Environmental Science & Technology,
Год журнала:
2024,
Номер
58(15), С. 6628 - 6636
Опубликована: Март 18, 2024
Biomass
waste-derived
engineered
biochar
for
CO2
capture
presents
a
viable
route
climate
change
mitigation
and
sustainable
waste
management.
However,
optimally
synthesizing
them
enhanced
performance
is
time-
labor-intensive.
To
address
these
issues,
we
devise
an
active
learning
strategy
to
guide
expedite
their
synthesis
with
improved
adsorption
capacities.
Our
framework
learns
from
experimental
data
recommends
optimal
parameters,
aiming
maximize
the
narrow
micropore
volume
of
biochar,
which
exhibits
linear
correlation
its
capacity.
We
experimentally
validate
predictions,
are
iteratively
leveraged
subsequent
model
training
revalidation,
thereby
establishing
closed
loop.
Over
three
cycles,
synthesized
16
property-specific
samples
such
that
uptake
nearly
doubled
by
final
round.
demonstrate
data-driven
workflow
accelerate
development
high-performance
broader
applications
as
functional
material.
Precision Chemistry,
Год журнала:
2024,
Номер
2(7), С. 300 - 329
Опубликована: Март 29, 2024
The
electrochemical
properties
of
2D
materials,
particularly
transition
metal
dichalcogenides
(TMDs),
hinge
on
their
structural
and
chemical
characteristics.
To
be
practically
viable,
achieving
large-scale,
high-yield
production
is
crucial,
ensuring
both
quality
suitability
for
applications
in
energy
storage,
electrocatalysis,
potential-based
ionic
sieving
membranes.
A
prerequisite
success
a
deep
understanding
the
synthesis
process,
forming
critical
link
between
materials
performance.
This
review
extensively
examines
liquid-phase
exfoliation
technique,
providing
insights
into
potential
advancements
strategies
to
optimize
TMDs
nanosheet
yield
while
preserving
attributes.
primary
goal
compile
techniques
enhancing
through
direct
exfoliation,
considering
parameters
like
solvents,
surfactants,
centrifugation,
sonication
dynamics.
Beyond
addressing
yield,
emphasizes
impact
these
TMD
nanosheets,
highlighting
pivotal
role
applications.
Acknowledging
evolving
research
methodologies,
explores
integrating
machine
learning
data
science
as
tools
relationships
key
Envisioned
advance
material
research,
including
optimization
graphene,
MXenes,
applications,
this
compilation
charts
course
toward
data-driven
techniques.
By
bridging
experimental
approaches,
it
promises
reshape
landscape
knowledge
electrochemistry,
offering
transformative
resource
academic
community.
Journal of Materials Chemistry A,
Год журнала:
2024,
Номер
12(11), С. 6211 - 6242
Опубликована: Янв. 1, 2024
This
paper
describes
the
progress
and
future
challenges
in
one-step
carbonization
activation
of
biomass
to
porous
carbons
for
diverse
energy
applications
terms
CO
2
capture,
storage
conversion.
Environmental Science & Technology,
Год журнала:
2024,
Номер
58(15), С. 6628 - 6636
Опубликована: Март 18, 2024
Biomass
waste-derived
engineered
biochar
for
CO2
capture
presents
a
viable
route
climate
change
mitigation
and
sustainable
waste
management.
However,
optimally
synthesizing
them
enhanced
performance
is
time-
labor-intensive.
To
address
these
issues,
we
devise
an
active
learning
strategy
to
guide
expedite
their
synthesis
with
improved
adsorption
capacities.
Our
framework
learns
from
experimental
data
recommends
optimal
parameters,
aiming
maximize
the
narrow
micropore
volume
of
biochar,
which
exhibits
linear
correlation
its
capacity.
We
experimentally
validate
predictions,
are
iteratively
leveraged
subsequent
model
training
revalidation,
thereby
establishing
closed
loop.
Over
three
cycles,
synthesized
16
property-specific
samples
such
that
uptake
nearly
doubled
by
final
round.
demonstrate
data-driven
workflow
accelerate
development
high-performance
broader
applications
as
functional
material.
