Quantitative Link between Potential Losses and Perovskite Solar Cell Stability During Accelerated Ageing
Опубликована: Янв. 1, 2025
Язык: Английский
High‐Efficiency Large‐Area Perovskite Solar Cells via a Multifunctional Crystallization Regulating Passivation Additive
Advanced Materials,
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 11, 2025
Abstract
Film
morphology
and
surface/interface
defect
density
play
a
critical
role
in
determining
the
efficiency
stability
of
perovskite
solar
cells
(PSCs).
Here,
chlorine‐substituted
aromatic
polycyclic
derivative
(BNCl)
is
reported,
which
shows
strong
interaction
with
both
lead
iodide
dimethyl
sulfoxide,
to
regulate
crystallization
perovskite,
along
effective
passivation
grain
boundaries
surface.
In
addition,
extruded
BNCl
molecule
at
hole
transport
layer
(HTL)/perovskite
interface
can
facilitate
transport,
leading
better
charge
transfer.
As
result,
certified
power
conversion
efficiencies
(PCEs)
25.04%
22.81%
are
achieved
for
PSCs
minimodules
aperture
areas
1
cm
2
12
respectively.
device
maintained
80%
its
initial
after
2500
h
maximum
point
(MPP)
tracking
under
ISOS‐L‐1
standard.
Язык: Английский
Benzalkonium chloride assisted quenching-free fabrication of nonalloyed FAPbI3 perovskite films and solar cells
Chemical Engineering Journal,
Год журнала:
2025,
Номер
unknown, С. 162771 - 162771
Опубликована: Апрель 1, 2025
Язык: Английский
The rise of perovskite solar cells-based integrated photovoltaic energy conversion-storage systems
Journal of Energy Chemistry,
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 1, 2025
Язык: Английский
High-efficiency and stable perovskite solar cells via bidentate chelation for facet-engineered growth of FAPbI3 (111) crystals
Chemical Engineering Journal,
Год журнала:
2025,
Номер
unknown, С. 164079 - 164079
Опубликована: Май 1, 2025
Язык: Английский
Machine learning-enabled optoelectronic material discovery: a comprehensive review
Journal of Materials Informatics,
Год журнала:
2025,
Номер
5(3)
Опубликована: Май 28, 2025
The
development
of
advanced
optoelectronic
materials
constitutes
a
pivotal
frontier
in
modern
energy
and
communication
technologies,
facilitating
critical
energy-photon-electron
interconversion
processes
that
underpin
sustainable
infrastructures
high-performance
electronic
devices.
However,
the
discovery
optimization
novel
face
substantial
hurdles
arising
from
complicated
structure-property
interdependencies,
prohibitive
costs,
protracted
innovation
cycles.
Conventional
empirical
approaches
computational
simulations
usually
exhibit
limited
efficacy
addressing
escalating
demands
for
with
superior
stability,
economic
viability,
customizable
properties.
integration
machine
learning
(ML)
high-throughput
screening
has
emerged
as
transformative
strategy
to
address
these
challenges.
By
rapidly
processing
large
multidimensional
datasets
predicting
material
properties
such
structure,
thermodynamic
charge
transport
behaviors,
ML
offers
unprecedented
capabilities
efficient
rational
design
materials.
This
review
provides
comprehensive
overview
cutting-edge
ML-driven
methodologies
emphasis
on
workflows,
data
strategies,
model
frameworks.
We
also
discuss
challenges
prospects
applications,
particularly
standardization,
interpretability
closed-loop
experimental
validation.
further
propose
potential
artificial
intelligence
autonomous
laboratories
build
powerful
pipeline
advance
Язык: Английский