Molecules,
Год журнала:
2022,
Номер
27(14), С. 4489 - 4489
Опубликована: Июль 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.
Chem & Bio Engineering,
Год журнала:
2025,
Номер
2(4), С. 210 - 228
Опубликована: Март 5, 2025
As
the
chemical
industry
shifts
toward
sustainable
practices,
there
is
a
growing
initiative
to
replace
conventional
fossil-derived
solvents
with
environmentally
friendly
alternatives
such
as
ionic
liquids
(ILs)
and
deep
eutectic
(DESs).
Artificial
intelligence
(AI)
plays
key
role
in
discovery
design
of
novel
development
green
processes.
This
review
explores
latest
advancements
AI-assisted
solvent
screening
specific
focus
on
machine
learning
(ML)
models
for
physicochemical
property
prediction
separation
process
design.
Additionally,
this
paper
highlights
recent
progress
automated
high-throughput
(HT)
platforms
screening.
Finally,
discusses
challenges
prospects
ML-driven
HT
strategies
optimization.
To
end,
provides
insights
advance
future
ACS Sustainable Chemistry & Engineering,
Год журнала:
2022,
Номер
11(1), С. 208 - 227
Опубликована: Дек. 23, 2022
Polyhydroxyalkanoates
(PHAs)
are
an
emerging
type
of
bioplastic
that
have
the
potential
to
replace
petroleum-based
plastics.
They
biosynthetizable,
biodegradable,
and
economically
viable
a
range
tunable
properties.
Despite
their
great
potential,
structure
properties
PHA
remain
unexplored
due
theoretically
infinite
chemical
space.
Therefore,
computational
approaches
for
accurate
predictions
various
need
be
developed
effectively
explore
this
large
For
purpose,
work
presents
multitask
artificial
neural
network
(ANN)
capable
predicting
glass
transition
temperature
(Tg)
melting
(Tm)
homopolymers
copolymers.
The
ANN
inputs
included
σProfiles
as
molecular
parameters
describing
monomer
chemistry
composition.
In
contrast,
polymer
weight
(M)
polydispersity
index
(PDI)
were
used
describe
state.
results
showed
after
optimizing
hyperparameters,
selected
architecture
was
remarkable
in
Tg
Tm
with
R2
values
0.979
0.986
average
absolute
relative
deviation
(AARD)
0.476%
0.520%,
respectively.
proposed
model
represents
initiative
promote
development
robust,
open
source,
user-friendly
models
polymers
based
solely
on
(σProfiles),
thereby
saving
time
resources
researchers
worldwide.
framework
described
is
flexible
so
it
can
applied
larger
space
incorporate
other
polymers.
Molecules,
Год журнала:
2022,
Номер
27(14), С. 4489 - 4489
Опубликована: Июль 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.
