Multi-scenario Adaptive Electronic Nose for the Detection of Environmental Odor Pollutants
Journal of Hazardous Materials,
Год журнала:
2025,
Номер
489, С. 137660 - 137660
Опубликована: Фев. 18, 2025
Язык: Английский
Nanomaterial Innovations and Machine Learning in Gas Sensing Technologies for Real-Time Health Diagnostics
ACS Sensors,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 10, 2025
Breath
sensors
represent
a
frontier
in
noninvasive
diagnostics,
leveraging
the
detection
of
volatile
organic
compounds
(VOCs)
exhaled
breath
for
real-time
health
monitoring.
This
review
highlights
recent
advancements
breath-sensing
technologies,
with
focus
on
innovative
materials
driving
their
enhanced
sensitivity
and
selectivity.
Polymers,
carbon-based
like
graphene
carbon
nanotubes,
metal
oxides
such
as
ZnO
SnO2
have
demonstrated
significant
potential
detecting
biomarkers
related
to
diseases
including
diabetes,
liver/kidney
dysfunction,
asthma,
gut
health.
The
structural
operational
principles
these
are
examined,
revealing
how
unique
properties
contribute
key
respiratory
gases
acetone,
ammonia
(NH3),
hydrogen
sulfide,
nitric
oxide.
complexity
samples
is
addressed
through
integration
machine
learning
(ML)
algorithms,
convolutional
neural
networks
(CNNs)
support
vector
machines
(SVMs),
which
optimize
data
interpretation
diagnostic
accuracy.
In
addition
sensing
VOCs,
devices
capable
monitoring
parameters
airflow,
temperature,
humidity,
essential
comprehensive
analysis.
also
explores
expanding
role
artificial
intelligence
(AI)
transforming
wearable
into
sophisticated
tools
personalized
enabling
disease
Together,
advances
sensor
ML-based
analytics
present
promising
platform
future
individualized,
healthcare.
Язык: Английский
Smart VOCs Recognition System Based on Single Gas Sensor and Multi-task Deep Learning Model
Sensors and Actuators B Chemical,
Год журнала:
2025,
Номер
unknown, С. 137853 - 137853
Опубликована: Апрель 1, 2025
Язык: Английский
Advances in Gas Detection of Pattern Recognition Algorithms for Chemiresistive Gas Sensor
Materials,
Год журнала:
2024,
Номер
17(21), С. 5190 - 5190
Опубликована: Окт. 24, 2024
Gas
detection
and
monitoring
are
critical
to
protect
human
health
safeguard
the
environment
ecosystems.
Chemiresistive
sensors
widely
used
in
gas
due
their
ease
of
fabrication,
high
customizability,
mechanical
flexibility,
fast
response
time.
However,
with
rapid
development
industrialization
technology,
main
challenges
faced
by
chemiresistive
poor
selectivity
insufficient
anti-interference
stability
complex
application
environments.
In
order
overcome
these
shortcomings
sensors,
pattern
recognition
method
is
emerging
having
a
great
impact
field
sensing.
this
review,
we
focus
systematically
on
advancements
data
processing
methods
for
feature
extraction,
such
as
determining
characteristics
original
curve,
curve
fitting
parameters,
transform
domain.
Additionally,
emphasized
developments
traditional
algorithms
neural
network
algorithm
discrimination
analyzed
advantages
through
an
extensive
literature
review.
Lastly,
summarized
research
provided
prospects
future
development.
Язык: Английский
A Study on the Effect of Drift Factor on Feature Optimization in Electronic Nose Detection
Applied Sciences,
Год журнала:
2024,
Номер
14(23), С. 11366 - 11366
Опубликована: Дек. 5, 2024
As
an
important
instrument
for
olfactory
detection
in
non-destructive
testing
(NDT),
the
electronic
nose
plays
role
simulating
detection.
However,
its
performance
is
often
affected
by
drift
phenomena,
including
changes
sensor
caused
environmental
factors
and
fatigue
due
to
long-term
use.
Although
effect
of
on
noses
widely
recognized,
there
still
a
relative
lack
research
how
affects
feature
optimization.
This
study
presents
novel
idea
that
not
only
affect
direct
readings
but
may
also
have
profound
optimization
process
hence
compensation
nose.
To
explore
this
concept,
we
chose
temperature
humidity,
two
most
common
factors,
our
experimental
study.
In
study,
verified
impact
found
positive
correlation
between
concentration
scores
correct
classification
rate.
Moreover,
adopted
innovative
quadratic
method,
which
aims
reduce
influence
factor
thus
improve
resistance
experiments,
unweighted
method
performs
best
reducing
effect.
After
process,
recognition
rate
reaches
100%
training
set
96%
test
set,
indicates
has
improved
significantly
terms
resistance.
summary,
explores
proposes
effective
treatment
provides
reference
direction
development
technology.
Язык: Английский