All-natural phyllosilicate-polysaccharide triboelectric sensor for machine learning-assisted human motion prediction

Abstract

The rapid development of smart and carbon-neutral cities motivates the potential of natural materials for triboelectric electronics. However, the relatively deficient charge density makes it challenging to achieve high Maxwell’s displacement current. Here, we propose a methodology for improving the triboelectricity of marine polysaccharide by incorporating charged phyllosilicate nanosheets. As a proof-of-concept, a flexible, flame-retardant, and eco-friendly triboelectric sensor is developed based on all-natural composite paper from alginate fibers and vermiculite nanosheets. The interlaced fibers and nanosheets not only enable superior electrical output but also give rise to wear resistance and mechanical stability. The fabricated triboelectric sensor successfully monitors slight motion signals from various joints of human body. Moreover, an effective machine-learning model is developed for human motion identification and prediction with accuracy of 96.2% and 99.8%, respectively. This work offers a promising strategy for improving the triboelectricity of organo-substrates and enables implementation of self-powered and intelligent platform for emerging applications.