Enhanced Positive Charge Performance in Triboelectric Series: Molecular Engineering of Conjugated Mesoporous Fibers for Machine Learning-Based Wearable Sensors.

Abstract

With the increasing focus on triboelectric-based sensors, research on synthesizing dielectric layers from specific substances is gradually emerging. Despite numerous negatively-charged triboelectric materials, there is a scarcity of synthesizable positively-charged materials, creating a research gap. This study demonstrates the molecular design of a conjugated, mesoporous, self-assembled sheet via bottom-up synthesis. The synthesized sheet is functionalized to create a triboelectric nanogenerator. Its large specific surface area, softness, and internal space increase the actual contact area and provide adsorption sites for polypyrrole nanoparticles. The incorporation of -COO functional group enhances positive triboelectric performance, forming a dielectric layer with charge-trapping capabilities. When contact with polytetrafluoroethylene (PTFE), this structure boosts the output voltage, showing significant amplification after charge injection with minimal decay. As a demonstration, the bilayer structure is applied as a touchpad on the experimenter's arm to write symbols. The signals are input into an innovative machine-learning model to interpret the writer's intent. Additionally, the device connects to a terminal for real-time medical services, suggesting practical applications for wearable triboelectric sensors with artificial intelligence.