Towards Industrially Adoptable Generation Invariant Reprocessable Polydicyclopentadiene Thermoset Plastics

Abstract

The industrial transition to sustainable polymer technologies necessitates novel end-of-life approaches for historically un-recyclable thermoset plastics. Polydicyclopentadiene (pDCPD), a high-performance thermoset known for its superior mechanical and thermal properties represents a compelling target for sustainability-oriented innovation due to its established industrial use, diverse manufacturing methods, historic challenges in reprocessing, and an increased interest from its relevant industries to recover valuable fillers and reinforcing materials from pDCPD carbon-fiber-reinforced polymers (CFRPs). Recent reports exhibit the ability to deconstruct pDCPD through a cleavable comonomer (CC) approach; however, we currently lack cost-effective strategies for scaling its deconstruction and recycling. This thesis addresses the fundamental barriers to industrial implementation of deconstructable pDCPD thermosets through a comprehensive, three-pronged approach that integrates data-driven molecular design, drop-in strategies for multigenerational recyclability, and cost-informed evaluation of CCs. In the first part of this work, a closed-loop experimental–computational platform is developed to predict glass transition temperatures (Tg) in deconstructable pDCPD networks incorporating bifunctional silyl ether (BSE) CCs and cleavable cross-linkers. Leveraging a curated dataset of 101 compositionally diverse pDCPD-based thermosets, machine learning model ensembling and strong regularization techniques are implemented to mitigate overfitting and quantify predictive uncertainty. Experimental validation of model predictions shows that the resulting models achieve accurate Tg predictions for variable CC and cleavable cross-linker loadings, novel CCs, and previously unseen related classes of strand cleaving cross-linkers. This chapter demonstrated the viability of predictive informatics in navigating the vast chemical and compositional space of deconstructable thermosets. The second segment presents a minimally chemically intensive, drop-in strategy for pDCPD recyclability. Using cleavable BSE comonomers and cross-linkers, networks with up to 20 wt% recycled oligomeric fragments are synthesized and evaluated. These materials exhibit thermomechanical properties and deconstructability that remain invariant across three generations of recycling. Furthermore, the incorporation of a cleavable cross-linker, dimethyl di-dicyclopentadiene silyl ether (DDMS), not only preserves but enhances bulk properties such as Tg in virgin and recycled samples, and addresses issues of oligomer incorporation in recycled samples as evidenced by gel fraction analysis. The ability to maintain and tune materials properties without post-processing or structural reformulation underscores the industrial potential of the drop-in CC approach for scalable, circular thermoset manufacturing. The final component of the thesis evaluates MeSi7, a seven-membered BSE CC, as a low-cost, synthetically accessible, and possibly scalable alternative to existing CCs. Thermodynamic polymerization parameters and CC performance under industrial thermoset cure conditions are assessed. We find that high-temperature cure conditions enable sufficient incorporation into the pDCPD network strands for deconstruction with as low as 5 mol% loading of MeSi7. These samples retain Tg values above 100 °C, with a moderate reduction relative to non-deconstructable analogues. Assessment of performance in industrial formulations also shows comparable deconstructability thresholds and modest impact on Tg. Importantly, MeSi7 is projected to cost less than 2% of iPrSi8 based on raw material pricing, offering a highly attractive economic profile for broader market applications. Together, these contributions deliver a framework for the rational design, performance prediction, and techno-economic evaluation of cleavable, recyclable thermosets through a convergence of data science, molecular design, and systems-level engineering considerations.