Applied and Computational Engineering,
Journal Year:
2025,
Volume and Issue:
123(1), P. 119 - 133
Published: Jan. 7, 2025
Decarbonization
and
development
of
clean
energy
has
long
been
a
challenge
require
the
reconstruction
consumption.
Hydrogen
emerges
as
promising
alternative
source
due
to
its
high
hear
capacity
negligible
carbon
emission.
However,
there
exist
lack
effective
efficient
approach
store,
transport,
recover
hydrogen
for
utilization.
Solid
materials
hold
promises
resolve
challenge.
Metal-organic
frameworks
(MOFs)
is
class
hybrid
organic
inorganic
that
exhibit
porosity
diverse
chemistry
rendering
them
suitable
gas
storage
separation.
Screening
optimal
MOFs
becomes
persistent
research
pursuit
infinite
number
MOF
materials.
Herein,
we
propose
computational
data-drive
throughput
screening
pipeline
storage.
We
generated
over
database
with
2000
prototypes
optimized
structures.
The
capacities
these
were
predicted
identification
top-performing
investigation
structure-property
relationships.
identify
top
candidates
superior
performance
than
reference
synthesizability.
relationship
between
metal
chemistry,
topology
revealed
future
experimental
guidance.
Journal of the American Chemical Society,
Journal Year:
2024,
Volume and Issue:
146(10), P. 6955 - 6961
Published: Feb. 29, 2024
Machine
learning
is
gaining
momentum
in
the
prediction
and
discovery
of
materials
for
specific
applications.
Given
abundance
metal–organic
frameworks
(MOFs),
computational
screening
existing
MOFs
propane/propylene
(C3H8/C3H6)
separation
could
be
equally
important
developing
new
MOFs.
Herein,
we
report
a
machine
learning-assisted
strategy
C3H8-selective
from
CoRE
MOF
database.
Among
four
algorithms
applied
learning,
random
forest
(RF)
algorithm
displays
highest
degree
accuracy.
We
experimentally
verified
identified
top-performing
(JNU-90)
with
its
benchmark
selectivity
performance
directly
producing
C3H6.
Considering
excellent
hydrolytic
stability,
JNU-90
shows
great
promise
energy-efficient
C3H8/C3H6.
This
work
may
accelerate
development
challenging
separations.
Nano-Micro Letters,
Journal Year:
2025,
Volume and Issue:
17(1)
Published: Jan. 27, 2025
Abstract
Compared
with
Zn
2+
,
the
current
mainly
reported
charge
carrier
for
zinc
hybrid
capacitors,
small-hydrated-sized
and
light-weight
NH
4
+
is
expected
as
a
better
one
to
mediate
cathodic
interfacial
electrochemical
behaviors,
yet
has
not
been
unraveled.
Here
we
propose
an
-modulated
cationic
solvation
strategy
optimize
spatial
distribution
achieve
dynamic
/NH
co-storage
boosting
Zinc
capacitors.
Owing
hierarchical
solvated
structure
in
Zn(CF
3
SO
)
2
–NH
CF
electrolyte,
high-reactive
small-hydrate-sized
(H
O)
induce
Helmholtz
plane
reconfiguration,
thus
effectively
enhancing
density
activate
20%
capacity
enhancement.
Furthermore,
adsorbed
hydrated
ions
afford
high-kinetics
ultrastable
C‧‧‧H
(NH
storage
process
due
much
lower
desolvation
energy
barrier
compared
heavy
rigid
Zn(H
6
(5.81
vs.
14.90
eV).
Consequently,
physical
uptake
multielectron
redox
of
carbon
cathode
enable
capacitor
deliver
high
(240
mAh
g
−1
at
0.5
A
),
large-current
tolerance
(130
50
ultralong
lifespan
(400,000
cycles).
This
study
gives
new
insights
into
design
cathode–electrolyte
interfaces
toward
advanced
zinc-based
storage.
Advanced Materials,
Journal Year:
2024,
Volume and Issue:
unknown
Published: July 27, 2024
Abstract
Metal–organic
frameworks
(MOFs),
renowned
for
their
exceptional
porosity
and
crystalline
structure,
stand
at
the
forefront
of
gas
adsorption
separation
applications.
Shortly
after
discovery
through
experimental
synthesis,
computational
simulations
quickly
become
an
important
method
in
broadening
use
MOFs
by
offering
deep
insights
into
structural,
functional,
performance
properties.
This
review
specifically
addresses
pivotal
role
molecular
enlarging
understanding
enhancing
applications,
particularly
adsorption.
After
reviewing
historical
development
implementation
simulation
methods
field
MOFs,
high‐throughput
screening
(HTCS)
studies
used
to
unlock
potential
CO
2
capture,
CH
4
storage,
H
water
harvesting
are
visited
recent
advancements
these
applications
highlighted.
The
transformative
impact
integrating
artificial
intelligence
with
HTCS
on
prediction
MOFs’
directing
efforts
promising
materials
is
addressed.
An
outlook
current
opportunities
challenges
accelerate
finally
provided.
Energies,
Journal Year:
2024,
Volume and Issue:
17(16), P. 4070 - 4070
Published: Aug. 16, 2024
This
review
aims
to
summarize
the
recent
advancements
and
prevailing
challenges
within
realm
of
hydrogen
storage
transportation,
thereby
providing
guidance
impetus
for
future
research
practical
applications
in
this
domain.
Through
a
systematic
selection
analysis
latest
literature,
study
highlights
strengths,
limitations,
technological
progress
various
methods,
including
compressed
gaseous
hydrogen,
cryogenic
liquid
organic
solid
material
storage,
as
well
feasibility,
efficiency,
infrastructure
requirements
different
transportation
modes
such
pipeline,
road,
seaborne
transportation.
The
findings
reveal
that
low
density,
high
costs,
inadequate
persist
despite
high-pressure
liquefaction.
also
underscores
potential
emerging
technologies
innovative
concepts,
metal–organic
frameworks,
nanomaterials,
underground
along
with
synergies
renewable
energy
integration
production
facilities.
In
conclusion,
interdisciplinary
collaboration,
policy
support,
ongoing
are
essential
harnessing
hydrogen’s
full
clean
carrier.
concludes
is
vital
global
transformation
climate
change
mitigation.
Machine
learning
(ML),
as
an
advanced
data
analysis
tool,
simulates
the
process
of
human
brain,
enabling
extraction
features,
discovery
patterns,
and
making
accurate
predictions
or
decisions
from
complex
data.
In
field
nanomaterial
design,
application
ML
technology
not
only
accelerates
performance
optimization
nanomaterials
but
also
promotes
innovation
materials
science
research
methods.
Bibliometrics,
a
method
based
on
quantitative
analysis,
provides
us
with
macro
perspective
to
observe
understand
in
design
by
statistically
analyzing
various
indicators
scientific
literature.
This
paper
quantitatively
analyzes
literature
related
ML-driven
seven
dimensions,
revealing
importance
necessity
design.
It
systematically
diversified
applications
combination
suitable
algorithms
being
key
enhancing
nanomaterials.
addition,
this
discusses
current
challenges
future
development
directions,
including
quality
set
construction,
algorithm
optimization,
deepening
interdisciplinary
cooperation.
review
researchers
state
trends
ideas
suggestions
for
research.
is
significant
value
promoting
progress
fostering
in-depth
research,
accelerating
innovative
material
technologies.
As
the
global
demand
for
clean
and
sustainable
energy
sources
intensifies,
hydrogen
emerges
as
a
promising
alternative
fuel.
The
widespread
adoption
of
hydrogen,
however,
is
impeded
by
lack
efficient
systems
storage
transportation.
This
review
aims
to
summarize
recent
advancements
prevailing
challenges
within
realm
transportation,
thereby
providing
guidance
impetus
future
research
practical
applications
in
this
domain.
Through
systematic
selection
analysis
latest
literature,
study
highlights
strengths,
limitations,
technological
progress
various
methods,
including
compressed
gaseous
cryogenic
liquid
organic
liquids
solid
materials
storage,
well
feasibility,
efficiency,
infrastructure
requirements
different
transportation
modes
such
pipelines,
road
seaborne
findings
reveal
that
low
density,
high
costs,
inadequate
persist
despite
high-pressure
liquefaction.
also
underscores
potential
emerging
technologies
innovative
concepts,
metal-organic
frameworks,
nanomaterials,
underground
along
with
synergies
renewable
integration
production
facilities.
In
conclusion,
interdisciplinary
collaboration,
policy
support,
ongoing
are
essential
harnessing
hydrogen’s
full
carrier.
concludes
vital
transformation
climate
change
mitigation.
Chemistry of Materials,
Journal Year:
2024,
Volume and Issue:
36(19), P. 9013 - 9030
Published: July 22, 2024
Metal–organic
frameworks
(MOFs)
began
to
emerge
over
two
decades
ago,
resulting
in
the
deposition
of
120
000
MOF-like
structures
(and
counting)
into
Cambridge
Structural
Database
(CSD).
Topological
analysis
is
a
critical
step
toward
understanding
periodic
MOF
materials,
offering
insight
design
and
synthesis
these
crystals
via
simplification
connectivity
imposed
on
complete
chemical
structure.
While
some
most
prevalent
topologies,
such
as
face-centered
cubic
(fcu),
square
lattice
(sql),
diamond
(dia),
are
simple
can
be
easily
assigned
structures,
MOFs
that
built
from
complex
building
blocks,
with
multiple
nodes
different
symmetry,
result
difficult
characterize
topological
configurations.
In
representations
diverge
where
definition
linkers
blurred,
especially
for
cases
they
not
immediately
obvious
terms.
Currently,
researchers
have
option
use
software
ToposPro,
MOFid,
CrystalNets
aid
assignment
topology
descriptors
new
existing
MOFs.
These
packages
readily
available
frequently
used
simplify
original
their
basic
before
algorithmically
matching
condensed
database
underlying
mathematical
nets.
approaches
often
require
in-built
bond
algorithms
alongside
rules.
this
Perspective,
we
discuss
importance
within
field
MOFs,
methods
techniques
implemented
by
packages,
availability
limitations
review
uptake
community.
