TY - JOUR
T1 - Statistical modeling enabled design of high-performance conductive composite fiber materials for energy harvesting and self-powered sensing
AU - Yang, Yujue
AU - Xu, Bingang
AU - Li, Meiqi
AU - Gao, Yuanyuan
AU - Han, Jing
N1 - Funding Information:
The authors would like to acknowledge the funding support from the Research Grants Council of the Hong Kong Special Administrative Region, China (project no. PolyU 15209020) for the work reported here.
Publisher Copyright:
© 2023 Elsevier B.V.
PY - 2023/6/15
Y1 - 2023/6/15
N2 - 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.
AB - 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.
KW - Composite fibers
KW - Fiber-based triboelectric nanogenerator
KW - Fractional factorial design
KW - Response surface methodology
KW - Wearable electronics
UR - http://www.scopus.com/inward/record.url?scp=85153608639&partnerID=8YFLogxK
U2 - 10.1016/j.cej.2023.143052
DO - 10.1016/j.cej.2023.143052
M3 - Journal article
AN - SCOPUS:85153608639
SN - 1385-8947
VL - 466
JO - Chemical Engineering Journal
JF - Chemical Engineering Journal
M1 - 143052
ER -