Chitosan biopolymer‐derived self‐powered triboelectric sensor with optimized performance through molecular surface engineering and data‐driven learning

Abstract

AbstractThe state‐of‐the‐art triboelectric nanogenerators (TENGs) are constructed with synthetic polymers, curtailing their application prospects and relevance in sustainable technologies. The economically viable transformation and engineering of naturally abundant materials into efficient TENGs for mechanical energy harvesting is meaningful not only for fundamental scientific exploration, but also for addressing societal needs. Being an abundant natural biopolymer, chitosan enables exciting opportunity for low‐cost, biodegradable TENG applications. However, the electrical outputs of chitosan‐based TENGs are low compared with the devices built with synthetic polymers. Here, we explore the facile molecular surface engineering in chitosan to significantly boost the performance of chitosan‐based TENG for enabling the practical applications, for example, self‐powered car speed sensor. The molecular surface engineering offers a potentially promising scheme for designing and implementing high‐performance biopolymer‐based TENGs for self‐powered nanosystems in sustainable technologies. We further explore for the first time the feasibility of data mining approaches to analyze and learn the acquired triboelectric signals from the car speed sensors and predict the relationship between the triboelectric signals and car speed values.