The
widespread
application
of
metal-organic
frameworks
(MOFs)
in
wastewater
and
gas
treatment
has
created
an
increasing
demand
for
accurate
rapid
assessment
their
BET
specific
surface
area.
However,
experimental
methods
acquiring
sufficient
statistical
data
are
often
costly
time-consuming.
Therefore,
this
study
proposes
a
dual-stage
stacking
model
with
Gaussian
mixture
model-virtual
sample
generation
(GMM-VSG)
technology
the
area
prediction.
In
study,
90
real
samples
were
selected
from
MOF
database
300
virtual
generated.
performance
on
both
was
evaluated
by
using
four
machine
learning
models,
including
Bayesian
regression
(Bayes),
adaptive
boosting
(AdaBoost),
random
forest
(RF),
extreme
gradient
(XGBoost).
Subsequently,
three
best-performing
models
linear
constructing
two-stage
model,
R2
value
0.974.
Finally,
conditions
adjusted
based
feature
importance
analysis
during
validation
process,
result
shows
that
prediction
accuracy
is
0.943.
This
contributes
to
development
more
efficient
evaluation
methods.
Ultrasonics Sonochemistry,
Год журнала:
2024,
Номер
107, С. 106912 - 106912
Опубликована: Май 17, 2024
The
United
Nations'
Sustainable
Development
Goals
(SDGs)
are
significant
in
guiding
modern
scientific
research.
In
recent
years,
scholars
have
paid
much
attention
to
MOFs
materials
as
green
materials.
However,
piezo
catalysis
of
has
not
been
widely
studied.
Piezoelectric
can
convert
mechanical
energy
into
electrical
energy,
while
effective
photocatalysts
for
removing
pollutants.
Therefore,
it
is
crucial
design
with
piezoelectric
properties
and
photosensitivity.
this
study,
sulfur-functionalized
metal-organic
frameworks
(S-MOFs)
were
prepared
using
organic
ligand
(H
C8
aromatic
isomers,
namely
para-xylene
(PX),
meta-xylene
(MX),
ortho-xylene
(OX),
and
ethylbenzene
(EB),
are
essential
industrial
chemicals
with
a
wide
range
of
applications.
The
effective
separation
these
isomers
is
crucial
across
various
sectors,
including
petrochemicals,
pharmaceuticals,
polymer
manufacturing.
Traditional
methods,
such
as
distillation
solvent
extraction,
energy-intensive.
In
contrast,
selective
adsorption
has
emerged
an
efficient
technique
for
separating
in
which
nanospace
engineering
offers
promising
strategies
to
address
existing
challenges
by
precisely
tailoring
the
structures
properties
porous
materials
at
nanoscale.
This
review
explores
application
modifying
pore
characteristics
diverse
materials─including
zeolites,
metal-organic
frameworks
(MOFs),
covalent
organic
(COFs),
other
substances─to
enhance
their
performance
isomer
separation.
Additionally,
this
provides
comprehensive
summary
how
different
techniques,
temperature
fluctuations,
enthalpy/entropy
considerations,
desorption
processes
influence
efficiency.
It
also
presents
forward-looking
perspective
on
remaining
potential
opportunities
advancing
The
widespread
application
of
metal-organic
frameworks
(MOFs)
in
wastewater
and
gas
treatment
has
created
an
increasing
demand
for
accurate
rapid
assessment
their
BET
specific
surface
area.
However,
experimental
methods
acquiring
sufficient
statistical
data
are
often
costly
time-consuming.
Therefore,
this
study
proposes
a
dual-stage
stacking
model
with
Gaussian
mixture
model-virtual
sample
generation
(GMM-VSG)
technology
the
area
prediction.
In
study,
90
real
samples
were
selected
from
MOF
database
300
virtual
generated.
performance
on
both
was
evaluated
by
using
four
machine
learning
models,
including
Bayesian
regression
(Bayes),
adaptive
boosting
(AdaBoost),
random
forest
(RF),
extreme
gradient
(XGBoost).
Subsequently,
three
best-performing
models
linear
constructing
two-stage
model,
R2
value
0.974.
Finally,
conditions
adjusted
based
feature
importance
analysis
during
validation
process,
result
shows
that
prediction
accuracy
is
0.943.
This
contributes
to
development
more
efficient
evaluation
methods.
