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Convenient strategies for the deconstruction and reprocessing of thermosets could improve the circularity of these materials, but most approaches developed to date do not involve established, high-performance engineering materials. Here, we show that bifunctional silyl ether, i.e., R′O–SiR2–OR′′, (BSE)-based comonomers generate covalent adaptable network analogues of the industrial thermoset polydicyclopentadiene (pDCPD) through a novel BSE exchange process facilitated by the low-cost food-safe catalyst octanoic acid. Experimental studies and density functional theory calculations suggest an exchange mechanism involving silyl ester intermediates with formation rates that strongly depend on the Si–R2 substituents. As a result, pDCPD thermosets manufactured with BSE comonomers display temperature- and time-dependent stress relaxation as a function of their substituents. Moreover, bulk remolding of pDCPD thermosets is enabled for the first time. Altogether, this work presents a new approach toward the installation of exchangeable bonds into commercial thermosets and establishes acid-catalyzed BSE exchange as a versatile addition to the toolbox of dynamic covalent chemistry. 

Thermoset polymers and fiber-reinforced polymer composites possess the chemical, physical, and mechanical properties necessary for energy-efficient vehicles and structures, but their energy-inefficient manufacturing and the lack of end-of-life management strategies render these materials unsustainable. Here, we demonstrate end-of-life deconstruction and upcycling of high-performance poly(dicyclopentadiene) (pDCPD) thermosets with a concurrent reduction in the energy demand for curing via frontal copolymerization. Triggered material deconstruction is achieved through cleavage of cyclic silyl ethers and acetals incorporated into pDCPD thermosets. Both solution-state and bulk experiments reveal that seven- and eight-membered cyclic silyl ethers and eight-membered cyclic acetals are incorporated efficiently with norbornene-derived monomers, permitting deconstruction at low comonomer loadings. Frontal copolymerization of DCPD with these tailored cleavable comonomers enables energy-efficient manufacturing of sustainable, high-performance thermosets with glass transition temperatures of >100 °C and elastic moduli of >1 GPa. The polymers are fully deconstructed, yielding hydroxyl-terminated oligomers that are upcycled to polyurethane-containing thermosets having a higher glass transition temperatures than that of the original polymer upon reaction with diisocyanates. This approach is extended to frontally polymerized fiber-reinforced composites, where large-fiber volume fraction composites (Vf = 65%) containing a cleavable comonomer are deconstructed and the reclaimed fibers are used to regenerate composites via frontal polymerization that display properties nearly identical to those of the original. This work demonstrates that the use of cleavable monomers, in combination with frontal manufacturing, provides a promising strategy to address sustainability challenges for high-performance materials at multiple stages of their lifecycle. 

As a machine-recognizable representation of polymer connectivity, BigSMILES line notation extends SMILES from deterministic to stochastic structures. The same framework that allows BigSMILES to accommodate stochastic covalent connectivity can be extended to non-covalent bonds, enhancing its value for polymers, supramolecular materials, and colloidal chemistry. Non-covalent bonds are captured through the inclusion of annotations to pseudo atoms serving as complementary binding pairs, minimal key/value pairs to elaborate other relevant attributes, and indexes to specify the pairing among potential donors and acceptors or bond delocalization. Incorporating these annotations into BigSMILES line notation enables the representation of four common classes of non-covalent bonds in polymer science: electrostatic interactions, hydrogen bonding, metal–ligand complexation, and π–π stacking. The principal advantage of non-covalent BigSMILES is the ability to accommodate a broad variety of non-covalent chemistry with a simple user-orientated, semi-flexible annotation formalism. This goal is achieved by encoding a universal but non-exhaustive representation of non-covalent or stochastic bonding patterns through syntax for (de)protonated and delocalized state of bonding as well as nested bonds for correlated bonding and multi-component mixture. By allowing user-defined descriptors in the annotation expression, further applications in data-driven research can be envisioned to represent chemical structures in many other fields, including polymer nanocomposite and surface chemistry.