Scalable spinning, winding, and knitting graphene textile TENG for energy harvesting and human motion recognition

Abstract

Textile electronics have attracted great attentions due to their promising applications with endowed capacity of information collection/storage/identification/detection/display. Paired consecutive, scalable, and mass-productive preparation process is critical to be developed for textile energy/sensory electronics with artificial intelligence. Here, we develop a consecutive and scalable process of spinning, roll-to-roll dip-coating, multiaxial winding, and machine knitting for preparing graphene textile triboelectric nanogenerators (TENGs) for energy harvesting and machine-learning assisted human motion monitoring. The graphene textile TENGs have shown high flexibility, shape adaptability, structural integrity, cyclic washability, and superior mechanical stability. Based on the 3D cardigan stitch knitting fashion, the graphene textile TENG shows a maximum peak power of 3.6 μW with an average output power of 0.48 μW, which is capable of powering portable electronics. The self-powered sensing performance of textile TENGs has also been characterized according to the stretching ratio (or external strain). Furthermore, this research uses machine learning algorithms for the analysis of the sensing signals to assist human motion monitoring. The demonstrated graphene-yarn based textile TENGs provide an efficient method to harvesting biomechanical energy and monitoring/distinguishing multiple human motions, which offer an excellent wearable digital platform/system for potential motion capture/monitoring, identification, and smart-sports related applications.