Abstract
In
this
report,
a
microporous
metal–organic
framework
of
[Ca(TDC)(DMA)]
n
(
1
)
and
two‐dimensional
coordination
polymer
[Ca(TDC)(DMF)
2
]
),
(TDC
2−
=Thiophene‐2,5‐dicarboxylate,
DMA=N,
N'‐dimethylacetamide
DMF=N,
N'‐dimethylformamide)
based
on
Ca(II)
were
designed
by
the
effect
solvent,
X‐ray
analysis
was
performed
for
single
crystals
.
Then,
compound
synthesized
in
three
different
methods
identified
with
set
analyses.
Compared
to
other
adsorbents,
MOFs
are
widely
used
field
adsorption
separation
various
gases
due
series
distinctive
features
such
as
diverse
adjustable
structures
pores
dimensions,
high
porosity
surface
area
regular
distribution
active
sites.
Therefore,
ability
uptake
(CH
4
,
CO
C
H
2,
N
several
binary
mixtures
(CO
/CH
/N
/H
/C
investigated
using
Grand
Canonical
Monte
Carlo
simulations.
Volumetric
gravimetric
isotherms
operating
conditions,
isosteric
heat
(q
st
chemical
potential
each
thermodynamic
state,
snapshots
during
simulation
process
reported
all
cases.
The
results
obtained
from
indicate
that
has
capacity
(16
mmol
g
−1
(12.5
(6.6
(5
CH
(1.5
at
bar.
It
also
performs
well
separating
mixtures,
which
can
be
attributed
presence
open
metal
sites
nodes
their
electrostatic
tendency
interact
containing
higher
quadrupole
dipole
moment
compared
components
mixture.
Industrial & Engineering Chemistry Research,
Год журнала:
2023,
Номер
63(1), С. 37 - 48
Опубликована: Дек. 25, 2023
The
existence
of
a
very
large
number
porous
materials
is
great
opportunity
to
develop
innovative
technologies
for
carbon
dioxide
(CO2)
capture
address
the
climate
change
problem.
On
other
hand,
identifying
most
promising
adsorbent
and
membrane
candidates
using
iterative
experimental
testing
brute-force
computer
simulations
challenging
due
enormous
variety
materials.
Artificial
intelligence
(AI)
has
recently
been
integrated
into
molecular
modeling
materials,
specifically
metal–organic
frameworks
(MOFs),
accelerate
design
discovery
high-performing
adsorbents
membranes
CO2
adsorption
separation.
In
this
perspective,
we
highlight
pioneering
works
in
which
AI,
simulations,
experiments
have
combined
produce
exceptional
MOFs
MOF-based
composites
that
outperform
traditional
capture.
We
outline
future
directions
by
discussing
current
opportunities
challenges
field
harnessing
experiments,
theory,
AI
accelerated
Langmuir,
Год журнала:
2024,
Номер
40(42), С. 21957 - 21975
Опубликована: Окт. 9, 2024
Metal–organic
frameworks
(MOFs)
are
a
class
of
hybrid
porous
materials
that
have
gained
prominence
as
noteworthy
material
with
varied
applications.
Currently,
MOFs
in
extensive
use,
particularly
the
realms
energy
and
catalysis.
The
synthesis
these
poses
considerable
challenges,
their
computational
analysis
is
notably
intricate
due
to
complex
structure
versatile
applications
field
science.
Density
functional
theory
(DFT)
has
helped
researchers
understanding
reactions
mechanisms,
but
it
costly
time-consuming
requires
bigger
systems
perform
calculations.
Machine
learning
(ML)
techniques
were
adopted
order
overcome
problems
by
implementing
ML
data
sets
for
synthesis,
structure,
property
predictions
MOFs.
These
fast,
efficient,
accurate
do
not
require
heavy
computing.
In
this
review,
we
discuss
models
used
MOF
incorporation
artificial
intelligence
(AI)
predictions.
advantage
AI
would
accelerate
research,
synthesizing
novel
multiple
properties
oriented
minimum
information.
Green Chemical Engineering,
Год журнала:
2024,
Номер
unknown
Опубликована: Июль 1, 2024
Ionic
liquid
(IL)
can
be
inserted
into
metal
organic
framework
(MOF)
to
form
IL@MOF
composite
with
enhanced
properties.
In
this
work,
hypothetical
IL@MOFs
were
computationally
constructed
and
screened
by
integrating
molecular
simulation
convolutional
neural
network
(CNN)
for
CO2
capture.
First,
the
IL
[BMIM][DCA]
a
large
solubility
was
1631
pre-selected
Computational-Ready
Experimental
(CoRE)
MOFs
create
IL@MOFs.
Then,
given
temperature
pressure
of
adsorption
desorption,
CO2/N2
selectivity
working
capacity
700
representative
assessed
via
simulations.
Based
on
results,
two
CNN
models
trained
used
predict
performance
other
IL@MOFs,
which
reduces
computational
costs
effectively.
By
combining
results
model
predictions,
22
top-ranked
identified.
Three
distinct
ones
IL@HABDAS,
IL@GUBKUL,
IL@MARJAQ
chosen
explicit
analysis.
It
found
that
desired
balance
between
obtained
inserting
optimal
number
molecules.
This
helps
guide
novel
design
composites
advanced
carbon
