Polymer Testing,
Journal Year:
2024,
Volume and Issue:
137, P. 108503 - 108503
Published: June 22, 2024
Halloysite
nanotubes
(HNTs)
and
polypropylene-grafted
maleic
anhydride
(PP-g-MA)
were
studied
for
their
effects
in
blends
of
polystyrene
(PS)
polyolefin
elastomer
(POE).
The
method
used
to
prepare
PS/POE
(90/10
80/20
wt/wt)
containing
1,
3,
5
phr
HNTs
with
or
without
PP-g-MA
(a
compatibilizer)
was
melt
blending.
Structural
morphological
studies
using
X-ray
diffraction
analysis
(XRD),
scanning
electron
microscopy
assisted
energy
dispersive
spectroscopy
(SEM-EDS),
transmission
(TEM)
confirmed
a
matrix-droplet
morphology
the
sample
compatibilizer
has
better
microstructure
than
other
formulations.
presence
both
together
been
discovered
improve
viscoelastic
properties
solid,
as
evidenced
by
increased
storage
modulus
complex
viscosity.
A
notable
change
occurred
rheological
behavior
HNTs.
dependence
zero-shear
viscosity
on
loading
(0
phr)
approximated
polynomial
curve
fitting
experimental
data
Carreau-Yasuda
model.
Computational
fluid
dynamics
(CFD)
simulations
also
study
changes
flow
patterns
shear
rates.
calculated
effective
viscosities
at
given
rate
(0.05
1/s)
qualitative
agreement
results.
Moreover,
we
utilized
various
machine-learning
techniques
predict
nanocomposites.
results
showed
that
Extreme
Gradient
Boosting
(XGBoost)
outperformed
predictive
models
based
evaluation
metrics.
Four-point
probe
measurements
found
samples
HNT
had
lowest
conductivities
due
aggregated
structures.
However,
homogeneous
distribution
led
sudden
rise
conductivity
PP-g-MA.
Computer
modeling
uniform
non-uniform
distributions
decreased
considerably
compared
distribution.
Ain Shams Engineering Journal,
Journal Year:
2024,
Volume and Issue:
15(9), P. 102895 - 102895
Published: June 4, 2024
Polymer
nanocomposites
have
received
significant
scientific
and
industrial
attention
due
to
the
synergetic
combination
of
features
a
polymeric
matrix
organic
or
inorganic
nanofillers.
While
experiments
been
essential
for
identifying
characterizing
new
materials,
their
high
costs
limited
trials
shifted
focus
towards
applying
machine
learning
(ML)
predict
nanocomposite
properties.
This
study
aims
establish
connection
with
tribological
performance
multi-walled
carbon
nanotubes
(MWCNT)
reinforced
polymethyl
methacrylate
(PMMA)
through
comparison
ML
techniques.
The
wear
friction
characteristics
MWCNT-reinforced
PMMA
were
predicted
based
on
three
input
variables:
material
weight
percentage,
load
weight,
track
diameter.
using
different
ensemble
algorithms:
random
forest
(RF),
extra
tree
(ET),
gradient
boosting
(GBM).
dataset
was
utilized
train
proposed
models
in
Python,
followed
by
hyperparameter
tuning
determine
best
model
predicting
target
values.
results
demonstrated
that
GBM
outperformed
RF
ET
models,
an
R-squared
0.99,
RMSE
0.62,
MAE
0.18.
models'
predictions
values
more
precise
than
These
findings
indicate
techniques,
particularly
model,
can
effectively
properties
nanocomposites,
potentially
reducing
need
extensive
experimental
contributing
advancements
science.
Discover Polymers.,
Journal Year:
2024,
Volume and Issue:
1(1)
Published: Sept. 27, 2024
Abstract
Polyethersulfone
composites
reinforced
with
biologically
synthesized
silver
nanoparticles
(AgNPs)
were
fabricated
via
compression
molding
at
30
GPa
and
250
°C,
nanoparticle
concentrations
ranging
from
0.5
to
3.0
wt.%.
Neem
leaf
extract
served
as
the
bioreducing
agent
in
AgNP
synthesis.
Characterization
using
transmission
electron
microscopy,
scanning
microscopy
energy
dispersive
X-ray
spectroscopy,
diffraction
confirmed
formation
of
spherical
AgNPs
an
average
size
approximately
21
nm
a
face-centered
cubic
structure.
Mechanical
testing
revealed
significant
property
improvements
addition
compared
control.
The
2
wt.%
composite
demonstrated
optimal
properties,
including
120%
increase
tensile
strength,
246%
flexural
43.18%
hardness,
127%
impact
resistance.
2.5
exhibited
optimum
hardness
247%
modulus,
while
had
highest
modulus
105%
increase.
These
enhanced
mechanical
properties
make
suitable
for
demanding
sustainable
engineering
applications,
such
automotive
systems,
potential
reduce
vehicle
weight,
improve
fuel
efficiency,
lower
emissions.
Additionally,
it
holds
promise
renewable
systems
cleaner
generation
water
purification
use
filters
or
membranes,
highlighting
bio-synthesized
advanced
materials
development.
