Nanomaterials,
Год журнала:
2024,
Номер
14(24), С. 2052 - 2052
Опубликована: Дек. 22, 2024
The
electronic
nose
is
an
increasingly
useful
tool
in
many
fields
and
applications.
Our
thermal
approach,
based
on
nanostructured
metal
oxide
chemiresistors
a
gradient,
has
the
advantage
of
being
tiny
therefore
integrable
portable
wearable
devices.
Obviously,
wise
choice
nanomaterial
crucial
for
device’s
performance
should
be
carefully
considered.
Here
we
show
how
addition
different
amounts
Au
(between
1
5
wt%)
Cu2O–SnO2
nanospheres
affects
performance.
Interestingly,
best
not
achieved
with
material
offering
highest
intrinsic
selectivity.
This
confirms
importance
specific
studies,
since
chemoresistive
gas
sensors
does
linearly
affect
nose.
By
optimizing
amount
Au,
device
perfect
classification
tested
gases
(acetone,
ethanol,
toluene)
good
concentration
estimation
(with
mean
absolute
percentage
error
around
16%).
These
performances,
combined
potentially
smaller
dimensions
less
than
0.5
mm2,
make
this
ideal
candidate
numerous
applications,
such
as
agri-food,
environmental,
biomedical
sectors.
Abstract
Dyes
are
organic
compounds.
Azo
dyes,
as
the
most
important
usually
synthesized
and
reduced
after
entering
human
body,
turning
into
mutagenic
carcinogenic
amines.
A
new
efficient
composite
containing
calix
[4]
arene
(Calix),
metal‐organic
framework
(MIL‐101(Fe)),
copper(II)
oxide
(Calix/MIL‐101(Fe)/CuO)
was
utilized
for
degradation
of
Malachite
Green
(MG).
The
dye
removal
efficiency
17%
Calix,
56%
CuO,
64%
MIL‐101(Fe),
98.8%
Calix/MIL‐101(Fe)/CuO
using
LED
visible
light.
MG
by
different
doses,
including
0,
1,
2,
3,
4
mg,
8%,
64%,
75%,
85%,
98.8%,
respectively.
kinetic
rate
constants
mg
were
0.0134,
0.1086,
0.1182,
0.1308,
0.1727
(zero‐order
rate),
5E‐04,
0.007,
0.008,
0.011,
0.023
(first‐order
4E‐05,
0.0007,
0.001,
0.0017,
0.0211
(second‐order
kinetics
obeyed
zero‐order
model.
exhibited
high
reusability
degradation.
results
suggest
that
could
serve
an
alternative
photocatalyst
contaminants
in
aqueous
media.
Sensors,
Год журнала:
2025,
Номер
25(5), С. 1423 - 1423
Опубликована: Фев. 26, 2025
Given
the
significant
impact
of
air
pollution
on
global
health,
continuous
and
precise
monitoring
quality
in
all
populated
environments
is
crucial.
Unfortunately,
even
most
developed
economies,
current
networks
are
largely
inadequate.
The
high
cost
stations
has
been
identified
as
a
key
barrier
to
widespread
coverage,
making
cost-effective
devices
potential
game
changer.
However,
accuracy
measurements
obtained
from
low-cost
sensors
affected
by
many
factors,
including
gas
cross-sensitivity,
environmental
conditions,
production
inconsistencies.
Fortunately,
machine
learning
models
can
capture
complex
interdependent
relationships
sensor
responses
thus
enhance
their
readings
accuracy.
After
gathering
placed
alongside
reference
station,
data
were
used
train
such
models.
Assessments
performance
showed
that
tailored
individual
units
greatly
improved
measurement
accuracy,
boosting
correlation
with
reference-grade
instruments
up
10%.
Nonetheless,
this
research
also
revealed
inconsistencies
similar
prevent
creation
unified
correction
model
for
given
type.
The Science of The Total Environment,
Год журнала:
2025,
Номер
977, С. 179364 - 179364
Опубликована: Апрель 15, 2025
Air
pollution
poses
a
significant
threat
to
public
health.
Low-cost
air
quality
sensors
(LCSs)
can
provide
data
foundation
for
monitoring,
particularly
supplementing
the
regulatory
monitoring
network
and
identifying
local
issues.
However,
performance
varies
considerably,
questions
remain
regarding
reliability
accuracy
of
LCS
data.
We
evaluated
accuracy,
stability
precision
six
LCSs
over
three-month
period
collocation
with
reference
instruments
at
two
locations.
A
mathematical
workflow
including
calibration
validation
was
developed
stability,
incorporating
combination
environmental
factors
(e.g.,
temperature,
relative
humidity),
linear
nonlinear
regression,
followed
by
evaluation
Bland-Altman
plots.
For
particulate
matter,
from
found
be
reliable
after
simple
regression
(R2
>
0.9
both
validation).
gas
nitrogen
dioxide,
carbon
monoxide,
Ozone,
methods
that
met
requirements
also
performed
well
using
models
0.7
validation),
whereas
machine
learning
models,
such
as
random
forest,
could
not
pass
validation,
require
cautious
application.
In
non-laboratory
environments,
into
function
may
lead
subsequent
instability.
Regarding
between
LCSs,
unstable
measurement
biases
among
devices
have
been
observed.
Linear
method
is
recommended
preferred
onsite
caution
advised
when
due
increased
uncertainty.
Furthermore,
deploying
it
important
consider
their
varying
responses
high
or
low
pollutant
concentrations.
Atmosphere,
Год журнала:
2025,
Номер
16(4), С. 472 - 472
Опубликована: Апрель 18, 2025
Within
the
scope
of
“Eco
Map
Zagreb”
project,
eight
sensor
sets
(type
AQMeshPod)
were
set
up
at
an
automatic
measuring
station
Institute
for
Medical
Research
and
Occupational
Health
(IMROH)
comparison
with
reference
methods
air
quality
measurement
during
2018.
This
is
a
city
background
within
Zagreb
network
monitoring,
where
measurements
SO2,
CO,
NO2,
O3,
PM10
PM2.5,
are
performed
using
standardized
accredited
according
to
EN
ISO/IEC
17025.
paper
presents
pollutant
mass
concentrations
determined
by
sensors
methods.
The
data
compared
filtered
remove
outliers
handle
deviations
between
results
obtained
methods,
considering
different
approaches
gas
PM
data.
A
showed
large
scattering
all
gaseous
pollutants
while
PM2.5
indicated
satisfactory
low
dispersion.
regression
analysis
significant
seasonal
dependence
pollutants.
Significant
statistical
differences
whole
year
in
seasons
pollutants,
as
well
PM10,
observed,
significance
varying
results.
