Olefinic polymers form the basis of modern materials infrastructure; however, their low recyclability leads to irreversible environmental accumulation. Covalent adaptable networks (CANs) have emerged as a highly attractive approach to address these challenges by incorporating dynamic covalent bonds that reversibly crosslink, thereby restoring mechanical performance. Furthermore, they impart true pluripotency (the ability to regenerate their pristine characteristics) to olefinic polymers, enabling unimpeded upcycling. This review critically analyzes the design, synthesis, and structure-property relationships of covalent adaptable networks in olefin-based hybrid materials, including transesterification, disulfide metathesis, imine exchange, silyl-ether exchange, olefin metathesis, and related dynamic motifs. The aim of this review is to examine how these approaches are leveraged to deliver advanced materials with functions such as reprocessability, self-healing, weldability, shape memory, improved creep resistance, and mixed-waste compatibilization. Beyond these experimental breakthroughs, this review highlights the expanding intersections between computational modeling and machine-learning algorithms, thereby facilitating the understanding of mechanisms in dynamic bonds, prediction of rheological and thermomechanical properties, and inverse design of vitrimer networks. Molecular-level chemical designs combined with machine-learning algorithms seek to redefine the polyolefin lifecycle.
Kumar et al. (Mon,) studied this question.