Polymer-Plastics Technology and Materials,
Год журнала:
2024,
Номер
unknown, С. 1 - 8
Опубликована: Дек. 12, 2024
Eco-friendly
silk
fibroin/poly(D,L-lactide-co-glycolide)
(SF/PLGA)
materials
were
successfully
fabricated
using
a
facile
strategy.
The
characterized
by
scanning
electron
micrograph
(SEM),
X-ray
diffraction
(XRD)
and
infrared
spectra
(IR).
Systematic
investigations
completed
to
examine
the
degradation
rates
in
natural
soil,
cytocompatibility
thermostability
of
materials.
It
is
interesting
find
that
after
SF
was
hybridized
PLGA,
rate
increased.
Meanwhile,
show
good
cytocompatibility.
SEM,
IR
XRD
results
reveal
there
hardly
any
interaction
between
PLGA
SF/PLGA
material,
physically
mixed
with
PLGA.
This
study
opens
up
new
horizon
design
preparation
SF-based
for
promising
applications
medical
biodegradable
material
fields.
Polymers,
Год журнала:
2024,
Номер
16(14), С. 2021 - 2021
Опубликована: Июль 15, 2024
Poly(vinyl
alcohol)
(PVA)
is
a
versatile
synthetic
polymer,
used
for
the
design
of
hydrogels,
porous
membranes
and
films.
Its
solubility
in
water,
film-
hydrogel-forming
capabilities,
non-toxicity,
crystallinity
excellent
mechanical
properties,
chemical
inertness
stability
towards
biological
fluids,
superior
oxygen
gas
barrier
good
printability
availability
(relatively
low
production
cost)
are
main
aspects
that
make
PVA
suitable
variety
applications,
from
biomedical
pharmaceutical
uses
to
sensing
devices,
packaging
materials
or
wastewater
treatment.
However,
pure
present
limited
flexibility
poor
biocompatibility
biodegradability,
which
restrict
its
use
alone
various
applications.
mixed
with
other
polymers
biomolecules
(polysaccharides,
proteins,
peptides,
amino
acids
etc.),
as
well
inorganic/organic
compounds,
generates
wide
PVA’s
shortcomings
considerably
improved,
new
functionalities
obtained.
Also,
transformation
brings
features
opens
door
unexpected
uses.
The
review
focused
on
recent
advances
PVA-based
hydrogels.
Abstract
Hydrogel
sensors
are
widely
utilized
in
soft
robotics
and
tissue
engineering
due
to
their
excellent
mechanical
properties
biocompatibility.
However,
high‐water
environments,
traditional
hydrogels
can
experience
significant
swelling,
leading
decreased
electrical
performance,
potentially
losing
shape,
sensing
capabilities.
This
study
addresses
these
challenges
by
leveraging
the
Hofmeister
effect,
coupled
with
directional
freezing
salting‐out
techniques,
develop
a
layered,
high‐strength,
tough,
antiswelling
PVA/MXene
hydrogel.
In
particular,
process
enhances
self‐entanglement
of
PVA,
resulting
an
S‐PM
hydrogel
tensile
strength
up
2.87
MPa.
Furthermore,
retains
its
structure
after
7
d
only
6%
change
resistance.
Importantly,
performance
is
improved
postswelling,
capability
rarely
achievable
hydrogels.
Moreover,
demonstrates
faster
response
times
more
stable
resistance
rates
underwater
tests,
making
it
crucial
for
long‐term
continuous
monitoring
challenging
aquatic
ensuring
sustained
operation
monitoring.
ACS Applied Materials & Interfaces,
Год журнала:
2024,
Номер
16(46), С. 64002 - 64011
Опубликована: Ноя. 7, 2024
Conductive
organohydrogels-based
flexible
pressure
sensors
have
gained
considerable
attention
in
health
monitoring,
artificial
skin,
and
human-computer
interaction
due
to
their
excellent
biocompatibility,
wearability,
versatility.
However,
hydrogels'
unsatisfactory
mechanical
unstable
electrical
properties
hinder
comprehensive
application.
Herein,
an
elastic,
fatigue-resistant,
antifreezing
poly(vinyl
alcohol)
(PVA)/lipoic
acid
(LA)
organohydrogel
with
a
double-network
structure
reversible
cross-linking
interactions
has
been
designed,
MXene
as
conductive
filler
is
functionalized
into
further
enhance
the
diverse
sensing
performance
of
sensors.
The
as-fabricated
MXene-based
PVA/LA
organohydrogels
(PLBM)
exhibit
stable
fatigue
resistance
for
over
450
cycles
under
40%
compressive
strain,
elasticity,
(<−20
°C),
degradability.
Furthermore,
based
on
PLBM
show
fast
response
time
(62
ms),
high
sensitivity
(S
=
0.0402
kPa–1),
stability
(over
1000
cycles).
exceptional
enables
monitor
human
movements,
such
joint
flexion
throat
swallowing.
Moreover,
integrating
one-dimensional
convolutional
neural
networks
long–short-term
memory
deep
learning
algorithms
developed
recognize
letters
93.75%
accuracy,
representing
enormous
potential
monitoring
motion
interaction.
