Carbon
nanotubes
(CNTs)
and
MXenes
are
promising
as
targeted
sensing
agents
in
advanced
functional
materials.
more
suitable
for
biosensing
applications
due
to
their
versatility
compatibility
with
aquatic
environments.
Molecules,
Год журнала:
2024,
Номер
29(12), С. 2937 - 2937
Опубликована: Июнь 20, 2024
Fuel
cells
are
at
the
forefront
of
modern
energy
research,
with
graphene-based
materials
emerging
as
key
enhancers
performance.
This
overview
explores
recent
advancements
in
cathode
for
fuel
cell
applications.
Graphene's
large
surface
area
and
excellent
electrical
conductivity
mechanical
strength
make
it
ideal
use
different
solid
oxide
(SOFCs)
well
proton
exchange
membrane
(PEMFCs).
review
covers
various
forms
graphene,
including
graphene
(GO),
reduced
(rGO),
doped
highlighting
their
unique
attributes
catalytic
contributions.
It
also
examines
effects
structural
modifications,
doping,
functional
group
integrations
on
electrochemical
properties
durability
cathodes.
Additionally,
we
address
thermal
stability
challenges
derivatives
high
SOFC
operating
temperatures,
suggesting
potential
solutions
future
research
directions.
analysis
underscores
transformative
advancing
technology,
aiming
more
efficient,
cost-effective,
durable
systems.
Polymers,
Год журнала:
2024,
Номер
16(18), С. 2607 - 2607
Опубликована: Сен. 14, 2024
This
review
explores
the
application
of
Long
Short-Term
Memory
(LSTM)
networks,
a
specialized
type
recurrent
neural
network
(RNN),
in
field
polymeric
sciences.
LSTM
networks
have
shown
notable
effectiveness
modeling
sequential
data
and
predicting
time-series
outcomes,
which
are
essential
for
understanding
complex
molecular
structures
dynamic
processes
polymers.
delves
into
use
models
polymer
properties,
monitoring
polymerization
processes,
evaluating
degradation
mechanical
performance
Additionally,
it
addresses
challenges
related
to
availability
interpretability.
Through
various
case
studies
comparative
analyses,
demonstrates
different
science
applications.
Future
directions
also
discussed,
with
an
emphasis
on
real-time
applications
need
interdisciplinary
collaboration.
The
goal
this
is
connect
advanced
machine
learning
(ML)
techniques
science,
thereby
promoting
innovation
improving
predictive
capabilities
field.