Flexible
devices
assembled
with
low-surface-energy
PDMS
substrates
often
face
challenges,
such
as
poor
interfacial
adhesion
among
multilayer
films
and
mismatched
mechanical
moduli,
complicating
the
development
of
stable
repeatable
pressure
sensors.
Herein,
a
internal
dynamic
cross-linking
ability
is
synthesized
to
alleviate
these
issues,
which
shows
good
tensile
properties,
flexibility,
self-healing
at
room
temperature.
Taking
advantage
material
homogeneity,
electrodes
sensing
layer
sensor
made
composite
ink
PDMS,
serving
additive,
have
strong
peeling
resistance
adhesion.
Furthermore,
microconvex
structures
formed
by
pressing
microstructural
template
effectively
improves
sensitivity
range
sensor.
This
flexible
can
be
applied
in
medical
health
monitoring,
classifying
symptoms
related
sleep
apnea-hypopnea
syndrome
through
machine
learning.
The
constructed
neural
network
accurately
learns
conditions,
including
normal
sleep,
tachycardia,
apnea,
talking,
snoring,
rapid
eye
movement.
Utilizing
homogeneity
materials,
this
design
aims
improve
stability
devices,
expanding
their
application
potential
for
long-term
reliable
use.
Inorganic Chemistry,
Год журнала:
2024,
Номер
63(2), С. 1328 - 1336
Опубликована: Янв. 3, 2024
Designing
friction
materials
with
high
electron
storage
capacity,
work
function,
low
cost,
and
stability
is
an
important
method
to
improve
the
output
performance
of
a
triboelectric
nanogenerator
(TENG).
Here,
we
report
two
kinds
based
on
Keggin-type
polyoxometalates
(POMs)-modified
graphite
carbon
nitride
(g-C3N4),
namely,
g-C3N4@PMo12
g-C3N4@PW12,
form
TENG
commercial
indium
tin
oxide/poly(ethylene
terephthalate)
(ITO/PET)
electrodes.
The
test
shows
that
device
exhibits
voltage
about
78
V,
current
657
nA,
transfer
charge
15
nC,
which
more
than
3
times
higher
unmodified
TENG.
This
improvement
attributed
fact
POM
loaded
surface
g-C3N4
can
be
used
as
shallow
trap
increase
capacity
through
interaction
density
material
by
increasing
function
composite.
not
only
broadens
choices
but
also
offers
practical
means
enhancing
TENG's
performance.
Flexible
sensors
are
widely
applied
in
the
fields
of
electronic
skins
and
wearable
devices,
yet
it
is
still
a
big
challenge
to
effectively
prolong
lifespan
damaged
reduce
environmental
pollution
caused
by
discarded
after
updating
upgrading.
Herein,
we
proposed
self-healing,
degradable,
biobased
polyurethane
elastomer
for
high-performance
flexible
pressure
sensors.
The
synthesized
using
fatty
diamine
as
chain
extender
possessed
high
tensile
strength
13.25
MPa
an
elongation
at
break
830%,
self-healing
efficiency
reached
up
109.2%.
Additionally,
could
be
fully
degraded
within
7
days
1
mol
L-1
NaOH
solution
with
assistance
ethanol.
elastomer-based
sensor
hump-like
microstructure
was
fabricated
reduced
graphene
oxide
conductive
material
via
simple
template
method.
showed
sensitivity
9.448
kPa-1,
large
sensing
range
0-300
kPa,
short
response/recovery
time
40/80
ms,
good
stability
14,000
cycles.
Moreover,
utilized
monitor
different
human
motions,
including
muscle
contraction,
joint
bending,
swallowing,
voice
recognition,
pulse
beat.
Importantly,
even
being
severely
damaged,
able
recover
its
function
detecting
motions.
findings
this
research
provide
strategy
sustainable
development
environmentally
friendly
functional
elastomers
Flexible
devices
assembled
with
low-surface-energy
PDMS
substrates
often
face
challenges,
such
as
poor
interfacial
adhesion
among
multilayer
films
and
mismatched
mechanical
moduli,
complicating
the
development
of
stable
repeatable
pressure
sensors.
Herein,
a
internal
dynamic
cross-linking
ability
is
synthesized
to
alleviate
these
issues,
which
shows
good
tensile
properties,
flexibility,
self-healing
at
room
temperature.
Taking
advantage
material
homogeneity,
electrodes
sensing
layer
sensor
made
composite
ink
PDMS,
serving
additive,
have
strong
peeling
resistance
adhesion.
Furthermore,
microconvex
structures
formed
by
pressing
microstructural
template
effectively
improves
sensitivity
range
sensor.
This
flexible
can
be
applied
in
medical
health
monitoring,
classifying
symptoms
related
sleep
apnea-hypopnea
syndrome
through
machine
learning.
The
constructed
neural
network
accurately
learns
conditions,
including
normal
sleep,
tachycardia,
apnea,
talking,
snoring,
rapid
eye
movement.
Utilizing
homogeneity
materials,
this
design
aims
improve
stability
devices,
expanding
their
application
potential
for
long-term
reliable
use.
