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.

The Electro-Mechanical Energy Harvesting Configurations in Different Modes from Machine to Self-Powered Wearable Electronics

“…These factors influence the repeated patterns of voltage output during the compressive strain provided to the composite. By analyzing the pattern of the voltage output from the compressive sample and their ability to provide similar patterns during repeated cycles, it is possible to predict the deformation of a particular loading sample and the nature of the loading by using machine learning . This information can then be used to design future human-customized energy harvesting samples that are more efficient and effective.…”

Synergizing Machine Learning Algorithm with Triboelectric Nanogenerators for Advanced Self-Powered Sensing Systems

“…In the selection of the KNN algorithms, it is essential to choose an appropriate k-value range. In 2023, Liu et al presented a method to enhance the triboelectric electrification of marine polysaccharides and developed a flexible, flame-retardant, and environmentally friendly TENG sensor [ 130 ]. As shown in Figure 4 c(i), the sensor used alginate fibers and vermiculite (VMT) nanosheets as triboelectric materials to construct a vertical contact–separation mode triboelectric nanogenerator (CS-TENG).…”

“…( b ) Self-powered artificial auditory pathway for sound detection [ 70 ]. ( c ) Phyllosilicate-polysaccharide triboelectric for human motion prediction [ 130 ]. ( d ) The working principle and application of the bioinspired bimodal mechanosensors [ 71 ].…”

Intelligent Gas Detection: g-C3N4/Polypyrrole Decorated Alginate Paper as Smart Selective NH3/NO2 Sensors at Room Temperature

“…First, the PPy exhibits high conductivity and good redox performance, making it a promising component to combine with porous g-CN for constructing heterojunctions with efficient charge separation. Second, the AP derived from nontoxic and degradable seaweed can serve as a promising candidate for the next generation of flexible substrates by replacing conventional synthetic substrates. − The unique surface polarity and versatile design of alginate paper allow it to combine with the g-CN/PPy nanocomposite without phase separation. Third, a matched deep learning model was developed to achieve qualitative recognition and prediction of different types of gases.…”