Molecules,
Journal Year:
2022,
Volume and Issue:
27(14), P. 4489 - 4489
Published: July 13, 2022
The
aim
of
this
work
was
to
develop
a
simple
and
easy-to-apply
model
predict
the
pH
values
deep
eutectic
solvents
(DESs)
over
wide
range
that
can
be
used
in
daily
work.
For
purpose,
38
different
DESs
were
measured
(ranging
from
0.36
9.31)
mathematically
interpreted.
To
mathematical
models,
first
numerically
described
using
σ
profiles
generated
with
COSMOtherm
software.
After
DESs’
description,
following
models
used:
(i)
multiple
linear
regression
(MLR),
(ii)
piecewise
(PLR),
(iii)
artificial
neural
networks
(ANNs)
link
experimental
descriptors.
Both
PLR
ANN
found
applicable
very
high
goodness
fit
(R2independent
validation
>
0.8600).
Due
good
correlation
predicted
values,
profile
could
as
DES
molecular
descriptor
for
prediction
their
values.
ACS Omega,
Journal Year:
2023,
Volume and Issue:
8(14), P. 13177 - 13191
Published: March 30, 2023
One
of
the
most
commonly
used
molecular
inputs
for
ionic
liquids
and
deep
eutectic
solvents
(DESs)
in
literature
are
critical
properties
acentric
factors,
which
can
be
easily
determined
using
modified
Lydersen-Joback-Reid
(LJR)
method
with
Lee-Kesler
mixing
rules.
However,
is
generally
applicable
only
to
binary
mixtures
DESs.
Nevertheless,
ternary
DESs
considered
more
interesting
may
provide
further
tailorability
developing
task-specific
particular
applications.
Therefore,
this
work,
a
new
framework
estimating
factor
based
on
their
structures
presented
by
adjusting
reported
an
extended
version
The
was
applied
data
set
consisting
87
334
distinct
compositions.
For
validation,
estimated
factors
were
predict
densities
results
showed
excellent
agreement
between
experimental
calculated
data,
average
absolute
relative
deviation
(AARD)
5.203%
5.712%
260
(573
compositions).
developed
methodology
incorporated
into
user-friendly
Excel
worksheet
computing
any
or
DES,
provided
Supporting
Information.
This
work
promotes
creation
robust,
accessible,
models
capable
predicting
properties,
thus
saving
time
resources.
ACS Omega,
Journal Year:
2023,
Volume and Issue:
8(29), P. 26533 - 26547
Published: July 12, 2023
Monosaccharides
play
a
vital
role
in
the
human
diet
due
to
their
interesting
biological
activity
and
functional
properties.
Conventionally,
sugars
are
extracted
using
volatile
organic
solvents
(VOCs).
Deep
eutectic
(DESs)
have
recently
emerged
as
new
green
alternative
VOCs.
Nonetheless,
selection
criterion
of
an
appropriate
DES
for
specific
application
is
very
difficult
task
designer
nature
these
theoretically
infinite
number
combinations
constituents
compositions.
This
paper
presents
framework
screening
large
monosaccharide
extraction
COSMO-RS.
The
employs
coefficients
at
dilution
(γi∞)
measure
glucose
fructose
solubility.
Moreover,
toxicity
analysis
considered
ensure
that
selected
safe
work
with.
Finally,
obtained
viscosity
predictions
were
used
select
DESs
not
transport-limited.
To
provide
more
insights
into
which
groups
responsible
effective
extraction,
structure-solubility
was
carried
out.
Based
on
212
constituents,
top-performing
hydrogen
bond
acceptors
found
be
carnitine,
betaine,
choline
chloride,
while
donors
oxalic
acid,
ethanolamine,
citric
acid.
A
research
initiative
presented
this
develop
robust
computational
frameworks
selecting
optimal
given
design
strategy
can
aid
development
novel
processes
DESs.
Journal of Materials Chemistry A,
Journal Year:
2023,
Volume and Issue:
12(4), P. 2209 - 2236
Published: Dec. 11, 2023
This
study
employs
various
machine
learning
algorithms
to
model
the
electrical
conductivity
and
gas
sensing
responses
of
polyaniline/graphene
(PANI/Gr)
nanocomposites
based
on
a
comprehensive
dataset
gathered
from
over
100
references.
ACS Sustainable Chemistry & Engineering,
Journal Year:
2024,
Volume and Issue:
12(21), P. 7987 - 8000
Published: May 13, 2024
Deep
eutectic
solvents
(DESs)
are
gaining
recognition
as
environmentally
friendly
solvent
alternatives
for
diverse
chemical
processes.
Yet,
designing
DESs
tailored
to
specific
applications
is
a
resource-intensive
task,
which
requires
an
accurate
estimation
of
their
physicochemical
properties.
Among
them,
viscosity
crucial,
it
often
dictates
DES's
suitability
solvent.
In
this
study,
artificial
neural
network
(ANN)
introduced
accurately
describe
the
and
mixtures
with
cosolvents.
The
ANN
utilizes
molecular
parameters
derived
from
σ-profiles,
computed
using
conductor-like
screening
model
real
segment
activity
coefficient
(COSMO-SAC).
data
set
comprises
1891
experimental
measurements
48
based
on
choline
chloride,
encompassing
279
different
compositions,
along
1618
points
DES
cosolvents
water,
methanol,
isopropanol,
dimethyl
sulfoxide,
covering
wide
range
0.3862
4722
mPa
s.
optimal
structure
describing
logarithmic
configured
9-19-16-1,
achieving
overall
average
absolute
relative
deviation
1.6031%.
More
importantly,
shows
remarkable
extrapolation
capacity,
capable
predicting
systems
including
(ethanol)
hydrogen
bond
donors
(2,3-butanediol)
not
considered
in
training.
also
demonstrates
extensive
applicability
domain,
94.17%
entire
database.
These
achievements
represent
significant
step
forward
developing
robust,
open
source,
highly
models
descriptors.
