Chemical Reviews,
Год журнала:
2024,
Номер
124(17), С. 9899 - 9948
Опубликована: Авг. 28, 2024
Electronic
skins
(e-skins)
have
seen
intense
research
and
rapid
development
in
the
past
two
decades.
To
mimic
capabilities
of
human
skin,
a
multitude
flexible/stretchable
sensors
that
detect
physiological
environmental
signals
been
designed
integrated
into
functional
systems.
Recently,
researchers
increasingly
deployed
machine
learning
other
artificial
intelligence
(AI)
technologies
to
neural
system
for
processing
analysis
sensory
data
collected
by
e-skins.
Integrating
AI
has
potential
enable
advanced
applications
robotics,
healthcare,
human–machine
interfaces
but
also
presents
challenges
such
as
diversity
model
robustness.
In
this
review,
we
first
summarize
functions
features
e-skins,
followed
feature
extraction
different
models.
Next,
discuss
utilization
design
e-skin
address
key
topic
implementation
e-skins
accomplish
range
tasks.
Subsequently,
explore
hardware-layer
in-skin
before
concluding
with
an
opportunities
various
aspects
AI-enabled
Advanced Functional Materials,
Год журнала:
2024,
Номер
unknown
Опубликована: Май 14, 2024
Abstract
Electrochemical
(EC)
analysis
has
emerged
as
a
high‐sensitivity,
reliable,
cost‐effective,
and
rapidly
evolving
technique
that
garnered
significant
attention
across
diverse
domains.
Furthermore,
EC‐based
techniques
hold
great
potential
for
miniaturization
integration.
The
integration
of
EC
with
mode/signal
(such
light,
magnetic,
thermal
signals,
etc.)
provides
unique
opportunities
biosensors
to
acquire
more
information
through
single
sensing
platform.
By
coupling
multiple
signals
or
processing
them
logically,
the
detection
accuracy
can
be
further
improved,
probability
false
positives
negatives
minimized.
In
this
review,
thorough
multi‐
sensors
in
field
is
conducted,
along
their
various
(e.g.,
fluorescence,
photothermal,
colorimetry,
microfluidic,
etc.).
aim
delve
into
latest
advances,
applications,
well
challenges
multi‐mode/signal
biosensors,
where
utilization
modalities
helps
enhance
accuracy,
sensitivity,
selectivity.
This
review
new
insight
synergistic
effects
integrating
other
techniques,
aiming
shed
light
on
near‐future
developments
EC‐integrated
biosensors.
ACS Nano,
Год журнала:
2024,
Номер
18(5), С. 4579 - 4589
Опубликована: Янв. 23, 2024
To
achieve
a
highly
realistic
robot,
closely
mimicking
human
skin
in
terms
of
materials
and
functionality
is
essential.
This
paper
presents
an
all-protein
silk
fibroin
bionic
(SFBS)
that
emulates
both
fast-adapting
(FA)
slow-adapting
(SA)
receptors.
The
mechanically
different
film
hydrogel,
which
exhibited
skin-like
properties,
such
as
stretchability
(>140%),
elasticity,
low
modulus
(<10
kPa),
biocompatibility,
degradability,
were
prepared
through
mesoscopic
reconstruction
engineering
to
mimic
the
epidermis
dermis.
Our
SFBS,
incorporating
SA
FA
sensors,
demonstrated
sensitive
(1.083
kPa–1)
static
pressure
sensing
performance
(in
vitro
vivo),
showed
ability
sense
high-frequency
vibrations
(50–400
Hz),
could
discriminate
sliding,
even
identify
fine
morphological
differences
between
objects.
As
proof
concept,
SFBS-integrated
rehabilitation
glove
was
synthesized,
help
stroke
patients
regain
sensory
feedback.
In
conclusion,
this
work
provides
practical
approach
for
developing
equivalents,
prostheses,
smart
robots.
Chemical Reviews,
Год журнала:
2024,
Номер
124(17), С. 9899 - 9948
Опубликована: Авг. 28, 2024
Electronic
skins
(e-skins)
have
seen
intense
research
and
rapid
development
in
the
past
two
decades.
To
mimic
capabilities
of
human
skin,
a
multitude
flexible/stretchable
sensors
that
detect
physiological
environmental
signals
been
designed
integrated
into
functional
systems.
Recently,
researchers
increasingly
deployed
machine
learning
other
artificial
intelligence
(AI)
technologies
to
neural
system
for
processing
analysis
sensory
data
collected
by
e-skins.
Integrating
AI
has
potential
enable
advanced
applications
robotics,
healthcare,
human–machine
interfaces
but
also
presents
challenges
such
as
diversity
model
robustness.
In
this
review,
we
first
summarize
functions
features
e-skins,
followed
feature
extraction
different
models.
Next,
discuss
utilization
design
e-skin
address
key
topic
implementation
e-skins
accomplish
range
tasks.
Subsequently,
explore
hardware-layer
in-skin
before
concluding
with
an
opportunities
various
aspects
AI-enabled
