Statistical modeling enabled design of high-performance conductive composite fiber materials for energy harvesting and self-powered sensing

Abstract

Fiber-based triboelectric nanogenerators (TENGs) composed of conductive electrode and triboelectric materials are capable of converting mechanical energy into electricity. Core-spun yarn provides a unique structure for preparing composite fibers to resolve the problem of interfacial failure between conductive layer and triboelectric layer for fiber-based TENGs. However, the underlying relationship between interactive parameters and its performance has not been understood that hinders the further developments. Herein, a statistical modeling enabled design was developed for high-performance conductive composite fiber (CCF) for energy harvesting and self-powered sensing. Interactive preparation parameters of CCF-based triboelectric nanogenerator (CCF-TENG) was studied via a statistical strategy combing fractional factorial design (FFD) and response surface methodology (RSM) for exploring the underlying relationships between parameters and properties of CCF-TENG. The two methods could effectively reduce the number of experiments and optimize the preparation parameters, facilitating the design of high-performance CCF-TENGs. The optimized CCF-TENGs could not only be applied to drive LEDs and calculator, but also attached on human body as wearable sensors and even connected with a Bluetooth system for wireless monitoring. Our proposed statistical modeling enabled strategy provide new insights in exploring quantitative underlying relationships for advanced applications of functional materials with optimized performance and desired functionalities.