AIChE Journal,
Journal Year:
2024,
Volume and Issue:
70(11)
Published: Aug. 19, 2024
Abstract
Ionic
liquids
(ILs)
are
promising
solvents
for
separating
aromatics
from
fuel
oils.
However,
studies
separate
polycyclic
with
ILs
rare
and
insufficient,
the
impact
of
solute
structure
on
extraction
performance
still
needs
to
be
determined.
In
this
work,
we
use
1‐ethyl‐3‐methylimidazolium
bis([trifluoromethyl]sulfonyl)imide
([EMIM][NTF
2
])
as
an
extractant
1‐methylnaphthalene,
quinoline,
benzothiophene
dodecane
mixtures.
Liquid–liquid
equilibrium
experiments
identified
optimal
operating
conditions.
Nine
molecules,
including
five
alkanes
four
aromatic
hydrocarbons,
were
used
study
relationship
between
structure.
Molecular
dynamics
simulation
quantum
chemistry
calculations
gave
a
deep
insight
reasonable
interpretation
structure‐performance
at
molecular
level.
An
industrial‐scale
process
was
proposed.
The
IL
can
easily
regenerated
using
heptane
back‐extractive
solvent.
A
high‐purity
oil
content
below
0.5
wt%
is
obtained
after
8‐stage
extraction.
ChemCatChem,
Journal Year:
2024,
Volume and Issue:
16(22)
Published: Aug. 6, 2024
Abstract
The
extensive
combustion
of
fossil
fuels
results
in
excessive
release
carbon
dioxide
(CO
2
),
causing
a
global
environmental
crisis.
It
is
imperative
to
develop
sustainable
methods
for
converting
CO
into
renewable
energy
sources.
Electrochemical
reduction
RR)
offers
the
potential
generate
valuable
chemicals,
including
C1
products
(e.
g.,
monoxide,
methane,
etc.)
and
C2+
ethene,
ethanol,
acetic
acid,
propyl
alcohol,
etc.).
Copper‐based
(Cu‐based)
catalysts
show
promise
producing
value‐added
products,
but
they
face
challenges
like
low
selectivity
stability.
catalytic
performances
Cu‐based
can
be
promoted
through
electronic
structure
adjustment,
selective
crystal
exposure,
as
well
molecular
additive
approaches.
Ionic
liquids
(ILs),
known
their
strong
adsorption
capacity,
adjustable
hydrophobicity,
wide
chemical
window,
hold
significant
addressing
current
associated
with
catalysts.
This
review
provides
comprehensive
overview
structural
characterization
mechanisms
ILs
used
RR
systems.
Additionally,
it
suggestions
future
research
avenues
regarding
IL‐functionalized
Cu
Green Chemical Engineering,
Journal Year:
2024,
Volume and Issue:
unknown
Published: June 1, 2024
Advanced
technologies
like
deep
learning
have
accelerated
the
discovery
of
novel
chemical
reactions,
especially
in
field
organic
synthesis.
With
hundreds
thousands
reactions
available
for
reference,
one
way
to
effectively
leverage
them
is
by
classifying
into
different
clusters
based
on
their
specific
characteristics,
which
makes
target-guided
navigation
vast
space
possible.
Although
previous
attempts
that
apply
reaction
classification
tasks
made
substantial
progress,
developing
a
model
with
good
interpretability
as
well
high
accuracy
large-scale
remains
an
open
question.
In
this
work,
learning-based
task
first
constructed
utilizing
pre-trained
BERT
and
autoencoder.
Then,
trained
under
open-source
dataset
USPTO_TPL
contains
recorded
up
1000
types.
The
multi-classification
testing
99.382%,
showing
its
great
potential
practical
use.
Besides,
similarity
map
presented
correlate
sigma-profile-based
statistical
features.
Finally,
representative
from
are
provided
illustrate
model's
effectiveness
task.
