Self-Healing, Reconfigurable, Thermal-Switching, Transformative Electronics for Health Monitoring.

Journal: Advanced materials (Deerfield Beach, Fla.)
Published Date:

Abstract

Soft, deformable electronic devices provide the means to monitor physiological information and health conditions for disease diagnostics. However, their practical utility is limited due to the lack of intrinsical thermal switching for mechanically transformative adaptability and self-healing capability against mechanical damages. Here, the design concepts, materials and physics, manufacturing approaches, and application opportunities of self-healing, reconfigurable, thermal-switching device platforms based on hyperbranched polymers and biphasic liquid metal are reported. The former provides excellent self-healing performance and unique tunable stiffness and adhesion regulated by temperature for the on-skin switch, whereas the latter results in liquid metal circuits with extreme stretchability (>900%) and high conductivity (3.40 × 10  S cm ), as well as simple recycling capability. Triggered by the increased temperature from the skin surface, a multifunctional device platform can conveniently conform and strongly adhere to the hierarchically textured skin surface for non-invasive, continuous, comfortable health monitoring. Additionally, the self-healing and adhesive characteristics allow multiple multifunctional circuit components to assemble and completely wrap on 3D curvilinear surfaces. Together, the design, manufacturing, and proof-of-concept demonstration of the self-healing, transformative, and self-assembled electronics open up new opportunities for robust soft deformable devices, smart robotics, prosthetics, and Internet-of-Things, and human-machine interfaces on irregular surfaces.

Authors

  • Li Yang
    Department of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
  • Zihan Wang
    Graduate School, Beijing University of Chinese Medicine, Beijing, China.
  • Hao Wang
    Department of Cardiology, Second Medical Center, Chinese PLA General Hospital, Beijing, China.
  • Biqiang Jin
    State Key Laboratory of Polymer Material Engineering, College of Polymer Science and Engineering, Sichuan University, Chengdu, 610065, China.
  • Chuizhou Meng
    State Key Laboratory for Reliability and Intelligence of Electrical Equipment, Hebei Key Laboratory of Smart Sensing and Human-Robot Interaction, School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300401, China.
  • Xue Chen
    Department of Orthopedics, The Second Hospital of Jilin University, Changchun 130041, China.
  • Runze Li
    Department of Statistics, The Pennsylvania State University, University Park, PA 16802, United States. Electronic address: rzli@psu.edu.
  • He Wang
    Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, China International Neuroscience Institute, Beijing, China.
  • Mingyang Xin
    State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin, 300130, China.
  • Zeshang Zhao
    State Key Laboratory for Reliability and Intelligence of Electrical Equipment, Hebei Key Laboratory of Smart Sensing and Human-Robot Interaction, School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300401, China.
  • Shijie Guo
  • Jinrong Wu
    State Key Laboratory of Polymer Material Engineering, College of Polymer Science and Engineering, Sichuan University, Chengdu, 610065, China.
  • Huanyu Cheng
    Department of Engineering Science and Mechanics, The Pennsylvania State University, University Park, PA, 16802, USA.