ACS Applied Materials & Interfaces,
Год журнала:
2023,
Номер
15(15), С. 19435 - 19446
Опубликована: Апрель 10, 2023
Conductive
hydrogels
as
promising
candidates
of
wearable
electronics
have
attracted
considerable
interest
in
health
monitoring,
multifunctional
electronic
skins,
and
human-machine
interfaces.
However,
to
simultaneously
achieve
excellent
electrical
properties,
superior
stretchability,
a
low
detection
threshold
conductive
remains
an
extreme
challenge.
Herein,
ultrastretchable
high-conductivity
MXene-based
organohydrogel
(M-OH)
is
developed
for
human
monitoring
machine-learning-assisted
object
recognition,
which
fabricated
based
on
Ti3C2Tx
MXene/lithium
salt
(LS)/poly(acrylamide)
(PAM)/poly(vinyl
alcohol)
(PVA)
hydrogel
through
facile
immersion
strategy
glycerol/water
binary
solvent.
The
M-OH
demonstrates
remarkable
stretchability
(2000%)
high
conductivity
(4.5
S/m)
due
the
strong
interaction
between
MXene
dual-network
PVA/PAM
matrix
incorporation
LS,
respectively.
Meanwhile,
sensor
enables
with
sensitivity
limit
(12
Pa).
Furthermore,
pressure
mapping
image
recognition
technology,
8
×
pixelated
M-OH-based
sensing
array
can
accurately
identify
different
objects
accuracy
97.54%
under
assistance
deep
learning
neural
network
(DNN).
This
work
comprehensive
performances
high-conductive
would
further
explore
extensive
potential
application
prospects
personal
healthcare,
interfaces,
artificial
intelligence.
Conductive
polymer
hydrogels
(CPHs)
are
widely
employed
in
emerging
flexible
electronic
devices
because
they
possess
both
the
electrical
conductivity
of
conductors
and
mechanical
properties
hydrogels.
However,
poor
compatibility
between
conductive
polymers
hydrogel
matrix,
as
well
swelling
behavior
humid
environments,
greatly
compromises
CPHs,
limiting
their
applications
wearable
devices.
Herein,
a
supramolecular
strategy
to
develop
strong
tough
CPH
with
excellent
anti-swelling
by
incorporating
hydrogen,
coordination
bonds,
cation-π
interactions
rigid
conducting
soft
matrix
is
reported.
Benefiting
from
effective
networks,
obtained
has
homogeneous
structural
integrity,
exhibiting
remarkable
tensile
strength
(1.63
MPa),
superior
elongation
at
break
(453%),
toughness
(5.5
MJ
m-3
).
As
strain
sensor,
possesses
high
(2.16
S
m-1
),
wide
linear
detection
range
(0-400%),
sensitivity
(gauge
factor
=
4.1),
sufficient
monitor
human
activities
different
windows.
Furthermore,
this
resistance
been
successfully
applied
underwater
sensors
for
monitoring
frog
swimming
communication.
These
results
reveal
new
possibilities
amphibious
sensors.
Chemical Society Reviews,
Год журнала:
2023,
Номер
52(17), С. 6191 - 6220
Опубликована: Янв. 1, 2023
This
review
highlights
the
recent
progress
in
piezoelectric
gels
(also
known
as
PiezoGels)
comprised
of
polymers,
ceramic
oxides
and
supramolecular
materials
used
for
energy
harvesting,
sensing
wound
dressing.
Materials Horizons,
Год журнала:
2023,
Номер
11(5), С. 1234 - 1250
Опубликована: Дек. 22, 2023
Conductive
hydrogels
have
attracted
much
attention
for
their
wide
application
in
the
field
of
flexible
wearable
sensors
due
to
outstanding
flexibility,
conductivity
and
sensing
properties.
However,
weak
mechanical
properties,
lack
frost
resistance
susceptibility
microbial
contamination
traditional
conductive
greatly
limit
practical
application.
In
this
work,
multifunctional
polyvinyl
alcohol
(PVA)/carboxymethyl
cellulose
(CMC)/poly(acrylamide-
Analytical Chemistry,
Год журнала:
2023,
Номер
95(7), С. 3811 - 3820
Опубликована: Фев. 7, 2023
Interest
in
wearable
and
stretchable
multifunctional
sensors
has
grown
rapidly
recent
years.
The
sensing
elements
must
accurately
detect
external
stimuli
to
expand
their
applicability
as
sensors.
However,
the
sensor's
self-healing
adhesion
a
target
object
have
been
major
challenges
developing
such
practical
versatile
devices.
In
this
study,
we
prepared
hydrogel
(LM-SA-PAA)
composed
of
liquid
metal
(LM),
sodium
alginate
(SA),
poly(acrylic
acid)
(PAA)
with
ultrastretchable,
excellent
self-healing,
self-adhesive,
high-sensitivity
capabilities
that
enable
conformal
contact
between
sensor
skin
even
during
dynamic
movements.
performance
stems
from
its
double
cross-linked
networks,
including
physical
chemical
networks.
cross-link
formed
by
ionic
interaction
carboxyl
groups
PAA
gallium
ions
provide
reversible
autonomous
repair
properties,
whereas
covalent
bond
provides
stable
strong
network.
Alginate
forms
microgel
shell
around
LM
nanoparticles
via
coordination
Ga
ions.
addition
offering
exceptional
colloidal
stability,
sufficient
polar
groups,
ensuring
adheres
diverse
substrates.
Based
on
efficient
electrical
pathway
provided
LM,
exhibited
strain
sensitivity
enabled
detection
various
human
motions
electrocardiographic
monitoring.
preparation
method
is
simple
can
be
used
for
low-cost
fabrication
sensors,
which
broad
application
prospects
human-machine
interface
compatibility
medical
ACS Applied Materials & Interfaces,
Год журнала:
2023,
Номер
15(15), С. 19435 - 19446
Опубликована: Апрель 10, 2023
Conductive
hydrogels
as
promising
candidates
of
wearable
electronics
have
attracted
considerable
interest
in
health
monitoring,
multifunctional
electronic
skins,
and
human-machine
interfaces.
However,
to
simultaneously
achieve
excellent
electrical
properties,
superior
stretchability,
a
low
detection
threshold
conductive
remains
an
extreme
challenge.
Herein,
ultrastretchable
high-conductivity
MXene-based
organohydrogel
(M-OH)
is
developed
for
human
monitoring
machine-learning-assisted
object
recognition,
which
fabricated
based
on
Ti3C2Tx
MXene/lithium
salt
(LS)/poly(acrylamide)
(PAM)/poly(vinyl
alcohol)
(PVA)
hydrogel
through
facile
immersion
strategy
glycerol/water
binary
solvent.
The
M-OH
demonstrates
remarkable
stretchability
(2000%)
high
conductivity
(4.5
S/m)
due
the
strong
interaction
between
MXene
dual-network
PVA/PAM
matrix
incorporation
LS,
respectively.
Meanwhile,
sensor
enables
with
sensitivity
limit
(12
Pa).
Furthermore,
pressure
mapping
image
recognition
technology,
8
×
pixelated
M-OH-based
sensing
array
can
accurately
identify
different
objects
accuracy
97.54%
under
assistance
deep
learning
neural
network
(DNN).
This
work
comprehensive
performances
high-conductive
would
further
explore
extensive
potential
application
prospects
personal
healthcare,
interfaces,
artificial
intelligence.