Thermoset polymers with permanently cross-linked networks have outstanding mechanical properties and solvent resistance, but they cannot be reprocessed or recycled. On the other hand, vitrimers with covalent adaptable networks can be recycled. Here we provide a simple and practical method coined as "vitrimerization" to convert the permanent cross-linked thermosets into vitrimer polymers without depolymerization. The vitrimerized thermosets exhibit comparable mechanical properties and solvent resistance with the original ones. This method allows recycling and reusing the unrecyclable thermoset polymers with minimum loss in mechanical properties and enables closed-loop recycling of thermosets with the least environmental impact.

Epoxy/anhydride-cured thermosets are widely used in aerospace and wind energy structures. Recycling the enormous amounts of epoxy thermoset waste remains a global challenge. Vitrimerization, a practical, low-cost, eco-friendly, and scalable method, was developed for closed-loop recycling of the cross-linked epoxy by converting the permanent network into a vitrimer-type dynamic network. Here, we study the effect of virgin epoxy network properties on network reforming efficiency and the activation energy of the dynamic network by varying the epoxy/anhydride ratio. The results point out that the permanent network cross-link density and glass transition temperature have a more pronounced effect on vitrimerization efficiency in comparison with the concentration of free hydroxyl groups in the system. The result can be potentially used to better control vitrimerization efficiency in recycling epoxy thermoset polymers and tailor the vitrimerized product properties.

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Service recommendations help travelers locate en route traffic information service of interest in a timely manner. However, recommendations based on simple traffic information, such as the number of requests for the location of a facility, fail to consider an individual's preferences. Most existing work on improving service recommendations has continued to utilize the same ratings and rankings of services without consideration of diverse users' demands. The challenge remains to push forward the modeling of spatiotemporal trajectories to improve service recommendations. In this research, we proposed a new method to address the above challenge. We developed a personalized service-trajectory correlation that could recommend the most appropriate services to users. In addition, we proposed the use of ''congeniality'' probability to measure the service demand similarity of two travelers based on their service-visiting behaviors and preferences. We employed a clustering-based scheme, taking into account the spatiotemporal dimensions to refine the trajectories at each spot where travelers stayed at a certain point in time. Experiments were conducted employing a real global positioning system-based dataset. The test results demonstrated that our proposed approach could reduce the deviation of the trajectory measurement to 10% and enhance the success rates of the service recommendations to 60%.