Van der Waals interactions enhanced multiple-times all-waste-recycled triboelectric nanogenerator for ultra-high lifetime stability

To mitigate the ever-increasing waste problem, utilization of recycled waste to fabricate energy harvesting devices has emerged as a propitious alternative. Despite several efforts, it is still a daunting challenge to sustain the properties of recycled-plastic-waste after multiple rounds of recycling. We address the above challenges by developing an industry-viable universal framework to recycle plastic-waste multiple-times without significant degradation in its properties. A multiple-times all-recycled triboelectric nanogenerator (MR-TENG) was demonstrated with high performance and record-high overall operational lifetime stability for 5 million cycles of mechanical impact. To the best of our knowledge, this is the first report of a nanogenerator made completely from recycled mixed-plastic-waste, which can sustain its energy-harvesting performance even after ten rounds of recycling. The multiple-times recyclability can be attributed to the improved van der Waals interactions between the polymeric blends at the macromolecular level facilitated by incorporating a mixed-plastic waste-derived compatibilizing agent, which is validated by molecular dynamics simulation. The adopted industry-viable reactive extrusion to fabricate the materials facilitates easy adaptability and commercialization. The practical application was demonstrated by utilizing it for deep learning-enabled MR-TENG-based advanced cognitive waste sorting and segregation, facilitating digitalized-waste-management, thus facilitating a major step towards upcycling of plastic-waste for circular economy. [Display omitted] •Proposed a universal framework to fabricate triboelectric nanogenerator sustaining its performance after multiple rounds of recycling.•An industry-viable solvent-free recycling process that facilitates large-scale manufacturability and commercialization.•An overall operational lifetime stability for more than 5 million cycles of mechanical impact is achieved.•Deep learning-enabled waste sorting and segregation is explored for waste management.