Functional Composites and Structures,
Год журнала:
2024,
Номер
6(4), С. 045008 - 045008
Опубликована: Ноя. 5, 2024
Abstract
Conducting
polymer
and
carbon
nanotube
(CNT)
based
nanocomposites
have
emerged
as
prospective
thermoelectric
(TE)
materials
due
to
their
potential
application
in
flexible
electronics.
Non-conventional
charge
heat
transport
these
nanocomposites,
presents
the
possibility
enhance
TE
conversion
efficiency,
given
by
ZT
.
However,
highly
non-linear
complex
association
of
structure
composition
with
overall
properties
hindered
development
any
general
strategy
develop
high
nanocomposites.
Here,
we
implement
artificial
neural
network
genetic
algorithm
data
driven
models
followed
optimization
design
efficiency
on
CNT
dispersed
polyaniline
(PANI)
matrix.
Our
suggest
that
concentration
plays
most
crucial
role
determining
.
Non-dominated
Pareto
optimal
solutions
consisting
different
combinations
variables
are
obtained
multi-objective
optimization.
Although
a
range
span
over
regions
search
space,
note
longer
CNTs
boost
Seebeck
coefficient
(
S)
electrical
conductivity
(σ),
smaller
length
lowers
thermal
k
),
while
higher
diameter
increase
The
results
provide
guideline
for
developing
CNT-PANI
enhanced
figure
merit.
Advanced Functional Materials,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 17, 2025
Abstract
Ternary
copper
chalcogenide
Cu
7
Sn
3
S
10
has
attracted
great
attention
due
to
its
complex
and
tunable
crystal
structure,
good
thermoelectric
performance,
earth‐abundant
eco‐friendly
elements.
However,
the
optimization
of
performance
in
is
greatly
restricted
because
strategy
element
doping
tune
electrical
thermal
transports
only
limited
by
halogen
Here,
highly
dispersed
multiwalled
carbon
nanotubes
(MCNTs)
are
introduced
into
realize
hybrid
materials
with
enhanced
performance.
A
series
7+
y
/
x
wt.%
MCNTs
successfully
fabricated
ball
milling
combined
spark
plasma
sintering.
The
high‐purity
/MCNTs
identified
as
polymorphs
simultaneously
crystalizing
tetragonal,
primitive
cubic,
face‐centered
cubic
structures.
Such
phase
structures
can
produce
lots
intrinsic
cation‐disorders,
interfaces,
grain
boundaries,
heterointerfaces,
which
strengthen
phonon
carrier
scattering,
while
heterointerfaces
serve
reservoirs
trap
holes
reduce
concentration
toward
optimal
range.
Combining
these
effects,
both
lattice
conductivity
significantly
reduced.
Correspondingly,
a
maximum
figure
merit
zT
0.65
achieved
7.05
/2wt.%
at
750
K,
about
twice
compared
MCNTs‐free
.
This
work
suggests
that
well
enhance
material's
Applied Physics Reviews,
Год журнала:
2025,
Номер
12(1)
Опубликована: Март 1, 2025
This
is
a
review
of
theoretical
and
methodological
development
over
the
past
decade
pertaining
to
computational
characterization
thermoelectric
materials
from
first
principles.
Primary
focus
on
electronic
thermal
transport
in
solids.
Particular
attention
given
relationships
between
various
methods
terms
hierarchy
as
well
tradeoff
physical
accuracy
efficiency
each.
Further
covered
are
up-and-coming
for
modeling
defect
formation
dopability,
keys
realizing
material's
potential.
We
present
discuss
all
these
close
connection
with
parallel
developments
high-throughput
infrastructure
code
implementation
that
enable
large-scale
computing
screening.
In
all,
it
demonstrated
advances
tools
now
ripe
efficient
accurate
targeting
needles
haystack,
which
“next-generation”
materials.
Energies,
Год журнала:
2025,
Номер
18(8), С. 2122 - 2122
Опубликована: Апрель 21, 2025
Thermoelectric
materials
are
functional
that
directly
convert
thermal
energy
into
electrical
or
vice
versa,
and
due
to
their
inherent
properties,
they
hold
significant
potential
in
the
field
of
conversion.
In
this
review,
we
examine
several
fundamental
strategies
aimed
at
enhancing
conversion
efficiency,
classification,
preparation
methods,
applications
thermoelectric
materials.
First,
introduce
an
important
parameter
for
evaluating
performance
materials,
dimensionless
quality
factor
ZT,
present
theory
electroacoustic
transport
which
provides
foundation
Second,
optimizing
carrier
concentration,
band
engineering,
phonon
entropy
engineering
summarized,
emphasizing
presents
numerous
possibilities
material
by
tuning
effective
mass,
convergence,
resonance.
By
analyzing
importance
various
optimization
strategies,
it
is
concluded
co-optimization
primary
method
improving
future.
addition,
overview
currently
available
provided,
including
two
categories,
classical
novel
along
with
a
highlight
techniques.
Finally,
principles
technology
illustrated,
its
fields
discussed,
problems
current
research
analyzed,
future
trends
outlined.
Overall,
paper
comprehensive
summary
classifications,
applications,
offering
valuable
references
insights
researchers
field,
aim
further
advancing
development
science.
Journal of Materials Informatics,
Год журнала:
2025,
Номер
5(3)
Опубликована: Май 28, 2025
The
development
of
advanced
optoelectronic
materials
constitutes
a
pivotal
frontier
in
modern
energy
and
communication
technologies,
facilitating
critical
energy-photon-electron
interconversion
processes
that
underpin
sustainable
infrastructures
high-performance
electronic
devices.
However,
the
discovery
optimization
novel
face
substantial
hurdles
arising
from
complicated
structure-property
interdependencies,
prohibitive
costs,
protracted
innovation
cycles.
Conventional
empirical
approaches
computational
simulations
usually
exhibit
limited
efficacy
addressing
escalating
demands
for
with
superior
stability,
economic
viability,
customizable
properties.
integration
machine
learning
(ML)
high-throughput
screening
has
emerged
as
transformative
strategy
to
address
these
challenges.
By
rapidly
processing
large
multidimensional
datasets
predicting
material
properties
such
structure,
thermodynamic
charge
transport
behaviors,
ML
offers
unprecedented
capabilities
efficient
rational
design
materials.
This
review
provides
comprehensive
overview
cutting-edge
ML-driven
methodologies
emphasis
on
workflows,
data
strategies,
model
frameworks.
We
also
discuss
challenges
prospects
applications,
particularly
standardization,
interpretability
closed-loop
experimental
validation.
further
propose
potential
artificial
intelligence
autonomous
laboratories
build
powerful
pipeline
advance
