Chemical Communications,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Jan. 1, 2024
Biomass-derived
carbon
materials
(BDCMs)
are
widely
considered
as
promising
and
practical
candidates
for
electrode
of
solid-state
supercapacitors
(SSCs),
due
to
their
low
cost,
good
chemical
mechanical
stabilities,
excellent
electrical
conductivity,
high
deployment
feasibility.
Numerous
investigations
have
recently
been
conducted
sustainably
transforming
biomass
into
with
electrochemical
performance
in
SSCs,
even
guided
by
data-driven
approaches.
Therefore,
this
review
addresses
conventional
emerging
synthesis
routes
BDCM-based
discusses
recent
advances
energy
storage
mechanisms
enhancement
BDCMs
improving
preparation
optimization
a
efficient
manner.
As
two
the
most
powerful
tools
novel
material
discovery
design,
machine
learning
(ML)
3D
printing
technologies
introduced
provide
closed-loop
guidelines
accurately
efficiently
producing
performance;
main
challenges
successfully
applying
ML
methodologies
also
addressed,
providing
critical
potential
innovation
future
development
SSCs.
In
review,
from
life-cycle
perspective,
environmental
benefits
assessed
being
highlighted
alternative
solidify
security
achieve
sustainable
management.
The
concluding
remarks
prospects
finally
discussed
valuable
insights
academic
researchers
governmental
policymakers.
With
concerted
efforts,
high-performance
SSCs
is
beneficial
achieving
UN
Sustainable
Development
Goals
7,
11-13.
Covalent
organic
frameworks
(COFs)
are
porous
crystalline
materials
obtained
by
linking
ligands
covalently.
Their
high
surface
area
and
adjustable
pore
sizes
make
them
ideal
for
a
range
of
applications,
including
CO2
capture,
CH4
storage,
gas
separation,
catalysis,
etc.
Traditional
methods
material
research,
which
mainly
rely
on
manual
experimentation,
not
particularly
efficient,
while
with
advancements
in
computer
science,
high-throughput
computational
screening
based
molecular
simulation
have
become
crucial
discovery,
yet
they
face
limitations
terms
resources
time.
Currently,
machine
learning
(ML)
has
emerged
as
transformative
tool
many
fields,
capable
analyzing
large
data
sets,
identifying
underlying
patterns,
predicting
performance
efficiently
accurately.
This
approach,
termed
"materials
genomics",
combines
ML
to
predict
design
high-performance
materials,
significantly
speeding
up
the
discovery
process
compared
traditional
methods.
review
discusses
functions
screening,
design,
prediction
COFs
highlights
their
applications
across
various
domains
like
thereby
providing
new
research
directions
enhancing
understanding
COF
applications.
Energies,
Journal Year:
2024,
Volume and Issue:
17(18), P. 4617 - 4617
Published: Sept. 14, 2024
This
review
analyzes
in
detail
the
topic
of
supercapacitors
based
on
biochar
technologies,
including
their
advantages,
disadvantages,
and
development
potential.
The
main
is
formation
precursors
process
pyrolysis
activation,
possibility
application
itself
various
fields
brought
closer.
structure,
division,
principle
operation
supercondensates
are
discussed,
where
good
bad
sides
pointed
out.
current
state
scientific
legal
knowledge
biocarbon
its
applications
verified,
results
many
authors
compared
to
examine
level
research
electrodes
created
from
lignocellulosic
biomass.
Current
sites
for
transportation,
electronics,
power
generation
(conventional
unconventional)
also
examined,
as
potential
further
technology
under
discussion.
Journal of Materials Informatics,
Journal Year:
2024,
Volume and Issue:
4(4)
Published: Oct. 24, 2024
Porous
carbon
materials
(PCMs)
are
preferred
as
electrode
for
supercapacitor
energy
storage
applications
due
to
their
superior
characteristics.
However,
the
optimal
performance
of
these
electrodes
requires
trial
and
error
experimental
exploration
complexity
influencing
factors.
To
address
this
limitation,
we
develop
a
machine
learning
(ML)
combined
validation
approach
predict,
screen
interpret
ideal
PCMs
supercapacitors.
Four
ML
models
used
predicting
specific
capacitance
(SC)
properties
light
gradient
boosting
(LGBM)
model
exhibits
best
prediction
with
an
R2
value
0.92.
Through
comprehensive
interpretability
analysis
ML,
important
variables
SC
identified
impact
range
is
determined.
By
analyzing
deviation
key
values
during
verification,
accurate
predictions
made,
facilitating
precise
material
screening.
Additionally,
accuracy
applicability
evaluated.
This
research
pioneered
eigenvalue
fall-point
screening
based
on
combination
experiments
accurately
materials,
providing
compelling
strategy
advancing
technology.
