Structure-Property Relationships in Graphene-Based Strain and Pressure Sensors for Potential Artificial Intelligence Applications.

Journal: Sensors (Basel, Switzerland)
Published Date: Mar 12, 2019

Abstract

Wearable electronic sensing devices are deemed to be a crucial technology of smart personal electronics. Strain and pressure sensors, one of the most popular research directions in recent years, are the key components of smart and flexible electronics. Graphene, as an advanced nanomaterial, exerts pre-eminent characteristics including high electrical conductivity, excellent mechanical properties, and flexibility. The above advantages of graphene provide great potential for applications in mechatronics, robotics, automation, human-machine interaction, etc.: graphene with diverse structures and leverages, strain and pressure sensors with new functionalities. Herein, the recent progress in graphene-based strain and pressure sensors is presented. The sensing materials are classified into four structures including 0D fullerene, 1D fiber, 2D film, and 3D porous structures. Different structures of graphene-based strain and pressure sensors provide various properties and multifunctions in crucial parameters such as sensitivity, linearity, and hysteresis. The recent and potential applications for graphene-based sensors are also discussed, especially in the field of human motion detection. Finally, the perspectives of graphene-based strain and pressure sensors used in human motion detection combined with artificial intelligence are surveyed. Challenges such as the biocompatibility, integration, and additivity of the sensors are discussed as well.

Authors

  • Zewei Luo
    Shanghai Key Laboratory of Multidimensional Information Processing, Department of Electronic Engineering, East China Normal University, Shanghai 200241, China. 52181213021@stu.ecnu.edu.cn.
  • Xiaotong Hu
    Shanghai Key Laboratory of Multidimensional Information Processing, Department of Electronic Engineering, East China Normal University, Shanghai 200241, China. 10172100328@stu.ecnu.edu.cn.
  • Xiyue Tian
    Shanghai Key Laboratory of Multidimensional Information Processing, Department of Electronic Engineering, East China Normal University, Shanghai 200241, China. 51181213032@stu.ecnu.edu.cn.
  • Chen Luo
    Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
  • Hejun Xu
    Shanghai Key Laboratory of Multidimensional Information Processing, Department of Electronic Engineering, East China Normal University, Shanghai 200241, China. 52151213012@stu.ecnu.edu.cn.
  • Quanling Li
    Shanghai Key Laboratory of Multidimensional Information Processing, Department of Electronic Engineering, East China Normal University, Shanghai 200241, China. 51161213021@stu.ecnu.edu.cn.
  • Qianhao Li
    Shanghai Key Laboratory of Multidimensional Information Processing, Department of Electronic Engineering, East China Normal University, Shanghai 200241, China. 51171213058@stu.ecnu.edu.cn.
  • Jian Zhang
    College of Pharmacy, Ningxia Medical University, Yinchuan, NingxiaHui Autonomous Region, China.
  • Fei Qiao
    Department of Electronic Engineering, Tsinghua University, 30 Shuangqing Road, Beijing 100084, China. qiaofei@tsinghua.edu.cn.
  • Xing Wu
  • V E Borisenko
    Belarusian State University of Informatics and Radioelectronics, P. Browka 6, 220013 Minsk, Belarus. borisenko@bsuir.by.
  • Junhao Chu
    Shanghai Key Laboratory of Multidimensional Information Processing, Department of Electronic Engineering, East China Normal University, Shanghai 200241, China. jhchu@mail.sitp.ac.cn.

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