4.8 Article

Three-dimensional printing of a tunable graphene-based elastomer for strain sensors with ultrahigh sensitivity

Journal

CARBON
Volume 143, Issue -, Pages 63-72

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.carbon.2018.11.008

Keywords

-

Funding

  1. National Key Research and Development Program of China [2017YFB0703200]
  2. National Natural Science Foundation of China [51772310]
  3. CAS Pioneer Hundred Talents Program
  4. Shanghai Pujiang Program [17PJ1410100]
  5. CAST [2017QNRC001]
  6. Shanghai Institute of Ceramics Innovative Funding

Ask authors/readers for more resources

A graphene-based elastomer for sensors with tunable and high sensitivity was fabricated by using three-dimensional printing, in which a printable ink was developed by homogenizing graphene and polydimethylsiloxane (PDMS). To make the elastomer tunable and highly sensitive, different microstructures of three-dimensional graphene-PDMS (3DGP) can be formed. Attributed to its well-interconnected scaffolds and designed microstructures, 3DGP demonstrates a series of multifunctional properties, such as excellent stability and a large gauge factor (up to 448 at 30% strain). 3DGP has continuously stable piezoresistive behavior, even after 100 compress-release cycles under 10% strain. By considering the essential properties of 3DGP scaffolds, such as filament diameter, interaxial angle and interlayer space, the printed 3DGP structure can be tunable and highly sensitive. The controllable design and scalable fabrication of the 3DGP advanced functional material suggests that tunable strain sensors and wearable devices have great potential for different applications, which is a finding that can be referenced by future studies on 3D graphene-based sensors. (C) 2018 Elsevier Ltd. All rights reserved.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.8
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available