Advancing triboelectric human machine interfaces with core-sheath nanocomposite fibres: Enhanced flexibility and motion identification via machine learning

Abstract

The rapid development of triboelectric nanogenerators (TENGs) has led to sustainable sensing applications as triboelectric interfaces for activity/pattern recognition in human machine interaction (HMI). In this work, core-sheath nanofibres were synthesized and employed as triboelectric materials for constructing new TENGs. These electrospun fibres of novel design consist of a nanocomposite core comprised of polystyrene (PS) and molybdenum disulfide (MoS2) nanoplatelets, and a sheath made of poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP). Compared to high performance TENGs based on neat PVDF-HFP nanofibres tribolayer, these new TENGs achieved open circuit voltage of 1944 V and short-circuit current of 40.3 μA, yielding a total improvement of 284% and 783%, respectively, attributed from the highly efficient charge storage and binding capabilities from the MoS2 nanoplatelets and the PS core. To harness the remarkable performances of these new TENGs, a touch input component was developed, designed to work in conjunction with machine learning algorithms. The triboelectric interfaces exhibited high accuracy in user identification, capable of recognizing individual users with up to 86% average accuracy, under identical press patterns applied by different users. This paves the way for future development of machine learning assisted HMI enabled by TENGs for next generation sustainable communication networks.