shows that an increasing percentage of DHP results in a decrease in the decomposition temperatures, suggesting that the incorporation of enol ether groups in the polymer backbone reduces the thermal stability of the copolymers. Moreover, a wide range of glass transition temperatures (16–165 °C) can be achieved by tuning the copolymer composition and the oxanorbornene structure. Critically, all of the poly(enol ether)s developed in this study are degradable under mildly acidic conditions. A higher incorporation of DHP in the copolymer leads to enhanced degradability, as evidenced by smaller final degradation products. Altogether, this study provides a facile approach for synthesizing biorenewable and degradable polymer materials with highly tunable thermal properties desired for their potential industrial applications. 

Degradable polymers made via ring-opening metathesis polymerization (ROMP) hold tremendous promise as eco-friendly materials. However, most of the ROMP monomers are derived from petroleum resources, which are typically considered less sustainable compared to biomass. Herein, we present a synthetic strategy to degradable polymers by harnessing alternating ROMP of biomass-based cyclic olefin monomers including exo-oxanorbornenes and cyclic enol ethers. A library of well-defined poly(enol ether)s with modular structures, tunable glass transition temperatures, and controlled molecular weights was achieved, demonstrating the versatility of this approach. Most importantly, the resulting copolymers exhibit high degrees of alternation, rendering their backbones fully degradable under acidic conditions. 

The current insufficient recycling of commodity polymer waste has resulted in pressing environmental and human health issues in our modern society. In the quest for next-generation polymer materials, chemists have recently shifted their attention to the design of chemically recyclable polymers that can undergo depolymerization to regenerate monomers under mild conditions. During the past decade, ring-closing metathesis reactions have been demonstrated to be a robust approach for the depolymerization of polyolefins, producing low-strain cyclic alkene products which can be repolymerized back to new batches of polymers. In this review, we aim to highlight the recent advances in chemical recycling of polyolefins enabled by ring-closing metathesis depolymerization (RCMD). A library of depolymerizable polyolefins will be covered based on the ring size of their monomers or depolymerization products, including five-membered, six-membered, eight-membered, and macrocyclic rings. Moreover, current limitations, potential applications, and future opportunities of the RCMD approach will be discussed. It is clear from recent research in this field that RCMD represents a powerful strategy towards closed-loop chemical recycling of novel polyolefin materials. 

Bottlebrush polymers, macromolecules consisting of dense polymer side chains grafted from a central polymer backbone, have unique properties resulting from this well-defined molecular architecture. With the advent of controlled radical polymerization techniques, access to these architectures has become more readily available. However, synthetic challenges remain, including the need for intermediate purification, the use of toxic solvents, and challenges with achieving long bottlebrush architectures due to backbone entanglements. Herein, we report hybrid bonding bottlebrush polymers (systems integrating covalent and noncovalent bonding of structural units) consisting of poly(sodium 4-styrenesulfonate) (p(NaSS)) brushes grafted from a peptide amphiphile (PA) supramolecular polymer backbone. This was achieved using photoinitiated electron/energy transfer-reversible addition–fragmentation chain transfer (PET-RAFT) polymerization in water. The structure of the hybrid bonding bottlebrush architecture was characterized using cryogenic transmission electron microscopy, and its properties were probed using rheological measurements. We observed that hybrid bonding bottlebrush polymers were able to organize into block architectures containing domains with high brush grafting density and others with no observable brushes. This finding is possibly a result of dynamic behavior unique to supramolecular polymer backbones, enabling molecular exchange or translational diffusion of monomers along the length of the assemblies. The hybrid bottlebrush polymers exhibited higher solution viscosity at moderate shear, protected supramolecular polymer backbones from disassembly at high shear, and supported self-healing capabilities, depending on grafting densities. Our results demonstrate an opportunity for novel properties in easily synthesized bottlebrush polymer architectures built with supramolecular polymers that might be useful in biomedical applications or for aqueous lubrication. 

In order to evaluate urban earthquake resilience, reliable structural modeling is needed. However, detailed modeling of a large number of structures and carrying out time history analyses for sets of ground motions are not practical at an urban scale. Reduced-order surrogate models can expedite numerical simulations while maintaining necessary engineering accuracy. Neural networks have been shown to be a powerful tool for developing surrogate models, which often outperform classical surrogate models in terms of scalability of complex models. Training a reliable deep learning model, however, requires an immense amount of data that contain a rich input-output relationship, which typically cannot be satisfied in practical applications. In this paper, we propose model-informed symbolic neural networks (MiSNN) that can discover the underlying closed-form formulations (differential equations) for a reduced-order surrogate model. The MiSNN will be trained on datasets obtained from dynamic analyses of detailed reinforced concrete special moment frames designed for San Francisco, California, subject to a series of selected ground motions. Training the MiSNN is equivalent to finding the solution to a sparse optimization problem, which is solved by the Adam optimizer. The earthquake ground acceleration and story displacement, velocity, and acceleration time histories will be used to train 1) an integrated SNN, which takes displacement and velocity states and outputs the absolute acceleration response of the structure; and 2) a distributed SNN, which distills the underlying equation of motion for each story. The results show that the MiSNN can reduce computational cost while maintaining high prediction accuracy of building responses.