ACS Materials Letters,
Год журнала:
2024,
Номер
6(12), С. 5348 - 5353
Опубликована: Ноя. 6, 2024
The
rational
design
of
organic
ligands
is
crucial
in
the
development
new
metal–organic
frameworks
(MOFs)
to
enrich
structural
diversity
and
application
potential
MOFs.
For
example,
tuning
ligand
symmetry
has
been
widely
utilized
construction
MOFs
with
specific
properties.
Herein,
a
novel
Y-based
MOF
(denoted
as
NU-60)
was
generated
by
selecting
tritopic
carboxylate
reduced
symmetry.
Compared
its
counterpart
relatively
high
symmetry,
NU-60
(3,12)-connected
topology,
highlighting
power
desymmetrization
method
diversity.
Gas
adsorption
studies
demonstrated
that
propane-selective
adsorbent
for
efficient
propane/propylene
separation.
Metal–organic
frameworks
(MOFs)
have
garnered
significant
attention
in
the
field
of
catalysis
due
to
their
unique
advantages
such
as
diverse
coordination
geometry,
variable
metal
nodes,
and
organic
linkers,
facilitating
precise
structural
compositional
control
for
achieving
programmable
catalytic
functionalities.
Although
inherent
microporous
structure
could
provide
excellent
shape
selectivity
during
catalysis,
it
typically
impedes
mass
transfer
process,
thereby
reducing
use
internal
active
sites
overall
efficiency.
Additionally,
employing
single
MOFs
catalysts
presents
challenges
complex
reactions
that
require
multifunctional
sites.
In
recent
years,
considerable
research
efforts
focused
on
designing
constructing
hierarchical
nanostructured
alleviate
substrate
diffusion
limitations
by
introducing
secondary
nanopores,
shortening
distances
via
construction
low-dimensional
nanoarchitectures,
integrating
distinct
with
suitable
functions.
This
review
provides
a
comprehensive
overview
design,
synthesis
methods,
formation
mechanisms
MOF-based
nanostructures
years.
Subsequently,
further
highlights
applications
thermal
electrocatalysis,
photocatalysis,
along
relationship
between
performances.
Finally,
an
outlook
potential
development
directions
hierarchically
structured
MOF
nanocatalysts.
JACS Au,
Год журнала:
2024,
Номер
4(8), С. 3170 - 3182
Опубликована: Авг. 12, 2024
In
this
study,
we
present
the
first
example
of
using
a
machine
learning
(ML)-assisted
design
strategy
to
optimize
synthesis
formulation
enzyme/ZIFs
(zeolitic
imidazolate
framework)
for
enhanced
performance.
Glucose
oxidase
(GOx)
and
horseradish
peroxidase
(HRP)
were
chosen
as
model
enzymes,
while
Zn(eIM)2
(eIM
=
2-ethylimidazolate)
was
selected
ZIF
test
our
ML-assisted
workflow
paradigm.
Through
an
iterative
ML-driven
training-design-synthesis-measurement
workflow,
efficiently
discovered
GOx/ZIF
(G151)
HRP/ZIF
(H150)
with
their
overall
performance
index
(OPI)
values
(OPI
represents
product
encapsulation
efficiency
(E
in
%),
retained
enzymatic
activity
(A
thermal
stability
(T
%))
at
least
1.3
times
higher
than
those
systematic
seed
data
studies.
Furthermore,
advanced
statistical
methods
derived
from
trained
random
forest
qualitatively
quantitatively
reveal
relationship
among
synthesis,
structure,
enzyme/ZIF
system,
offering
valuable
guidance
future
studies
on
enzyme/ZIFs.
Overall,
proposed
holds
promise
accelerating
development
other
enzyme
immobilization
systems
biocatalysis
applications
beyond,
including
drug
delivery
sensing,
others.
Inorganic Chemistry,
Год журнала:
2024,
Номер
63(35), С. 16418 - 16428
Опубликована: Авг. 20, 2024
Lanthanide
metal-organic
frameworks
(Ln-MOFs)
have
excellent
optical
properties
and
structural
diversity,
providing
a
unique
platform
for
the
development
of
fluorescent
sensing
materials.
In
work
described
herein,
series
isostructural
3D
Ln-MOFs
[Ln(L)(H
Advanced Energy Materials,
Год журнала:
2024,
Номер
unknown
Опубликована: Сен. 17, 2024
Abstract
Photocatalysis
and
electrocatalysis
have
emerged
as
promising
technologies
for
addressing
the
energy
crisis
environmental
issues.
However,
widespread
application
of
these
is
hampered
by
challenge
scaling
up
production
photo/electrocatalysts
that
are
not
only
highly
active
stable
but
also
cost‐effective
environmentally
benign.
This
review
delves
into
latest
advancements
in
large‐scale
synthesis
photo/electrocatalysts.
The
factors
to
be
considered
catalysts
discussed
first.
methods
batch
preparation
then
comprehensively
introduced,
with
a
thorough
discussion
their
respective
advantages
limitations.
Moreover,
data
analysis
via
machine
learning
techniques,
which
accelerates
identification
refinement
potential
new
offers
insights
enhancing
high‐throughput
catalysts,
introduced
detail.
Then
representative
examples
presented
illustrate
applications
field
industrial‐level
photo/electrocatalysis.
Finally,
challenges
prospects
development
discussed.
By
bridging
gap
between
laboratory
research
industrial
application,
this
aims
provide
reference
future
sustainable
conversion
beyond.
Advanced Functional Materials,
Год журнала:
2024,
Номер
unknown
Опубликована: Окт. 4, 2024
Abstract
Separating
propylene
(C
3
H
6
)
from
propylene/propane
/C
8
mixture
using
energy‐efficient
adsorption
is
industrially
important,
but
due
to
the
lack
of
universal
pore
features,
rational
selection
a
suitable
adsorbent
in
ocean
porous
materials
tough
task.
In
this
study,
comprehensive
work
on
discovery
high‐performance
C
separation
adsorbents
carried
out
by
utilizing
advantages
high‐throughput
computational
screening
(HTCS).
First,
based
HTCS
data
mining
CoRE
MOF
2019
and
Tobacco
3.0
database,
target
material,
Cd‐HFDPA,
screened
out.
Second,
electrostatic
potential
(ESP)
analysis
shows
that
Cd‐HFDPA
has
obvious
characteristics
high
affinity
for
according
ESP
matching,
which
further
confirmed
isotherms,
Ideal
Adsorbed
Solution
Theory
selectivity,
enthalpy
analyses,
breakthrough
experiments.
Finally,
an
industrial
two‐bed
pressure
swing
process
proposed
its
productivity
energy
consumption
are
compared
with
other
benchmark
materials.
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.
Abstract
Purifying
ultra‐high
purity
propylene
(>99.995%)
with
an
energy‐efficient
adsorptive
separation
method
is
a
promising
yet
challenging
technology
that
remains
unfulfilled.
Instead
of
solely
considering
the
effect
adsorbents
on
guest
molecules,
we
propose
synergistic
adsorption
mechanism
for
deep
removal
propane
and
propyne,
utilizing
supramolecular
interactions
in
both
“host‐guest”
“guest‐guest”
systems.
Through
modulation
pore
environment,
Ni‐DMOF‐DM
exhibits
exceptionally
high
capacities
propyne
(171
197
cm
3
/g
at
ambient
temperature
pressure,
respectively),
unprecedented
propane/propylene
selectivity
(2.74).
Theoretical
calculations
confirm
geometric
C‐H···π
bonds
C‐H···O
hydrogen
resulting
from
host‐guest
interactions,
alongside
C‐H···H
guest‐guest
within
confined
space.
Breakthrough
experiments
demonstrated
(propane
<
0.005%
1.0
ppm)
can
be
directly
collected
ternary
mixtures
Ni‐DMOF‐DM,
achieving
productivity
up
to
152.14
L/kg.
