Stretchable Capacitive Sensors of Torsion, Strain, and Touch Using Double Helix Liquid Metal Fibers

Abstract

Soft and stretchable sensors have the potential to be incorporated into soft robotics and conformal electronics. Liquid metals represent a promising class of materials for creating these sensors because they can undergo large deformations while retaining electrical continuity. Incorporating liquid metal into hollow elastomeric capillaries results in fibers that can integrate with textiles, comply with complex surfaces, and be mass produced at high speeds. Liquid metal is injected into the core of hollow and extremely stretchable elastomeric fibers and the resulting fibers are intertwined into a helix to fabricate capacitive sensors of torsion, strain, and touch. Twisting or elongating the fibers changes the geometry and, thus, the capacitance between the fibers in a predictable way. These sensors offer a simple mechanism to measure torsion up to 800 rad m−1—two orders of magnitude higher than current torsion sensors. These intertwined fibers can also sense strain capacitively. In a complementary embodiment, the fibers are injected with different lengths of liquid metal to create sensors capable of distinguishing touch along the length of a small bundle of fibers via self‐capacitance. The three capacitive‐based modes of sensing described here may enable new sensing applications that employ the unique attributes of stretchable fibers.

Document Details

Document Type
Pub Defense Publication
Publication Date
Mar 23, 2017
Source ID
10.1002/adfm.201605630

Entities

People

  • Christopher B. Cooper
  • Daniel Armstrong
  • Jan Genzer
  • Kuralamudhan Arutselvan
  • Michael D. Dickey
  • Mohammad Rashed Khan
  • Yiliang Lin
  • Ying Liu

Organizations

  • North Carolina State University
  • United States Army Natick Soldier Research, Development and Engineering Center

Tags

Readers

  • Electrical Engineering
  • Nanocomposite Materials Science
  • Optical Fiber Sensing and Electromagnetic Propagation.

Technology Areas

  • AI & ML
  • Autonomy
  • Microelectronics
  • Microelectronics - Microelectromechanical Systems