Experimental Development of Self-Healing Polymer Networks Supported by DFT Calculations

Intrinsic self-healing reversible polymer networks based on thermoreversible Diels–Alder cycloadditions represent an important class of sustainable materials. While the furan-maleimide Diels–Alder system has been extensively studied, its relatively slow reaction kinetics under ambient conditions restricts broader applications. In this work, we present a methodology for developing dynamic polymer networks using alternative diene–dienophile combinations guided by density functional theory (DFT) calculations. This approach aims to obtain materials with enhanced reaction kinetics while maintaining thermal reversibility, with the flexibility to tailor these properties for specific applications. As a case study, fulvenes and electron-deficient cyanoalkenes were identified as promising diene–dienophile pairs through DFT analysis. Multifunctional compounds containing these moieties were synthesized and thoroughly characterized. Dynamic polymer networks were subsequently prepared using three combinations: fulvene moieties paired with maleimide counterparts, cyanoalkene moieties paired with furan counterparts, and cyanoalkene moieties paired with fulvene counterparts. Characterization through TGA, DSC, dynamic rheometry, and NMR techniques revealed properties consistent with DFT predictions (e.g., increased reaction rates), though challenges such as competing side reactions and stability issues were noted. Overall, our findings highlight the potential of alternative Diels–Alder chemistries for advancing self-healing polymer networks. Furthermore, this study demonstrates the value of integrating predictive techniques with experimental synthesis and characterization not only to identify innovative Diels–Alder chemistries but also to address critical practical factors beyond reaction rates and reversibility.