Machine learning applications for electrospun nanofibers: a review
Balakrishnan Subeshan,
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Asonganyi Atayo,
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Eylem Asmatulu
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et al.
Journal of Materials Science,
Journal Year:
2024,
Volume and Issue:
59(31), P. 14095 - 14140
Published: July 30, 2024
Abstract
Electrospun
nanofibers
have
gained
prominence
as
a
versatile
material,
with
applications
spanning
tissue
engineering,
drug
delivery,
energy
storage,
filtration,
sensors,
and
textiles.
Their
unique
properties,
including
high
surface
area,
permeability,
tunable
porosity,
low
basic
weight,
mechanical
flexibility,
alongside
adjustable
fiber
diameter
distribution
modifiable
wettability,
make
them
highly
desirable
across
diverse
fields.
However,
optimizing
the
properties
of
electrospun
to
meet
specific
requirements
has
proven
be
challenging
endeavor.
The
electrospinning
process
is
inherently
complex
influenced
by
numerous
variables,
applied
voltage,
polymer
concentration,
solution
flow
rate,
molecular
weight
polymer,
needle-to-collector
distance.
This
complexity
often
results
in
variations
nanofibers,
making
it
difficult
achieve
desired
characteristics
consistently.
Traditional
trial-and-error
approaches
parameter
optimization
been
time-consuming
costly,
they
lack
precision
necessary
address
these
challenges
effectively.
In
recent
years,
convergence
materials
science
machine
learning
(ML)
offered
transformative
approach
electrospinning.
By
harnessing
power
ML
algorithms,
scientists
researchers
can
navigate
intricate
space
more
efficiently,
bypassing
need
for
extensive
experimentation.
holds
potential
significantly
reduce
time
resources
invested
producing
wide
range
applications.
Herein,
we
provide
an
in-depth
analysis
current
work
that
leverages
obtain
target
nanofibers.
examining
work,
explore
intersection
ML,
shedding
light
on
advancements,
challenges,
future
directions.
comprehensive
not
only
highlights
processes
but
also
provides
valuable
insights
into
evolving
landscape,
paving
way
innovative
precisely
engineered
various
Graphical
abstract
Language: Английский
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