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.
Applied Physics Letters,
Год журнала:
2024,
Номер
125(9)
Опубликована: Авг. 26, 2024
Recently,
quasi-one-dimensional
van
der
Waals
crystal
Ta2PdS6
has
been
reported
as
a
promising
thermoelectric
material
with
an
extraordinarily
high
power
factor.
However,
element
doping
to
tune
the
properties
not
studied
yet.
Here,
we
systematically
investigated
effect
of
Se
on
phase
composition,
charge
transport
and
performance
Se-doped
Ta2Pd(S1−xSex)6
(x
=
0,
0.02,
0.05,
0.07)
polycrystalline
bulk
materials.
Upon
at
S
sites
increase
carrier
concentration
mobility,
electrical
conductivity
Ta2Pd(S0.93Se0.07)6
is
dramatically
enhanced,
while
slightly
reduced,
yielding
significantly
improved
factor
compared
that
pristine
Ta2PdS6.
Consequently,
exhibits
peak
ZT
0.29
700
K
when
content
x
0.07,
which
more
than
twice
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.
