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Download fileExtremely Stretchable, Stable, and Durable Strain Sensors Based on Double-Network Organogels
journal contribution
posted on 2018-08-29, 00:00 authored by Haoxiang Zhang, Wenbin Niu, Shufen ZhangStretchable strain
sensors offer great potential for diverse applications in modern electronics.
However, it is still difficult to fabricate strain sensors with extreme
stretchability, high stability, and superior durability because of
the challenge in elastic matrix. In this work, the first example of
extremely stretchable and highly stable double-networks ethylene glycol
(EG) organogel is developed for the fabrication of wearable strain
sensors with high performances. It is shown that the formation of
hybrid physically and chemically cross-linked double-networks endows
the EG organogel with an extraordinarily stretchability as high as
21 000%, which is the highest value for gels reported in the
literature. Meanwhile, the low vapor pressure of EG gives the organogel
high ambient stability. Benefiting from the intrinsic stretchability
and stability of EG organogel, the strain sensors are fabricated easily
by incorporating graphene as electrically conductive filler, which
display extremely wide strain-sensing range (>10 500% fracture
strain) with a gauge factor of 2.3. More importantly, the sensor can
withstand >50 000 loading–unloading cycles in air,
exhibiting high stability and superior durability. It is demonstrated
that these sensors can track joint movements and muscle vibrations
(such as human joint motions, drinking, saying, breathing, and slight
cough) of human body and even distinguish the deformations of different
directions and the touches of a hair. This work not only provided
a new elastic matrix platform for the fabrication of extremely stretchable,
stable, and durable strain sensors but also demonstrates their applications
as wearable electronic devices for tracking both large and tiny motions
of human body, which could be further extended to the practical applications
in electronic skin, human–machine interactions, and personalized
health monitoring.