Recent
research
on
the
electrocatalytic
CO2
reduction
reaction
(eCO2RR)
has
garnered
significant
attention
given
its
capability
to
address
environmental
issues
associated
with
emissions
while
harnessing
clean
energy
produce
high-value-added
products.
Compared
C1
products,
C2+
products
provide
greater
densities
and
are
highly
sought
after
as
chemical
feedstocks.
However,
formation
of
C-C
bond
is
challenging
due
competition
H-H
C-H
bonds.
Therefore,
elevate
selectivity
yield
fuels,
it
essential
develop
more
advanced
electrocatalysts
optimize
design
electrochemical
cell
configurations.
Of
materials
investigated
for
CO2RR,
Cu-based
stand
out
their
wide
availability,
affordability,
compatibility.
Moreover,
catalysts
exhibit
promising
capabilities
in
adsorption
activation,
facilitating
compounds
via
coupling.
This
review
examines
recent
both
cells
electroreduction
compounds,
introducing
core
principles
eCO2RR
pathways
involved
generating
A
key
focus
categorization
catalyst
designs,
including
defect
engineering,
surface
modification,
nanostructure
tandem
catalysis.
By
analyzing
studies
catalysts,
we
aim
elucidate
mechanisms
behind
enhanced
compounds.
Additionally,
various
types
electrolytic
discussed.
Lastly,
prospects
limitations
utilizing
highlighted
future
research.
Deleted Journal,
Год журнала:
2024,
Номер
1(3), С. 100029 - 100029
Опубликована: Июль 19, 2024
Electrocatalytic
conversion
of
CO2
into
valuable
products
is
a
promising
approach
toward
mitigating
climate
change
and
energy
crisis.
However,
the
product
diversity
multi-electron
transfer
pathways
drive
development
numerous
strategies
for
catalyst
component
active
site
modifications,
leading
to
long
journey
rational
electrocatalyst
design.
The
integration
machine
learning
(ML)
with
experimental
workload
provides
an
opportunity
speed
up
materials
discovery
by
automatically
exploiting
trends
patterns
from
database.
This
review
focuses
on
interpretability
ML
models
in
design,
demonstrates
reliable
workflow
based
adequate
catalytic
data
refined
descriptors,
satisfactory
configuration
model
appropriate
human
intervention.
Moreover,
combination
data-driven
techs
cutting-edge
methodologies
also
discussed,
which
can
serve
as
bridge
between
contemporary
catalysis
quantum
chemistry.
may
provoke
more
ML-based
innovations
rationalization
design
novel
net-zero
industries.
