ENVIRONMENTAL SYSTEMS RESEARCH,
Год журнала:
2024,
Номер
13(1)
Опубликована: Окт. 9, 2024
Air
is
one
of
the
most
significant
elements
environment.
The
increasing
global
air
pollution
crisis
poses
an
unavoidable
threat
to
human
health,
environmental
sustainability,
ecosystems,
and
earth's
climate.
has
been
referred
as
a
silent
killer
due
its
insidious
nature.
Its
indirect
impact
on
health
further
underscores
dangerous
effects.
Early
detection
quality
can
potentially
save
millions
lives
globally.
A
unique
transformative
approach
harness
power
machine
learning
combat
pollution.
This
research
presents
manual
web-based
automatic
prediction
system
that
provides
real-time
alerts
status
help
prevent
premature
deaths,
chronic
diseases,
other
problems.
pollutants,
including
carbon
monoxide
(CO),
ozone
(O3),
nitrogen
dioxide
(NO2),
particulate
matter
(PM
2.5),
are
used
in
this
study
for
feature
analysis
extraction.
utilizes
publicly
available
data
from
23,463
different
cities
worldwide.
Data
preprocessing
was
performed
before
feeding
into
models
correlation
evaluation.
proposed
uses
various
predict
quality,
Random
Forest
(100%),
Logistic
Regression
(79%),
Decision
Tree
Support
Vector
Machine
(93%),
Linear
SVC
(98%),
K-Nearest
Neighbor
(99%),
Multinomial
Naïve
Bayes
(52%).
user-friendly
Django-based
web
interface
offers
accessible
platform
users
monitor
real-time,
based
two
best-performing
models:
techniques.
Abstract
Neural
interfaces,
emerging
at
the
intersection
of
neurotechnology
and
urban
planning,
promise
to
transform
how
we
interact
with
our
surroundings
communicate.
By
recording
decoding
neural
signals,
these
interfaces
facilitate
direct
connections
between
brain
external
devices,
enabling
seamless
information
exchange
shared
experiences.
Nevertheless,
their
development
is
challenged
by
complexities
in
materials
science,
electrochemistry,
algorithmic
design.
Electrophysiological
crosstalk
mismatch
electrode
rigidity
tissue
flexibility
further
complicate
signal
fidelity
biocompatibility.
Recent
closed‐loop
brain‐computer
while
promising
for
mood
regulation
cognitive
enhancement,
are
limited
accuracy
adaptability
user
interfaces.
This
perspective
outlines
challenges
discusses
progress
contrasting
non‐invasive
invasive
approaches,
explores
dynamics
stimulation
interfacing.
Emphasis
placed
on
applications
beyond
healthcare,
highlighting
need
implantable
high‐resolution
capabilities.
Tenside Surfactants Detergents,
Год журнала:
2024,
Номер
61(4), С. 285 - 296
Опубликована: Апрель 29, 2024
Abstract
This
review
critically
analyzes
the
incorporation
of
artificial
intelligence
(AI)
in
surface
chemistry
and
catalysis
to
emphasize
revolutionary
impact
AI
techniques
this
field.
The
current
examines
various
studies
that
using
techniques,
including
machine
learning
(ML),
deep
(DL),
neural
networks
(NNs),
catalysis.
It
reviews
literature
on
application
models
predicting
adsorption
behaviours,
analyzing
spectroscopic
data,
improving
catalyst
screening
processes.
combines
both
theoretical
empirical
provide
a
comprehensive
synthesis
findings.
demonstrates
applications
have
made
remarkable
progress
properties
nanostructured
catalysts,
discovering
new
materials
for
energy
conversion,
developing
efficient
bimetallic
catalysts
CO
2
reduction.
AI-based
analyses,
particularly
advanced
NNs,
provided
significant
insights
into
mechanisms
dynamics
catalytic
reactions.
will
be
shown
plays
crucial
role
by
significantly
accelerating
discovery
enhancing
process
optimization,
resulting
enhanced
efficiency
selectivity.
mini-review
highlights
challenges
data
quality,
model
interpretability,
scalability,
ethical,
environmental
concerns
AI-driven
research.
importance
continued
methodological
advancements
responsible
implementation
Materials Futures,
Год журнала:
2024,
Номер
3(4), С. 042103 - 042103
Опубликована: Окт. 8, 2024
Abstract
High-entropy
oxides
(HEOs),
with
their
multi-principal-element
compositional
diversity,
have
emerged
as
promising
candidates
in
the
realm
of
energy
materials.
This
review
encapsulates
progress
harnessing
HEOs
for
conversion
and
storage
applications,
encompassing
solar
cells,
electrocatalysis,
photocatalysis,
lithium-ion
batteries,
solid
oxide
fuel
cells.
The
critical
role
theoretical
calculations
simulations
is
underscored,
highlighting
contribution
to
elucidating
material
stability,
deciphering
structure-activity
relationships,
enabling
performance
optimization.
These
computational
tools
been
instrumental
multi-scale
modeling,
high-throughput
screening,
integrating
artificial
intelligence
design.
Despite
promise,
challenges
such
fabrication
complexity,
cost,
hurdles
impede
broad
application
HEOs.
To
address
these,
this
delineates
future
research
perspectives.
include
innovation
cost-effective
synthesis
strategies,
employment
situ
characterization
micro-chemical
insights,
exploration
unique
physical
phenomena
refine
performance,
enhancement
models
precise
structure-performance
predictions.
calls
interdisciplinary
synergy,
fostering
a
collaborative
approach
between
materials
science,
chemistry,
physics,
related
disciplines.
Collectively,
these
efforts
are
poised
propel
towards
commercial
viability
new
technologies,
heralding
innovative
solutions
pressing
environmental
challenges.
Advanced Materials,
Год журнала:
2024,
Номер
unknown
Опубликована: Окт. 30, 2024
Abstract
Nanoparticles
(NPs)
of
high
entropy
materials
(HEMs)
have
attracted
significant
attention
due
to
their
versatility
and
wide
range
applications.
HEM
NPs
can
be
synthesized
by
fragmenting
bulk
HEMs
or
disintegrating
recrystallizing
them.
Alternatively,
directly
producing
in
NP
form
from
atomic/ionic/molecular
precursors
presents
a
challenge.
A
widely
adopted
strategy
involves
thermodynamically
driving
formation
leveraging
the
entropic
contribution
but
incorporating
strategies
limit
growth
at
elevated
temperatures
used
for
maximizing
entropy.
second
approach
is
kinetically
drive
promoting
rapid
reactions
homogeneous
reactant
mixtures
using
highly
diluted
precursor
dissolutions.
Additionally,
experimental
evidence
suggests
that
enthalpy
plays
role
processes
moderate
temperatures,
with
energy
cost
generating
additional
surfaces
interfaces
nanoscale
stabilizing
phase.
This
review
critically
assesses
various
synthesis
developed
preparation,
highlighting
key
illustrative
examples
offering
insights
into
underlying
mechanisms.
Such
are
critical
fine‐tuning
conditions
achieve
specific
outcomes,
ultimately
enabling
effective
optimized
generations
these
advanced
both
current
emerging
applications
across
scientific
technological
fields.
ACS Nanoscience Au,
Год журнала:
2023,
Номер
4(1), С. 3 - 20
Опубликована: Ноя. 16, 2023
Materials
referred
to
as
"high
entropy"
contain
a
large
number
of
elements
randomly
distributed
on
the
lattice
sites
crystalline
solid,
such
that
high
configurational
entropy
is
presumed
contribute
significantly
their
formation
and
stability.
High
temperatures
are
typically
required
achieve
stabilization,
which
can
make
it
challenging
synthesize
colloidal
nanoparticles
materials.
Nonetheless,
strategies
emerging
for
synthesis
nanoparticles,
interest
synergistic
properties
unique
catalytic
functions
arise
from
constituent
interactions.
In
this
Perspective,
we
highlight
classes
materials
have
been
made
well
insights
into
synthetic
methods
pathways
by
they
form.
We
then
discuss
concept
within
context
synthesized
at
much
lower
than
drive
formation.
Next,
identify
address
challenges
opportunities
in
field
nanoparticle
synthesis.
emphasize
aspects
characterization
especially
important
consider
materials,
including
powder
X-ray
diffraction
elemental
mapping
with
scanning
transmission
electron
microscopy,
among
most
commonly
used
techniques
laboratory
settings.
Finally,
share
perspectives
future
directions
involving
an
emphasis
synthesis,
characterization,
fundamental
knowledge
needed
anticipated
advances
key
application
areas.
Materials,
Год журнала:
2024,
Номер
17(5), С. 1088 - 1088
Опубликована: Фев. 27, 2024
From
1990
to
2024,
this
study
presents
a
groundbreaking
bibliometric
and
sentiment
analysis
of
nanocomposite
literature,
distinguishing
itself
from
existing
reviews
through
its
unique
computational
methodology.
Developed
by
our
research
group,
novel
approach
systematically
investigates
the
evolution
nanocomposites,
focusing
on
microstructural
characterization,
electrical
properties,
mechanical
behaviors.
By
deploying
advanced
Boolean
search
strategies
within
Scopus
database,
we
achieve
meticulous
extraction
in-depth
exploration
thematic
content,
methodological
advancement
in
field.
Our
uniquely
identifies
critical
trends
insights
concerning
microstructure,
attributes,
performance.
The
paper
goes
beyond
traditional
textual
analytics
evaluation,
offering
new
interpretations
data
highlighting
significant
collaborative
efforts
influential
studies
domain.
findings
uncover
language,
shifts,
global
contributions,
providing
distinct
comprehensive
view
dynamic
research.
A
component
is
“State-of-the-Art
Gaps
Extracted
Results
Discussions”
section,
which
delves
into
latest
advancements
This
section
details
various
types
their
properties
introduces
applications,
especially
films.
tracing
historical
progress
identifying
emerging
trends,
emphasizes
significance
collaboration
molding
Moreover,
“Literature
Review
Guided
Artificial
Intelligence”
showcases
an
innovative
AI-guided
research,
first
Focusing
articles
2023,
selected
based
citation
frequency,
method
offers
perspective
interplay
between
nanocomposites
properties.
It
highlights
composition,
structure,
functionality
systems,
integrating
recent
for
overview
current
knowledge.
analysis,
with
average
score
0.638771,
reflects
positive
trend
academic
discourse
increasing
recognition
potential
nanocomposites.
another
novelty,
maps
intellectual
domain,
emphasizing
pivotal
themes
influence
crosslinking
time
attributes.
While
acknowledging
limitations,
exemplifies
indispensable
role
tools
synthesizing
understanding
extensive
body
literature.
work
not
only
elucidates
prevailing
but
also
contributes
insights,
enhancing
