Advanced Functional Materials,
Год журнала:
2024,
Номер
34(37)
Опубликована: Апрель 8, 2024
Abstract
Triboelectric
nanogenerators
(TENG)
not
only
enable
sustainable
self‐powered
sensing
of
devices,
but
also
have
superhuman
noncontact/contact
identification
capabilities,
which
are
propelling
humanity
toward
the
intelligent
era.
However,
inherently
low
dielectric
constant
triboelectric
materials
as
well
mechanical
mismatch
between
electrodes
and
severely
limited
their
efficient
stable
output
performance.
Taking
inspiration
from
asymmetric
structure
function
human
skin,
a
novel
single‐electrode
TENG
is
developed,
whose
electrode
layer
integrated
in
Janus
architecture.
By
tuning
balance
gravity
internal
noncovalent
interactions,
gradient
dispersion
carbon
nanotubes
waterborne
polyurethane
networks
can
be
feasibly
achieved,
boost
device
performance
by
reinforcement
both
charge
trapping
capacity
transfer
layer.
As
proof‐of‐concept,
deep
learning
to
realize
evolution
perception
under
noncontact
(motion
prediction)
contact
(material
identification)
modes.
The
bionic
design
strategy
film
offer
valuable
insights
into
improving
durability
TENG.
Additionally,
proximal
prediction
tactile
functions
desirable
attempts
for
future
human‐machine
interfaces.
Abstract
This
review
is
a
critical
analysis
of
the
current
state‐of‐the‐art
in
core
spun
yarn
textile
triboelectric
nanogenerators
(CSY‐T‐TENGs)
for
self‐powered
smart
sensing
applications.
The
rapid
expansion
wireless
communication,
flexible
conductive
materials,
and
wearable
electronics
over
last
ten
years
now
demanding
autonomous
energy,
which
has
created
new
research
space
field
T‐TENGs.
Current
exploring
T‐TENGs
made
from
CSYs
as
stable
reliable
energy
harvesters
devices
modern
IoT
platforms.
CSY‐TENGs
are
emerging
an
important
technology
due
to
its
simple
structure,
low
cost,
excellent
performance
converting
mechanical
into
electrical
ability.
paper
provides
on
progress,
it
analyzes
unique
advantages
conventional
T‐TENGs,
describes
fabrication
techniques
discusses
materials
used
along
with
their
properties
characteristics,
highlights
recent
advancements
integration
self‐excitation
circuits,
charge
storage
IoT‐enabled
applications,
such
environmental
health
monitoring.
In
conclusion,
challenges
future
directions
road
map
optimization,
upscaling,
commercialization
technology.
Advanced Functional Materials,
Год журнала:
2024,
Номер
34(49)
Опубликована: Авг. 8, 2024
Abstract
Recent
developments
in
robotics
increasingly
highlight
the
importance
of
sensing
technology,
especially
tactile
perception,
enabling
robots
to
effectively
engage
with
their
environment
and
interpret
physical
interactions.
Due
power
efficiency
low
cost,
triboelectric
mechanism
has
been
frequently
studied
for
measuring
pressure
identifying
materials
enhance
robot
perception.
Nevertheless,
there
limited
exploration
using
effect
detect
curved
surfaces,
despite
prevalence
daily
lives.
Here,
a
multimodal
sensor
(TMTS)
multilayered
structural
design
is
proposed
recognize
distinct
materials,
curvatures,
simultaneously,
thus
decoupling
different
modalities
enable
more
accurate
detection.
By
attaching
sensors
robotic
fingertips
leveraging
deep
learning
analytics,
quantitative
curvature
measurement
provides
precise
insights
into
an
object's
detailed
geometric
characteristics
rather
than
merely
assessing
its
overall
shape,
hence
achieving
automatic
recognition
12
grasped
objects
99.2%
accuracy.
The
can
be
further
used
accurately
softness
under
touch
gestures
hand,
94.1%
accuracy,
demonstrating
significant
potential
wide‐ranging
applications
future
robotic‐enabled
intelligent
society.
Nanomaterials,
Год журнала:
2024,
Номер
14(2), С. 165 - 165
Опубликована: Янв. 12, 2024
The
advancement
of
the
Internet
Things
(IoT)
has
increased
demand
for
large-scale
intelligent
sensing
systems.
periodic
replacement
power
sources
ubiquitous
systems
leads
to
significant
resource
waste
and
environmental
pollution.
Human
staffing
costs
associated
with
also
increase
economic
burden.
triboelectric
nanogenerators
(TENGs)
provide
both
an
energy
harvesting
scheme
possibility
self-powered
sensing.
Based
on
contact
electrification
from
different
materials,
TENGs
a
rich
material
selection
collect
complex
diverse
data.
As
data
collected
by
become
increasingly
numerous
complex,
approaches
machine
learning
(ML)
deep
(DL)
algorithms
have
been
proposed
efficiently
process
output
signals.
In
this
paper,
latest
advances
in
ML
assisting
solid-solid
TENG
liquid-solid
sensors
are
reviewed
based
sample
size
complexity
pros
cons
various
analyzed
application
scenarios
presented.
prospects
synergizing
hardware
(TENG
sensors)
software
(ML
algorithms)
environment
their
main
challenges
future
developments
discussed.
Advanced Functional Materials,
Год журнала:
2024,
Номер
34(37)
Опубликована: Апрель 8, 2024
Abstract
Triboelectric
nanogenerators
(TENG)
not
only
enable
sustainable
self‐powered
sensing
of
devices,
but
also
have
superhuman
noncontact/contact
identification
capabilities,
which
are
propelling
humanity
toward
the
intelligent
era.
However,
inherently
low
dielectric
constant
triboelectric
materials
as
well
mechanical
mismatch
between
electrodes
and
severely
limited
their
efficient
stable
output
performance.
Taking
inspiration
from
asymmetric
structure
function
human
skin,
a
novel
single‐electrode
TENG
is
developed,
whose
electrode
layer
integrated
in
Janus
architecture.
By
tuning
balance
gravity
internal
noncovalent
interactions,
gradient
dispersion
carbon
nanotubes
waterborne
polyurethane
networks
can
be
feasibly
achieved,
boost
device
performance
by
reinforcement
both
charge
trapping
capacity
transfer
layer.
As
proof‐of‐concept,
deep
learning
to
realize
evolution
perception
under
noncontact
(motion
prediction)
contact
(material
identification)
modes.
The
bionic
design
strategy
film
offer
valuable
insights
into
improving
durability
TENG.
Additionally,
proximal
prediction
tactile
functions
desirable
attempts
for
future
human‐machine
interfaces.
