Journal of Optical Technology,
Journal Year:
2024,
Volume and Issue:
91(6), P. 429 - 429
Published: June 1, 2024
Subject
of
study.
Lead-free
perovskite
nanocrystals
(LFPNCs),
their
main
characteristics,
synthesis
methods,
and
optical
properties
are
examined.
Aim
The
aim
is
to
analyze
the
state-of-the-art
research
data
on
methods
LFPNCs.
In
addition,
formation
processes
LFPNCs,
dependence
nanocrystal
size
photoluminescence
quantum
yield
(PLQY)
parameters
(such
as
method,
temperature,
ligand
type)
determined.
Main
results.
LFPNCs
analyzed
based
LaMer
cluster
growth
models.
According
data,
primary
for
preparing
these
(NCs)
hot-injection
ligand-assisted
reprecipitation
(LARP).
Evidently,
average
increases
with
increasing
reaction
temperature.
For
NCs
synthesized
by
LARP,
temperature
100°C
leads
a
slight
decrease
in
yield.
However,
prepared
hot-injection,
PLQY
remains
independent
Additionally,
using
oleic
acid
results
narrow
distribution
NCs,
whereas
mixture
ligands
exhibiting
highest
PLQY.
Practical
significance.
literature
analysis
show
that
LARP
method
most
promising
synthesizing
owing
its
ease
implementation,
energy
efficiency,
scalability.
produced
this
can
be
applied
active
materials
sensor
technologies,
photovoltaics,
optoelectronic
devices.
Advanced Materials,
Journal Year:
2024,
Volume and Issue:
36(18)
Published: Jan. 19, 2024
Abstract
Machine
learning
holds
significant
research
potential
in
the
field
of
nanotechnology,
enabling
nanomaterial
structure
and
property
predictions,
facilitating
materials
design
discovery,
reducing
need
for
time‐consuming
labor‐intensive
experiments
simulations.
In
contrast
to
their
achiral
counterparts,
application
machine
chiral
nanomaterials
is
still
its
infancy,
with
a
limited
number
publications
date.
This
despite
great
advance
development
new
sustainable
high
values
optical
activity,
circularly
polarized
luminescence,
enantioselectivity,
as
well
analysis
structural
chirality
by
electron
microscopy.
this
review,
an
methods
used
studying
provided,
subsequently
offering
guidance
on
adapting
extending
work
nanomaterials.
An
overview
within
framework
synthesis–structure–property–application
relationships
presented
insights
how
leverage
study
these
highly
complex
are
provided.
Some
key
recent
reviewed
discussed
Finally,
review
captures
achievements,
ongoing
challenges,
prospective
outlook
very
important
field.
Small,
Journal Year:
2024,
Volume and Issue:
20(29)
Published: Feb. 11, 2024
Functional
nanostructures
build
up
a
basis
for
the
future
materials
and
devices,
providing
wide
variety
of
functionalities,
possibility
designing
bio-compatible
nanoprobes,
etc.
However,
development
new
nanostructured
via
trial-and-error
approach
is
obviously
limited
by
laborious
efforts
on
their
syntheses,
cost
manpower.
This
one
reasons
an
increasing
interest
in
design
novel
with
required
properties
assisted
machine
learning
approaches.
Here,
dataset
synthetic
parameters
optical
important
class
light-emitting
nanomaterials
-
carbon
dots
are
collected,
processed,
analyzed
transitions
red
near-infrared
spectral
ranges.
A
model
prediction
characteristics
these
based
multiple
linear
regression
established
verified
comparison
predicted
experimentally
observed
synthesized
three
different
laboratories.
Based
analysis,
open-source
code
provided
to
be
used
researchers
procedures.
ACS Nano,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Sept. 24, 2024
Atomically
precise
metal
nanoclusters
(MNCs)
represent
a
fascinating
class
of
ultrasmall
nanoparticles
with
molecule-like
properties,
bridging
conventional
metal-ligand
complexes
and
nanocrystals.
Despite
their
potential
for
various
applications,
synthesis
challenges
such
as
understanding
varied
synthetic
parameters
property-driven
persist,
hindering
full
exploitation
wider
application.
Incorporating
smart
methodologies,
including
closed-loop
framework
automation,
data
interpretation,
feedback
from
AI,
offers
promising
solutions
to
address
these
challenges.
In
this
perspective,
we
summarize
the
that
has
been
demonstrated
in
nanomaterials
explore
research
frontiers
MNCs.
Moreover,
perspectives
on
inherent
opportunities
MNCs
are
discussed,
aiming
provide
insights
directions
future
advancements
emerging
field
AI
Science,
while
integration
deep
learning
algorithms
stands
substantially
enrich
by
offering
enhanced
predictive
capabilities,
optimization
strategies,
control
mechanisms,
thereby
extending
MNC
synthesis.
Advanced Optical Materials,
Journal Year:
2023,
Volume and Issue:
12(8)
Published: Aug. 21, 2023
Abstract
Metal
halide
perovskite
nanoplatelets
(NPls)
have
recently
joined
a
rich
family
of
2D
semiconductor
nanomaterials.
Quantum
and
dielectric
confinement
in
these
nanostructures
endow
them
with
useful
optical
properties,
which
include,
but
are
not
limited
to,
high
linear
nonlinear
absorption
coefficients,
narrow
tunable
emission
bands,
photoluminescence
quantum
yield.
These
characteristics
render
NPls
promising
for
applications
lighting,
photodetection,
optics,
photocatalysis.
Doping
is
universal
approach
tuning
electronic
properties
materials,
the
B‐site
doping
allows
further
improvement
adjustment
on
demand
above‐mentioned
may
result
appearance
fundamentally
new
behavior
through
embedding
optically
active
dopants.
In
this
mini‐review,
basic
knowledge
about
shortly
summarized
terms
their
colloidal
synthesis,
then
considered
existing
approaches
(in
situ
post‐synthetic
doping)
its
effect
(doping
self‐emitting
ions
such
as
Mn
2+
rare‐earth
elements;
consequences
response).
Journal of Materials Informatics,
Journal Year:
2023,
Volume and Issue:
3(3)
Published: July 13, 2023
Virtual
sample
generation
(VSG),
as
a
cutting-edge
technique,
has
been
successfully
applied
in
machine
learning-assisted
materials
design
and
discovery.
A
virtual
without
experimental
validation
is
defined
an
unknown
sample,
which
either
expanded
from
the
original
data
distribution
for
modeling
or
designed
via
algorithms
predicting.
This
review
aims
to
discuss
applications
of
VSG
techniques
discovery
based
on
research
progress
recent
years.
First,
we
summarize
commonly
used
expansion
training
set,
including
Bootstrap,
Monte
Carlo,
particle
swarm
optimization,
mega
trend
diffusion,
Gaussian
mixture
model,
random
forest,
generative
adversarial
networks.
Next,
frequently
employed
searching
are
introduced,
efficient
global
proactive
progress.
Then,
universally
adopted
inverse
methods
presented,
genetic
algorithm,
Bayesian
pattern
recognition
projection.
Finally,
future
directions
proposed.
Nanoscale,
Journal Year:
2023,
Volume and Issue:
15(16), P. 7482 - 7492
Published: Jan. 1, 2023
Ligand-free
methods
for
the
synthesis
of
halide
perovskite
nanocrystals
are
great
interest
because
their
excellent
performance
in
optoelectronics
and
photonics.
