Journal of Molecular Liquids,
Journal Year:
2023,
Volume and Issue:
395, P. 123888 - 123888
Published: Dec. 27, 2023
Efficient
drug
delivery
systems
(DDSs)
play
a
pivotal
role
in
ensuring
pharmaceuticals'
targeted
and
effective
administration.
However,
the
intricate
interplay
between
formulations
poses
challenges
their
design
optimization.
Simulations
have
emerged
as
indispensable
tools
for
comprehending
these
interactions
enhancing
DDS
performance
to
address
this
complexity.
This
comprehensive
review
explores
latest
advancements
simulation
techniques
provides
detailed
analysis.
The
encompasses
various
methodologies,
including
molecular
dynamics
(MD),
Monte
Carlo
(MC),
finite
element
analysis
(FEA),
computational
fluid
(CFD),
density
functional
theory
(DFT),
machine
learning
(ML),
dissipative
particle
(DPD).
These
are
critically
examined
context
of
research.
article
presents
illustrative
case
studies
involving
liposomal,
polymer-based,
nano-particulate,
implantable
DDSs,
demonstrating
influential
simulations
optimizing
systems.
Furthermore,
addresses
advantages
limitations
It
also
identifies
future
directions
research
development,
such
integrating
multiple
techniques,
refining
validating
models
greater
accuracy,
overcoming
limitations,
exploring
applications
personalized
medicine
innovative
DDSs.
employing
like
MD,
MC,
FEA,
CFD,
DFT,
ML,
DPD
offer
crucial
insights
into
behaviour,
aiding
Despite
advantages,
rapid
cost-effective
screening,
require
validation
addressing
limitations.
Future
should
focus
on
models,
enhance
outcomes.
paper
underscores
contribution
emphasizing
providing
valuable
facilitating
development
optimization
ultimately
patient
As
we
continue
explore
impact
advancing
discovery
improving
DDSs
is
expected
be
profound.
Advances in Colloid and Interface Science,
Journal Year:
2024,
Volume and Issue:
333, P. 103281 - 103281
Published: Aug. 24, 2024
Growing
concerns
about
environmental
pollution
have
highlighted
the
need
for
efficient
and
sustainable
methods
to
remove
dye
contamination
from
various
ecosystems.
In
this
context,
computational
such
as
molecular
dynamics
(MD),
Monte
Carlo
(MC)
simulations,
quantum
mechanics
(QM)
calculations,
machine
learning
(ML)
are
powerful
tools
used
study
predict
adsorption
processes
of
dyes
on
adsorbents.
These
provide
detailed
insights
into
interactions
mechanisms
involved,
which
can
be
crucial
designing
systems.
MD
detailing
arrangements,
dyes'
behaviour
interaction
energies
with
They
simulate
entire
process,
including
surface
diffusion,
solvent
layer
penetration,
physisorption.
QM
especially
density
functional
theory
(DFT),
determine
structures
reactivity
descriptors,
aiding
in
understanding
mechanisms.
identify
stable
configurations
like
hydrogen
bonding
electrostatic
forces.
MC
simulations
equilibrium
properties
by
sampling
configurations.
ML
proven
highly
effective
predicting
optimizing
processes.
models
offer
significant
advantages
over
traditional
methods,
higher
accuracy
ability
handle
complex
datasets.
optimize
conditions,
clarify
adsorbent
functionalization
roles,
removal
efficiency
under
conditions.
This
research
explores
MD,
MC,
QM,
approaches
connect
macroscopic
phenomena.
Probing
these
techniques
provides
energetics
pollutants
surfaces.
The
findings
will
aid
developing
new
materials
removal.
review
has
implications
remediation,
offering
a
comprehensive
at
scales.
Merging
microscopic
data
observations
enhances
knowledge
pollutant
adsorption,
laying
groundwork
efficient,
technologies.
Addressing
growing
challenges
ecosystem
protection,
contributes
cleaner,
more
future.
•
Enviro
concern
drives
eco-friendly
Computation
unveils
Study
bridges
dynamics,
Carlo,
mechanics.
Insights
inform
novel
adsorbents
Integration
shapes
greener
solutions.
Journal of Molecular Liquids,
Journal Year:
2024,
Volume and Issue:
410, P. 125592 - 125592
Published: July 20, 2024
Heavy
metals
pose
a
significant
threat
to
ecosystems
and
human
health
because
of
their
toxic
properties
ability
bioaccumulate
in
living
organisms.
Traditional
removal
methods
often
fall
short
terms
cost,
energy
efficiency,
minimizing
secondary
pollutant
generation,
especially
complex
environmental
settings.
In
contrast,
molecular
simulation
offer
promising
solution
by
providing
in-depth
insights
into
atomic
interactions
between
heavy
potential
adsorbents.
This
review
highlights
the
for
removing
types
pollutants
science,
specifically
metals.
These
powerful
tool
predicting
designing
materials
processes
remediation.
We
focus
on
specific
like
lead,
Cadmium,
mercury,
utilizing
cutting-edge
techniques
such
as
Molecular
Dynamics
(MD),
Monte
Carlo
(MC)
simulations,
Quantum
Chemical
Calculations
(QCC),
Artificial
Intelligence
(AI).
By
leveraging
these
methods,
we
aim
develop
highly
efficient
selective
unravelling
underlying
mechanisms,
pave
way
developing
more
technologies.
comprehensive
addresses
critical
gap
scientific
literature,
valuable
researchers
protection
health.
modelling
hold
promise
revolutionizing
prediction
metals,
ultimately
contributing
sustainable
solutions
cleaner
healthier
future.
Journal of Molecular Liquids,
Journal Year:
2024,
Volume and Issue:
410, P. 125513 - 125513
Published: July 14, 2024
The
contamination
of
natural
water
resources
by
pharmaceutical
pollutants
has
become
a
significant
environmental
concern.
Traditional
experimental
approaches
for
understanding
the
adsorption
behavior
these
contaminants
on
different
surfaces
are
often
time-consuming
and
resource-intensive.
In
response,
this
review
article
explores
powerful
combination
in
silico
techniques,
including
molecular
dynamics
(MD),
Monte
Carlo
simulations
(MC),
quantum
mechanics
(QM),
as
comprehensive
toolset
to
obtain
broad
perspectives
into
pollutants.
By
bridging
multiple
scales,
from
molecular-level
interactions
macroscopic
impact,
computational
methods
offer
holistic
processes
involved.
We
provide
an
overview
their
ecological
effects,
emphasizing
need
efficient
sustainable
solutions.
Subsequently,
we
delve
theoretical
foundations
MD,
MC,
QM,
highlighting
respective
strengths
simulating
pollutant
adsorption.
Moreover,
synergistic
potential
combining
methodologies
is
also
discussed
more
characterization
processes.
Recent
case
studies
illustrate
successful
application
techniques
predicting
behaviors
various
conditions.
Finally,
implications
discussed,
along
with
how
modelling
can
guide
solutions
mitigating
impact.
Journal of Materials Chemistry A,
Journal Year:
2023,
Volume and Issue:
11(47), P. 26127 - 26151
Published: Jan. 1, 2023
Gastrointestinal
drug
pollutants
pose
environmental
risks.
Our
study
explores
the
adsorption
of
famotidine
(FA)
and
pantoprazole
(PA)
using
Au-ddoped@ZIF-90-gglycerol
adsorbent
(A@Z/G),
emphasizing
pH-sensitive
effects
on
ecosystems.
ACS Omega,
Journal Year:
2023,
Volume and Issue:
8(35), P. 31600 - 31619
Published: Aug. 15, 2023
Catalysts
played
a
crucial
role
in
advancing
modern
human
civilization,
from
ancient
times
to
the
industrial
revolution.
Due
high
cost
and
limited
availability
of
traditional
catalysts,
there
is
need
develop
cost-effective,
high-activity,
nonprecious
metal-based
electrocatalysts.
Metal-organic
frameworks
(MOFs)
have
emerged
as
an
ideal
candidate
for
heterogeneous
catalysis
due
their
physicochemical
properties,
hybrid
inorganic/organic
structures,
uncoordinated
metal
sites,
accessible
organic
sections.
MOFs
are
nanoporous
crystalline
materials
that
can
be
used
catalysts
facilitate
polymerization
reactions.
Their
chemical
structural
diversity
make
them
effective
various
reactions
compared
catalysts.
been
applied
gas
storage
separation,
ion-exchange,
drug
delivery,
luminescence,
sensing,
nanofilters,
water
purification,
catalysis.
The
review
focuses
on
MOF-enabled
value-added
compound
production,
including
alcohol
oxidation,
olefin
oligomerization,
offer
tunable
porosity,
spatial
density,
single-crystal
XRD
control
over
catalyst
properties.
In
this
review,
were
focused
CO2
fixation,
reduction,
photoelectrochemical
splitting.
Overall,
great
potential
versatile
diverse
applications
future.
