Medical Design Briefs - February 2021 - 34

vices. Micro-supercapacitors have a small
footprint, high power density, and the
ability to charge and discharge quickly.
However, according to Cheng, when fabricated for wearable devices, conventional micro-supercapacitors produce a
" sandwich-like " stacked geometry that
displays poor flexibility, long ion diffusion distances, and a complex integration process when combined with wearable electronics.
This led Cheng and his team to
explore alternative device architectures
and integration processes to advance the
use of micro-supercapacitors in wearable
devices. They found that arranging
micro-supercapacitor cells in a serpentine, island-bridge layout allows the configuration to stretch and bend at the
bridges, while reducing deformation of
the micro-supercapacitors - the islands.
When combined, the structure becomes

what the researchers refer to as microsupercapacitors arrays.
" By using an island-bridge design when
connecting cells, the micro-supercapacitor
arrays displayed increased stretchability
and allowed for adjustable voltage outputs, " Cheng says. " This allows the system
to be reversibly stretched up to 100
percent. "
By using nonlayered, ultrathin zincphosphorus nanosheets and 3D laserinduced graphene foam - a highly
porous, self-heating nanomaterial - to
construct the island-bridge design of the
cells, Cheng and his team saw drastic
improvements in electric conductivity
and the number of absorbed charged
ions. This proved that these microsupercapacitor arrays can charge and
discharge efficiently and store the energy needed to power a wearable device.
The researchers also integrated the system

A self-powered, stretchable system will be used in wearable health-monitoring and diagnostic
devices. (Credit: Penn State College of Engineering)

with a triboelectric nanogenerator, an
emerging technology that converts mechanical movement to electrical energy.
This combination created a self-powered
system.
" When we have this wireless charging
module that's based on the triboelectric
nanogenerator, we can harvest energy
based on motion, such as bending your
elbow or breathing and speaking, "
Cheng says. " We are able to use these
everyday human motions to charge the
micro-supercapacitors. "
By combining this integrated system
with a graphene-based strain sensor, the
energy-storing micro-supercapacitor
arrays - charged by the triboelectric
nanogenerators - are able to power the
sensor, Cheng says, showing the potential for this system to power wearable,
stretchable devices.
Other researchers on this project were
Cheng Zeng, assistant professor; Zhixiang
Peng, research assistant; Chao Xing, associate professor; Huaming Chen, associate
professor; Chunlei Huang, assistant professor, and Jun Wang, professor, all at
Minjiang University; Bingwen Zhang,
assistant professor at the Fujian Provincial
Key Laboratory of Functional Marine
Sensing Materials at Minjiang University;
and Shaolong Tang, professor of physics,
Nanjing University.
The National Natural Science Foundation of China; the Educational Commission of Fujian Province for Youths;
the U.S. National Science Foundation;
the National Heart, Lung, and Blood
Institute of the U.S. National Institutes
of Health supported this work.
This article was written by Tessa M. Pick,
Penn State University. For more information,
visit https://news.psu.edu.

Tapping the Brain to Boost Stroke Rehabilitation
A brain-machine interface
coupled with robot offers
increased benefits for
stroke survivors.
University of Houston
Houston, TX
Stroke survivors who had ceased to
benefit from conventional rehabilitation
gained clinically significant arm movement and control by using an external
robotic device powered by the patients'
own brains.

The results of the clinical trial were
described in the journal NeuroImage:
Clinical.
Jose Luis Contreras-Vidal, director of
the Non-Invasive Brain Machine Interface Systems Laboratory at the
University of Houston, says testing
showed that most patients retained the
benefits for at least two months after the
therapy sessions ended, suggesting the
potential for long-lasting gains. He is
also Hugh Roy and Lillie Cranz Cullen
Distinguished Professor of Electrical and
Computer Engineering.

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The trial involved training stroke survivors with limited movement in one
arm to use a brain-machine interface
(BMI), a computer program that captures brain activity to determine the subject's intentions and then triggers an
exoskeleton, or robotic device affixed to
the affected arm, to move in response to
those intentions. The device wouldn't
move if intention wasn't detected, ensuring that subjects remained engaged in
the exercise.
Using robotics in rehabilitation isn't
new, says Contreras-Vidal, co-principal
Medical Design Briefs, February 2021

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Medical Design Briefs - February 2021

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