Scientific Reports,
Journal Year:
2024,
Volume and Issue:
14(1)
Published: Oct. 14, 2024
Energy
harvesters
based
on
nanomaterials
are
getting
more
and
popular,
but
their
way
to
commercial
availability,
some
crucial
issues
still
need
be
solved.
The
objective
of
the
study
is
select
an
appropriate
nanomaterial.
Using
features
Reinforcement
Deep
Q-Network
(DQN)
in
conjunction
with
Fuzzy
PROMETHEE,
proposed
model,
we
present
this
work
a
hybrid
fuzzy
approach
selecting
materials
for
vehicle-environmental-hazardous
substance
(EHS)
combination
that
operates
roadways
under
traffic
conditions.
DQN
able
accumulate
useful
experience
operating
dynamic
environment,
accordingly
deliver
highest
energy
output
at
same
time
bring
consideration
factors
such
as
durability,
cost,
environmental
impact.
PROMETHEE
allows
participation
human
experts
during
decision-making
process,
going
beyond
quantitative
data
typically
learned
by
through
inclusion
qualitative
preferences.
Instead,
method
unites
strength
individual
approaches,
result
providing
highly
resistant
adjustable
material
selection
real
EHS.
pointed
out
can
give
high
efficiency
reference
years
service,
price,
effects.
model
provides
95%
accuracy
computational
300
s,
application
hypothesis
practical
testing
chosen
showed
selected
harvest
fluctuating
conditions
proved
concept
True
Vehicle
Environmental
High-risk
Substance
scenarios.
Small,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Aug. 16, 2024
Abstract
In
deserts,
sedimentation
from
frequent
dust
activities
on
solar
cells
poses
a
substantial
technical
challenge,
reducing
efficiency
and
necessitating
advanced
cost‐inefficient
cleaning
mechanisms.
Herein,
novel
sandfish
scale‐inspired
self‐healing
fluorinated
copolymer‐based
triboelectric
layer
is
directly
incorporated
top
of
the
polysilicon
cell
for
sustained
hybrid
energy
harvesting.
The
transparent
biomimetic
layer,
with
distinctive
saw‐tooth
microstructured
morphology,
exhibits
ultra‐low
sand
adhesion
high
abrasion‐resistant
properties,
inhibits
deposition
cells,
concurrently
harvests
kinetic
wind‐driven
particles
through
nanogenerator
(TENG).
film
low
friction
coefficient
(0.149),
minimal
force
(27
nN),
small
wear
area
(327
µm
2
).
addition,
over
months,
structure
demonstrates
only
16%
decline
in
maximum
power
output
compared
to
bare
cell,
which
experiences
60%
decline.
Further,
scale‐based
TENG
device's
electrical
fully
restored
its
original
value
after
6‐h
cycle
maintains
consistent
stable
outputs.
These
results
highlight
exceptional
advantages
employing
materials
as
robust
layers,
showcasing
device
stability
durability
prolonged
use
harsh
desert
environments,
ultimately
contributing
cost‐of‐electricity
generation
paradigm.
ACS Sensors,
Journal Year:
2024,
Volume and Issue:
9(11), P. 5945 - 5954
Published: Nov. 7, 2024
Since
each
material
has
a
unique
ability
to
lose
or
obtain
electrons,
specific
triboelectric
signals
are
produced
when
materials
in
contact
with
different
objects.
Triboelectric
nanogenerator
(TENG)
devices
show
great
potential
for
use
as
tactile
sensors;
nevertheless,
analyzing
the
structure-function
relationship
of
functionalized
sensing
interfaces
under
environmental
conditions
and
improving
stability
accuracy
through
design
hydrophobic
structure
on
surface
remain
major
challenges
development
intelligent
networks.
Compared
traditional
rigid
micronanostructure,
elastic
micronanostructure
strategy
is
applied
achieve
both
hydrophobicity
based
template
method
this
work.
The
corresponding
roughness
angle
89.9
nm
117.9°,
respectively.
As
expected,
output
voltage
charge
density
enhanced
by
almost
65.8
33.4%,
respectively,
establishment
an
surface.
More
importantly,
signal
waveforms
also
present
acceptable
durability
subsequent
recognition
after
immersion
water
ethanol
12
days
metal
impact
000
cycles.
Hence,
combined
deep
machine
learning
effect,
perception
system
integrated
moisture-resistant
TENG-based
sensor
fatigue
testing,
data
processing,
display
modules
developed
real-time
monitoring
approximately
100%
(mask),
76%
(plank),
93%
(plastic),
89%
(rubber)
identification
accuracies
natural
environment.
Finally,
proposed
broad
application
field
human-computer
interaction.
Chemical Science,
Journal Year:
2024,
Volume and Issue:
15(27), P. 10436 - 10447
Published: Jan. 1, 2024
A
high-performance
triboelectric
nanogenerator
with
dual
nanostructure
is
fabricated
and
further
enhanced
by
surface
chemical
modification.
The
signal
used
to
control
an
optocoupler
switch
for
remote
of
a
switching
circuit.
Scientific Reports,
Journal Year:
2024,
Volume and Issue:
14(1)
Published: Oct. 14, 2024
Energy
harvesters
based
on
nanomaterials
are
getting
more
and
popular,
but
their
way
to
commercial
availability,
some
crucial
issues
still
need
be
solved.
The
objective
of
the
study
is
select
an
appropriate
nanomaterial.
Using
features
Reinforcement
Deep
Q-Network
(DQN)
in
conjunction
with
Fuzzy
PROMETHEE,
proposed
model,
we
present
this
work
a
hybrid
fuzzy
approach
selecting
materials
for
vehicle-environmental-hazardous
substance
(EHS)
combination
that
operates
roadways
under
traffic
conditions.
DQN
able
accumulate
useful
experience
operating
dynamic
environment,
accordingly
deliver
highest
energy
output
at
same
time
bring
consideration
factors
such
as
durability,
cost,
environmental
impact.
PROMETHEE
allows
participation
human
experts
during
decision-making
process,
going
beyond
quantitative
data
typically
learned
by
through
inclusion
qualitative
preferences.
Instead,
method
unites
strength
individual
approaches,
result
providing
highly
resistant
adjustable
material
selection
real
EHS.
pointed
out
can
give
high
efficiency
reference
years
service,
price,
effects.
model
provides
95%
accuracy
computational
300
s,
application
hypothesis
practical
testing
chosen
showed
selected
harvest
fluctuating
conditions
proved
concept
True
Vehicle
Environmental
High-risk
Substance
scenarios.
