npj Computational Materials,
Год журнала:
2024,
Номер
10(1)
Опубликована: Апрель 16, 2024
Abstract
Polymers
play
an
integral
role
in
various
applications,
from
everyday
use
to
advanced
technologies.
In
the
era
of
machine
learning
(ML),
polymer
informatics
has
become
a
vital
field
for
efficiently
designing
and
developing
polymeric
materials.
However,
focus
predominantly
centered
on
single-component
polymers,
leaving
vast
chemical
space
blends
relatively
unexplored.
This
study
employs
high-throughput
molecular
dynamics
(MD)
simulation
combined
with
active
(AL)
uncover
enhanced
thermal
conductivity
(TC)
compared
constituent
polymers.
Initially,
TC
about
600
amorphous
polymers
200
varying
blending
ratios
are
determined
through
MD
simulations.
The
optimal
representation
method
is
identified,
which
involves
weighted
sum
approach
that
extends
existing
blends.
An
AL
framework,
combining
ML,
employed
explore
approximately
550,000
unlabeled
framework
proves
highly
effective
accelerating
discovery
high-performance
transport.
Additionally,
we
delve
into
relationship
between
TC,
radius
gyration
(
R
g
),
hydrogen
bonding,
highlighting
roles
inter-
intra-chain
interactions
transport
A
significant
positive
association
improvement
indirect
contribution
H-bond
interaction
enhancement
revealed
log-linear
model
odds
ratio
calculation,
emphasizing
impact
increasing
enhancing
blend
TC.
Journal of Materials Chemistry A,
Год журнала:
2023,
Номер
11(10), С. 4850 - 4875
Опубликована: Янв. 1, 2023
Motivated
by
the
advantages
of
AIEgens
in
diversifying
energy
species
and
modulating
transformation,
application
based
on
conversion
solar,
chemical,
mechanical,
electrical
energies
are
summarized.
Renewable Energy,
Год журнала:
2023,
Номер
220, С. 119422 - 119422
Опубликована: Окт. 6, 2023
Photovoltaic
(PV)
module
soiling,
i.e.,
the
accumulation
of
soil
deposits
on
surface
a
PV
module,
directly
affects
amount
solar
energy
received
by
cells
in
that
and
can
also
give
rise
to
additional
heating,
leading
significant
power
generation
losses.
In
this
work,
we
present
results
from
an
extensive
outdoor
experimental
testing
campaign
apply
detailed
characterisation
techniques,
consider
resulting
Soil
sixty
low-iron
glass
coupons
was
collected
at
various
tilt
angles
over
study
period
12
months
capture
monthly,
seasonal
annual
variations.
Transmittance
measurements
showed
horizontal
experienced
highest
degree
soiling.
The
wet-season,
dry-season
full-year
samples
relative
transmittance
decrease
65
%,
68
64
respectively,
which
corresponds
predicted
67
70
66
%
electrical
generation.
An
analysis
soiling
matter
using
X-ray
diffractometer
scanning
electron
microscope
presence
particulate
with
diameters
<10
μm
(PM10),
most
prevalent
studied
region.
npj Computational Materials,
Год журнала:
2024,
Номер
10(1)
Опубликована: Апрель 16, 2024
Abstract
Polymers
play
an
integral
role
in
various
applications,
from
everyday
use
to
advanced
technologies.
In
the
era
of
machine
learning
(ML),
polymer
informatics
has
become
a
vital
field
for
efficiently
designing
and
developing
polymeric
materials.
However,
focus
predominantly
centered
on
single-component
polymers,
leaving
vast
chemical
space
blends
relatively
unexplored.
This
study
employs
high-throughput
molecular
dynamics
(MD)
simulation
combined
with
active
(AL)
uncover
enhanced
thermal
conductivity
(TC)
compared
constituent
polymers.
Initially,
TC
about
600
amorphous
polymers
200
varying
blending
ratios
are
determined
through
MD
simulations.
The
optimal
representation
method
is
identified,
which
involves
weighted
sum
approach
that
extends
existing
blends.
An
AL
framework,
combining
ML,
employed
explore
approximately
550,000
unlabeled
framework
proves
highly
effective
accelerating
discovery
high-performance
transport.
Additionally,
we
delve
into
relationship
between
TC,
radius
gyration
(
R
g
),
hydrogen
bonding,
highlighting
roles
inter-
intra-chain
interactions
transport
A
significant
positive
association
improvement
indirect
contribution
H-bond
interaction
enhancement
revealed
log-linear
model
odds
ratio
calculation,
emphasizing
impact
increasing
enhancing
blend
TC.
