Tech Briefs Magazine - April 2022 - 33

complex system of neurons, neural pathways,
and receptors that drive human
perception of sensory stimuli.
While some existing technologies have
used electrical signals to mimic different
levels of pain, these new devices can
react to real mechanical pressure, temperature,
and pain and deliver the right
electronic response. As a result, the artificial
skin knows the difference between
gently touching a pin with your finger or
accidentally stabbing yourself with it.
For more information, contact news@
rmit.edu.au; +61 439 704 077.
Sensors Driven by Machine Learning
Sniff Out Gas Leaks
The system works on large oil and gas infrastructure, helping
to cut methane emissions by 90 percent.
Los Alamos National Laboratory, Los Alamos, NM
A
natural-gas leak-detection tool uses
sensors and machine learning to
locate leak points at oil and gas fields,
promising automatic, affordable sampling
across vast natural gas infrastructure.
The Autonomous, Low-cost, Fast
Leak De tec tion System (ALFaLDS) was
developed to discover accidental releases
of meth ane, a potent greenhouse gas.
ALFaLDS detects, locates, and quantifies
a natural-gas leak based on real-time
methane and ethane (in natural gas)
and atmospheric wind measurements
that are analyzed by a machine-learning
code trained to locate leaks. The code
is trained using high plume dispersion
models and the training is finessed onsite
by controlled releases.
Tests demonstrated that ALFaLDS locates
the engineered methane leaks precisely
and quantifies their size. This novel
capability for locating leaks with high skill,
speed, and accuracy at lower cost promises
affordable sampling of fugitive gas leaks at
well pads and oil and gas fields. ALFaLDS's
success in locating and quantifying fugitive
methane leaks at natural-gas facilities could
lead to a 90 percent reduction in methane
emissions if implemented by the industry.
ALFaLDS uses a small sensor, which
makes it ideal for deployment on cars and
drones. The team is developing the sensors
that were integrated with a mini 3D
sonic anemometer and the powerful machine-learning
code in the tests. The code
is autonomous and can read data from any
gas and wind sensors to help find leaks fast
and minimize fugitive emissions from the
vast network of natural-gas extraction, production,
and consumption.
For more information, contact Charles
Po ling at cpoling@lanl.gov; 505-257-8006.
Ultra-Sensitive, Resilient Sensor for
Smart Textiles
The Slinky-like sensor survives washing machines, cars, and
hammers.
Harvard University, Cambridge, MA
I
f the smart textiles of the future are going
to survive, their components are
going to need to be resilient. Re searchers
have developed an ultra-sensitive, resilient
strain sensor that can be embedded in textiles
and soft robotic systems.
The researchers created a design
that looks and behaves very much like a
Slinky - a solid cylinder of rigid metal
that when patterned into a spiral shape,
becomes stretchable. The researchers
started with a rigid bulk material - in
Tech Briefs, April 2022
this case carbon fiber - and patterned it
in such a way that the material becomes
stretchable. The pattern is known as a serpentine
meander because its sharp ups
and downs resemble the slithering of a
snake. The patterned conductive carbon
fibers are then sandwiched between two
pre-strained elastic substrates.
The overall electrical conductivity of
the sensor changes as the edges of the
patterned carbon fiber come out of
contact with each other, similar to the
www.techbriefs.com
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TB Sensors 0422_1.indd 33
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Free Info at http://info.hotims.com/82320-751
3/17/22 10:02 AM
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Tech Briefs Magazine - April 2022

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