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Abstract Supramolecular polymer networks contain non-covalent cross-links that enable access to broadly tunable mechanical properties and stimuli-responsive behaviors; the incorporation of multiple unique non-covalent cross-links within such materials further expands their mechanical responses and functionality. To date, however, the design of such materials has been accomplished through discrete combinations of distinct interaction types in series, limiting materials design logic. Here we introduce the concept of leveraging “nested” supramolecular crosslinks, wherein two distinct types of non-covalent interactions exist in parallel, to control bulk material functions. To demonstrate this concept, we use polymer-linked Pd₂L₄metal–organic cage (polyMOC) gels that form hollow metal–organic cage junctions through metal–ligand coordination and can exhibit well-defined host-guest binding within their cavity. In these “nested” supramolecular network junctions, the thermodynamics of host-guest interactions within the junctions affect the metal–ligand interactions that form those junctions, ultimately translating to substantial guest-dependent changes in bulk material properties that could not be achieved in traditional supramolecular networks with multiple interactions in series. 

Abstract Thermoset toughness and deconstructability are often opposing features; simultaneously improving both without sacrificing other mechanical properties (e.g., stiffness and tensile strength) is difficult, but, if achieved, could enhance the usage lifetime and end‐of‐life options for these materials. Here, a strategy that addresses this challenge in the context of photopolymer resins commonly used for 3D printing of glassy, acrylic thermosets is introduced. It is shown that incorporating bis‐acrylate “transferinkers,” which are cross‐linkers capable of undergoing degenerative chain transfer and new strand growth, as additives (5–25 mol%) into homemade or commercially available photopolymer resins leads to photopolymer thermosets with substantially improved tensile toughness and triggered chemical deconstructability with minimal impacts on Young's moduli, tensile strengths, and glass transition temperatures. These properties result from a transferinker‐driven topological transition in network structure from the densely cross‐linked long, heterogeneous primary strands of traditional photopolymer networks to more uniform, star‐like networks with few dangling ends; the latter structure more effectively bear stress yet is also more easily depercolated via solvolysis. Thus, transferinkers represent a simple and effective strategy for improving the mechanical properties of photopolymer thermosets and providing a mechanism for their triggered deconstructability. 

Abstract The advent of covalent adaptable networks (CANs) through the incorporation of dynamic covalent bonds has led to unprecedented properties of macromolecular systems, which can be engineered at the molecular level. Among the various types of stimuli that can be used to trigger chemical changes within polymer networks, light stands out for its remote and spatiotemporal control under ambient conditions. However, most examples of photoactive CANs need to be transparent and they exhibit slow response, side reactions, and limited light penetration. In this vein, it is interesting to understand how molecular engineering of optically active dynamic linkages that offer fast response to visible light can impart “living” characteristics to CANs, especially in opaque systems. Here, the use of carbazole‐based thiuram disulfides (CTDs) that offer dual reactivity as photoactivated reshuffling linkages and iniferters under visible light irradiation is reported. The fast response to visible light activation of the CTDs leads to temporal control of shape manipulation, healing, and chain extension in the polymer networks, despite the lack of optical transparency. This strategy charts a promising avenue for manipulating multifunctional photoactivated CANs in a controlled manner. 

Abstract Polyethylene (PE) is the most widely produced synthetic polymer. By installing chemically cleavable bonds into the backbone of PE, it is possible to produce chemically deconstructable PE derivatives; to date, however, such designs have primarily relied on carbonyl‐ and olefin‐related functional groups. Bifunctional silyl ethers (BSEs; SiR₂(OR′₂)) could expand the functional scope of PE mimics as they possess strong Si−O bonds and facile chemical tunability. Here, we report BSE‐containing high‐density polyethylene (HDPE)‐like materials synthesized through a one‐pot catalytic ring‐opening metathesis polymerization (ROMP) and hydrogenation sequence. The crystallinity of these materials can be adjusted by varying the BSE concentration or the steric bulk of the Si‐substituents, providing handles to control thermomechanical properties. Two methods for chemical recycling of HDPE mimics are introduced, including a circular approach that leverages acid‐catalyzed Si−O bond exchange with 1‐propanol. Additionally, despite the fact that the starting HDPE mimics were synthesized by chain‐growth polymerization (ROMP), we show that it is possible to recover the molar mass and dispersity of recycled HDPE products using step‐growth Si−O bond formation or exchange, generating high molecular weight recycled HDPE products with mechanical properties similar to commercial HDPE. 

Crosslinking DLP 3D printable elastomers using scissile mechanophores increases tear resistance, and enables suturing of the material. 

Advancing mechanoresponsive materials require novel mechanophores, though clear and structured design guidelines are still emerging. In this work, we present a systematic workflow aimed at facilitating the design and discovery of new mechanophores. By integrating the classic iso-metrical CoGEF approach with our innovative iso-tensional Tension Model of Bond Activation (TMBA) simulation, the workflow described herein enables comprehensive evaluation of mechanophore candidates prior to experimental implementation, with a practical case study included for detailed illustration. This predictive capability allows computational screening, efficient identification and filtering away unexpected issues while providing valuable insights for potential structural optimization.