All-Fabric Capacitive Pressure Sensors with Piezoelectric Nanofibers for Wearable Electronics and Robotic Sensing.

Journal: ACS applied materials & interfaces
PMID:

Abstract

Flexible pressure sensors are increasingly sought after for applications ranging from physiological signal monitoring to robotic sensing; however, the challenges associated with fabricating highly sensitive, comfortable, and cost-effective sensors remain formidable. This study presents a high-performance, all-fabric capacitive pressure sensor (AFCPS) that incorporates piezoelectric nanofibers. Through the meticulous optimization of conductive fiber electrodes and P(VDF-TrFE) nanofiber dielectric layers, the AFCPS exhibits exceptional attributes such as high sensitivity (4.05 kPa), an ultralow detection limit (0.6 Pa), an extensive detection range (∼100 kPa), rapid response time (<26 ms), and robust stability (>14,000 cycles). The sensor's porous structure enhances its compressibility, while its piezoelectric properties expedite charge separation, thereby increasing the interface capacitance and augmenting overall performance. These features are elucidated further through multiphysical field-coupling simulations and experimental testing. Owing to its comprehensive superior performance, the AFCPS has demonstrated its efficacy in monitoring human activity and physiological signals, as well as in discerning soft robotic grasping movements. Additionally, we have successfully implemented multiple AFCPS units as pressure sensor arrays to ascertain spatial pressure distribution and enable intelligent robotic gripping. Our research underscores the promising potential of the AFCPS device in wearable electronics and robotic sensing, thereby contributing significantly to the advancement of high-performance fabric-based sensors.

Authors

  • Min Su
    Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, P. R. China.
  • Jianting Fu
    Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400700, China.
  • Zixiao Liu
  • Pei Li
    State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Huaxi District, Guiyang 550025, China.
  • Guojun Tai
    Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, P. R. China.
  • Pengsai Wang
    Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, P. R. China.
  • Lei Xie
    Ph.D. Program in Computer Science, The City University of New York, New York, NY, United States.
  • Xueqin Liu
    Chongqing University of Technology, Chongqing 400054, P. R. China.
  • Ximin He
    Department of Material Science and Engineering, University of California, Los Angeles, Los Angeles, CA 90095, USA. ximinhe@ucla.edu.
  • Dapeng Wei
    Chongqing School, University of Chinese Academy of Sciences, Chongqing 400714, People's Republic of China.
  • Jun Yang
    Cardiovascular Endocrinology Laboratory, Hudson Institute of Medical Research, Clayton, Victoria, Australia; Department of Medicine, Monash University, Clayton, Victoria, Australia.