Cell Reports Physical Science,
Journal Year:
2021,
Volume and Issue:
2(4), P. 100396 - 100396
Published: April 1, 2021
Machine
learning
(ML)
is
emerging
as
a
powerful
approach
that
has
recently
shown
potential
to
affect
various
frontiers
of
carbon
capture,
key
interim
technology
assist
in
the
mitigation
climate
change.
In
this
perspective,
we
reveal
how
ML
implementations
have
improved
process
many
aspects,
for
both
absorption-
and
adsorption-based
approaches,
ranging
from
molecular
level.
We
discuss
role
predicting
thermodynamic
properties
absorbents
improving
absorption
process.
For
adsorption
processes,
promises
techniques
exploring
options
find
most
cost-effective
scheme,
which
involves
choosing
solid
adsorbent
designing
configuration.
also
highlight
advantages
associated
risks,
elaborate
on
importance
features
needed
train
models,
identify
promising
future
opportunities
capture
processes.
Energy & Environmental Science,
Journal Year:
2018,
Volume and Issue:
11(5), P. 1062 - 1176
Published: Jan. 1, 2018
Carbon
capture
and
storage
(CCS)
is
vital
to
climate
change
mitigation,
has
application
across
the
economy,
in
addition
facilitating
atmospheric
carbon
dioxide
removal
resulting
emissions
offsets
net
negative
emissions.
This
contribution
reviews
state-of-the-art
identifies
key
challenges
which
must
be
overcome
order
pave
way
for
its
large-scale
deployment.
Nature Communications,
Journal Year:
2019,
Volume and Issue:
10(1)
Published: Nov. 15, 2019
Abstract
Electrochemical
processes
coupling
carbon
dioxide
reduction
reactions
with
organic
oxidation
are
promising
techniques
for
producing
clean
chemicals
and
utilizing
renewable
energy.
However,
assessments
of
the
economics
technology
remain
questionable
due
to
diverse
product
combinations
significant
process
design
variability.
Here,
we
report
a
technoeconomic
analysis
electrochemical
reaction–organic
reaction
coproduction
via
conceptual
thereby
propose
potential
economic
combinations.
We
first
develop
fully
automated
synthesis
framework
guide
simulations,
which
then
employed
predict
levelized
costs
chemicals.
identify
global
sensitivity
current
density,
Faraday
efficiency,
overpotential
across
295
both
understand
at
various
levels.
The
highlights
promise
that
value-added
can
secure
feasibility.
Chemistry of Materials,
Journal Year:
2018,
Volume and Issue:
30(18), P. 6325 - 6337
Published: Aug. 23, 2018
Open
framework
materials
(OFMs)
such
as
metal–organic
frameworks
(MOFs)
can
provide
structurally
and
chemically
tailorable
nanopores.
This
exceptional
tunability
has
allowed
for
careful
positioning
of
optimal
adsorption
sites
within
MOF
pores
to
enable
selective
CO2
physisorption,
making
these
promising
energy-efficient
capture.
However,
given
the
multitude
features
that
be
simultaneously
altered
thousands
MOFs
synthesized
date,
it
daunting
elucidate
most
critical
boosting
capture
capabilities.
Here
we
use
a
multiscale
approach—density
functional
theory
(DFT),
grand
canonical
Monte
Carlo
(GCMC),
machine
learning
(ML)—to
investigate
role
various
pore
chemical
topological
in
enhancement
metrics
MOFs.
To
thorough
"sweep"
target
region
structure-space,
used
computational
synthesis
methods
create
sets
encompassing
all
possible
combinations
16
topologies
13
functionalized
molecular
building
blocks.
The
pure
CO2,
CO2/H2
CO2/N2
mixtures
resulting
31
parent
its
derivatives
was
then
simulated,
were
calculated.
Functionalization
with
hydroxyl,
thiol,
cyano,
amino,
or
nitro
chemistries
found
often
improve
MOFs,
but
efficacy
this
strategy
depended
strongly
on
topology.
Decision
trees
trained
predict
improvement
decline
upon
functionalization
whereas
five
additional
algorithms
absolute
training
us
determine,
without
human
bias,
relative
importance
structural/topological
factors
capabilities
