Multifunctional superhydrophobic conductive sponge for real-time monitoring of oil-water separation and amphibious human activity
Yongming Lv,
No information about this author
Zhenming Chu,
No information about this author
Zhiguo Zhang
No information about this author
et al.
Surfaces and Interfaces,
Journal Year:
2025,
Volume and Issue:
unknown, P. 105875 - 105875
Published: Jan. 1, 2025
Language: Английский
Highly sensitive and flexible strain sensors based on electroplating copper/laser-induced graphene composites
Journal of Alloys and Compounds,
Journal Year:
2025,
Volume and Issue:
unknown, P. 179928 - 179928
Published: March 1, 2025
Language: Английский
Advances in Crack-Based Strain Sensors on Stretchable Polymeric Substrates: Crack Mechanisms, Geometrical Factors, and Functional Structures
C.-H. Song,
No information about this author
Haran Lee,
No information about this author
Chan Park
No information about this author
et al.
Polymers,
Journal Year:
2025,
Volume and Issue:
17(7), P. 941 - 941
Published: March 30, 2025
This
review
focuses
on
deepening
the
structural
understanding
of
crack-based
strain
sensors
(CBSS)
stretchable
and
flexible
polymeric
substrates
promoting
sensor
performance
optimization.
CBSS
are
cutting-edge
devices
that
purposely
incorporate
cracks
into
their
functional
elements,
thereby
achieving
high
sensitivity,
wide
working
ranges,
rapid
response
times.
To
optimize
CBSS,
systematic
research
characteristics
is
essential.
comprehensively
analyzes
key
factors
determining
such
as
crack
mechanism,
geometrical
factors,
structures
proposes
optimization
strategies
grounded
in
these
insights.
In
addition,
we
explore
potential
numerical
analysis
machine
learning
to
offer
novel
perspectives
for
Following
this,
introduce
various
applications
CBSS.
Finally,
discuss
current
challenges
future
prospects
research,
providing
a
roadmap
next-generation
technologies.
Language: Английский
High-Sensitivity and Wide-Range Flexible Pressure Sensor Based on Gradient-Wrinkle Structures and AgNW-Coated PDMS
Xiaoran Liu,
No information about this author
Xinyi Wang,
No information about this author
Tao Xue
No information about this author
et al.
Micromachines,
Journal Year:
2025,
Volume and Issue:
16(4), P. 468 - 468
Published: April 15, 2025
Flexible
pressure
sensors
have
garnered
significant
attention
due
to
their
wide
range
of
applications
in
human
motion
monitoring
and
smart
wearable
devices.
However,
the
fabrication
that
offer
both
high
sensitivity
a
detection
remains
challenging
task.
In
this
paper,
we
propose
an
AgNW-coated
PDMS
flexible
piezoresistive
sensor
based
on
gradient-wrinkle
structure.
By
modifying
microstructure
PDMS,
demonstrates
varying
sensitivities
responses
across
different
ranges.
The
wrinkle
contributes
(0.947
kPa−1)
at
low
pressures,
while
film
with
gradient
contact
height
ensures
continuous
change
area
through
gradual
activation
wrinkles,
resulting
(10–50
kPa).
This
paper
also
investigates
state
films
under
pressures
further
elaborate
sensor’s
sensing
mechanism.
excellent
performance
real-time
response
touch
behavior,
joint
motion,
swallowing
behavior
recognition,
grasping
highlights
its
broad
application
prospects
human–computer
interaction,
monitoring,
intelligent
robotics.
Language: Английский
An Overview of Two-Dimensional Nanomaterial—MXene in Energy Storage and Sensing Application
Shuaib Abrar Jalaludeen,
No information about this author
Kishore Kumar Venkatesan,
No information about this author
S. Rafi Ahamed
No information about this author
et al.
Plasmonics,
Journal Year:
2025,
Volume and Issue:
unknown
Published: April 22, 2025
Language: Английский
Data-Driven Strain Sensor Design Based on a Knowledge Graph Framework
Sensors,
Journal Year:
2024,
Volume and Issue:
24(17), P. 5484 - 5484
Published: Aug. 24, 2024
Wearable
flexible
strain
sensors
require
different
performance
depending
on
the
application
scenario.
However,
developing
based
solely
experiments
is
time-consuming
and
often
produces
suboptimal
results.
This
study
utilized
sensor
knowledge
to
reduce
redundancy
explore
designs.
A
framework
combining
graphs
graph
representational
learning
methods
was
proposed
identify
targeted
performance,
decipher
hidden
information,
discover
new
Unlike
process-parameter-based
machine
methods,
it
used
relationship
as
semantic
features
improve
prediction
precision
(up
0.81).
Based
framework,
a
designed
tested,
demonstrating
wide
range
(300%)
closely
matching
predicted
performance.
outperforms
similar
materials.
Overall,
present
work
favorable
design
constraints
paves
way
for
long-awaited
implementation
of
text-mining-based
management
systems,
which
will
facilitate
intelligent
process.
Language: Английский
A high performance capacitive flexible pressure sensor based on three-dimensional porous rGO/PDMS composite
Journal of Materials Science Materials in Electronics,
Journal Year:
2024,
Volume and Issue:
35(35)
Published: Dec. 1, 2024
Language: Английский
Pre-Cracked conductive networks for strain Sensing: Mechanisms, fabrication, properties and applications
Ying Wu,
No information about this author
Yaru Guo,
No information about this author
Tian Zhai
No information about this author
et al.
Composites Part A Applied Science and Manufacturing,
Journal Year:
2024,
Volume and Issue:
unknown, P. 108643 - 108643
Published: Dec. 1, 2024
Language: Английский
Intelligent Song Recognition via a Hollow‐Microstructure‐Based, Ultrasensitive Artificial Eardrum
Advanced Science,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Sept. 20, 2024
Abstract
Artificial
ears
with
intelligence,
which
can
sensitively
detect
sound—a
variant
of
pressure—and
generate
consciousness
and
logical
decision‐making
abilities,
hold
great
promise
to
transform
life.
However,
despite
the
emerging
flexible
sensors
for
sound
detection,
most
success
is
limited
very
simple
phonemes,
such
as
a
couple
letters
or
words,
probably
due
lack
device
sensitivity
capability.
Herein,
construction
ultrasensitive
artificial
eardrums
enabling
intelligent
song
recognition
reported.
This
strategy
employs
novel
geometric
engineering
sensing
units
in
soft
microstructure
array
(to
significantly
reduce
effective
modulus)
along
complex
exploration
leveraging
machine
learning
algorithms.
Unprecedented
pressure
(6.9
×
10
3
kPa
−1
)
demonstrated
sensor
hollow
pyramid
architecture
porous
slants.
The
integrated
exhibits
unparalleled
(exceeding
by
1–2
orders
magnitude
compared
reported
benchmark
samples)
detection
sensitivity,
accurately
identify
100%
(for
training
set)
97.7%
test
database
segments
from
77
songs
varying
language,
style,
singer.
Overall,
results
highlight
outstanding
performance
hollow‐microstructure‐based
sensor,
indicating
its
potential
applications
human–machine
interaction
wearable
acoustical
technologies.
Language: Английский
Labyrinthine Wrinkle‐Patterned Fiber Sensors Based on a 3D Stress Complementary Strategy for Machine Learning‐Enabled Medical Monitoring and Action Recognition
Yongming Lv,
No information about this author
Zhenming Chu,
No information about this author
Desheng Huang
No information about this author
et al.
Small,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Dec. 15, 2024
Fiber
strain
sensors
show
good
application
potential
in
the
field
of
wearable
smart
fabrics
and
equipment
because
their
characteristics
easy
deformation
weaving.
However,
integration
fiber
with
sensitive
response,
stretchability,
effective
practical
remains
a
challenge.
Herein,
this
paper
proposes
new
strategy
based
on
3D
stress
complementation
through
pre-stretching
swelling
processes,
polydimethylsiloxane
(PDMS)/silver
nanoparticle
(AgNPs)/MXene/carbon
nanotubes
(CNTs)
sensor
bilayer
labyrinthian
wrinkles
conductive
network
PU
surface
is
fabricated.
Benefiting
from
wrinkled
structure
synergies
composite
materials,
exhibits
stretchability
(>150%),
high
sensitivity
(maximum
gauge
factor
57896),
ultra-low
detection
limit
(0.1%),
fast
response/recovery
time
(177/188
ms)
long-term
durability.
It
can
be
used
as
Morse
code
issuance
recognition
to
express
patient's
symptoms
feelings.
Further,
enables
comprehensive
human
movement
monitoring
collects
data
different
assistance
machine
learning,
letters/numbers
are
recognized
predicted
an
accuracy
99.17%
99.33%.
Therefore,
shows
generation
flexible
applications
medical
human-computer
interaction.
Language: Английский