ACS Applied Materials & Interfaces,
Journal Year:
2024,
Volume and Issue:
17(1), P. 1311 - 1321
Published: Dec. 16, 2024
This
study
introduces
a
flexible
and
scalable
charge-trapping
intermediate
layer
of
conjugated
polymeric
film
comprising
[PANI/PEDOT:PSS]n
between
the
[PVA/PDDA]n
triboelectric
graphene-based
[PVA/GNP-PSS]n
electrode
using
layer-by-layer
(LbL)
assembly
method.
By
varying
deposition
layers,
optimal
coating
layout
was
identified
as
2
8
bilayers
respectively.
The
nanogenerator
(TENG)
fabricated
with
this
configuration
achieved
peak
output
voltage
current
180
V
9
μA,
respectively,
at
3
Hz
5
N
against
PDMS.
represents
63.6%
increase
in
20%
compared
to
TENG
without
layer,
owing
surface
charge
density
reaching
61.5
μC/m2.
Furthermore,
an
ultrathin,
free-standing
PANI-PEDOT:PSS
encapsulated
PVA-PDDA
film,
which
resulted
significant
performance
315
V.
Inspired
by
these
results,
we
investigated
flame-retardant
properties
LbL
on
polyurethane
foam
(PUF)
demonstrated,
through
open
flame
test,
that
presence
prevented
flashover,
melting,
dripping
burning
PUF.
coated
PUF
exhibited
lower
heat
release
capacity
402
J/g·K
neat
PUF,
thermal
degradation
formation
10.95
wt
%
residue
TGA
test.
In
addition,
performance.
Therefore,
contributes
future
energy
harvesting
materials
via
sustainable
assembly.
ACS Applied Materials & Interfaces,
Journal Year:
2024,
Volume and Issue:
16(29), P. 38269 - 38282
Published: July 10, 2024
Triboelectric
nanogenerator
(TENG)
has
been
demonstrated
as
a
sustainable
energy
utilization
method
for
waste
mechanical
and
self-powered
system.
However,
the
charge
dissipation
of
frictional
layer
materials
in
humid
environment
severely
limits
their
stable
supply.
In
this
work,
new
is
reported
preparing
polymer
film
hydrophobic
negative
friction
material
by
solution
blending
poly(vinylidene
fluoride-
Langmuir,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Jan. 2, 2025
Triboelectric
nanogenerators
(TENGs)
offer
a
convenient
means
to
convert
mechanical
energy
from
human
movement
into
electricity,
exhibiting
the
application
prospects
in
behavior
monitoring.
Nevertheless,
present
methods
improve
device
monitoring
effect
are
limited
design
of
triboelectric
material
level
(control
electron
gain
and
loss
ability).
As
compared
with
reported
work,
we
TENG-based
tactile
sensors
by
optimizing
structure
electrode/triboelectric
interface
multiple
strains
mechanism.
Cu@Ni
double-clad
waste
woven
fabrics
used
as
electrodes,
which
characterized
large
number
pores
formed
between
fibers,
greatly
increasing
specific
surface
area
electrode
generating
dynamic
strain
under
differentiated
stress
fields
because
their
different
elastic
modulus.
To
be
exact,
resin
layer
undergoes
deformation
0.64-4.47
kPa
external
new
generates
at
induced
slip
4.47-63.84
stress,
resulting
accumulation
charges
on
PDMS
surface.
The
establishment
further
facilitates
generation
distinct
signal
waveforms
that
easily
distinguishable
its
amplitude
peak
form.
Besides,
combined
deep
machine
learning
effect,
an
open
setting,
identification
accuracy
five
behaviors
approaches
100%.
This
provides
pathway
for
enhancing
sensor.
Flexible
noncontact
sensors
are
of
great
significance
in
contemporary
applications.
Nevertheless,
conventional
that
rely
on
metal
electrodes
have
limited
flexibility,
and
their
multilayer
architectures
likely
to
experience
interfacial
delamination
during
extended
use.
To
tackle
these
problems,
we
introduce
an
all-gel-based
flexible
triboelectric
sensor,
which
consists
two
parts,
i.e.,
the
gelatin/poly(vinyl
alcohol)
(PVA)/poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate)
(PEDOT:PSS)/graphene
(G)
electrode
hydrogel
PVA/cellulose/carbon
nanotubes
(CNT)
aerogel.
The
not
only
features
outstanding
biocompatibility
but
also
exhibits
high
conductivity.
aerogel
demonstrates
excellent
mechanical
properties,
such
as
good
elasticity
durability.
This
sensor
can
stably
output
a
current
1.18
μA,
charge
18.4
nC/cm2,
voltage
3.7
V
at
stable
state.
It
accurately
reliably
detect
wide
range
human
motions,
elbow
bending,
knee
movement,
running,
providing
reliable
approach
for
motion
perception
facilitating
progress
relevant
fields.
Triboelectric
nanogenerators
(TENGs)
are
emerging
as
a
sustainable
and
environmentally
friendly
approach
for
energy
harvesting
self-powered
sensing,
because
of
their
diverse
material
options,
simple
structure,
efficient
conversion.
However,
developing
tribopositive
materials
with
both
high-charge-inducing
high-charge-trapping
capabilities
remains
significant
challenge.
Herein,
high-performance
TENG
is
developed
based
on
polyaniline
(PANI)
embedded
polyacrylonitrile
(PAN)
nanofiber
membrane
(NM)
(P/P
NM)
wireless
sensing.
The
incorporation
PANI
significantly
enhanced
the
electrical
performance,
mechanical
properties,
thermal
stability
P/P
NMs.
NM-based
achieved
an
output
voltage
726
V,
short-circuit
current
density
32
μA/cm2,
peak
power
23.3
W/m2,
which
were
approximately
2.3,
3.6,
4.6
times
higher
than
those
pristine
PAN
TENG,
respectively.
Detailed
investigations
revealed
that
improved
electron-donating
ability
dielectric
constant
(by
4.25
times)
NMs,
thereby
boosting
TENG.
was
elucidated
through
capacitor
charging
operation
low-power
devices.
Furthermore,
integrated
into
sensing
system,
enabled
cross-scale
monitoring
human
signals
ranging
from
tiny
pulses
to
large-scale
movements.
introduction
nanofillers
provides
simple,
effective,
scalable
strategy
positive
tribomaterials,
thus,
advancing
practical
application
TENGs
in
Sensors,
Journal Year:
2025,
Volume and Issue:
25(8), P. 2520 - 2520
Published: April 17, 2025
The
integration
of
Deep
Learning
with
sensor
technologies
has
significantly
advanced
the
field
intelligent
sensing
and
decision
making
by
enhancing
perceptual
capabilities
delivering
sophisticated
data
analysis
processing
functionalities.
This
review
provides
a
comprehensive
overview
synergy
between
sensors,
particular
focus
on
applications
triboelectric
nanogenerator
(TENG)-based
self-powered
sensors
combined
artificial
intelligence
(AI)
algorithms.
First,
evolution
is
reviewed,
highlighting
advantages,
limitations,
application
domains
several
classical
models.
Next,
innovative
in
autonomous
driving,
wearable
devices,
Industrial
Internet
Things
(IIoT)
are
discussed,
emphasizing
critical
role
neural
networks
precision
capabilities.
then
delves
into
TENG-based
introducing
their
mechanisms
based
contact
electrification
electrostatic
induction,
material
selection
strategies,
novel
structural
designs,
efficient
energy
conversion
methods.
algorithms
showcased
through
groundbreaking
motion
recognition,
smart
healthcare,
homes,
human–machine
interaction.
Finally,
future
research
directions
outlined,
including
multimodal
fusion,
edge
computing
integration,
brain-inspired
neuromorphic
computing,
to
expand
robotics,
space
exploration,
other
high-tech
fields.
offers
theoretical
technical
insights
collaborative
innovation
technologies,
paving
way
for
development
next-generation
systems.