Layered double hydroxide-based nanomaterials for supercapacitors and batteries: Strategies and mechanisms
Chuan Jing,
No information about this author
Shengrong Tao,
No information about this author
Bin Fu
No information about this author
et al.
Progress in Materials Science,
Journal Year:
2024,
Volume and Issue:
unknown, P. 101410 - 101410
Published: Dec. 1, 2024
Language: Английский
Challenges and strategies for zinc anodes in aqueous Zinc-Ion batteries
Mingming Wang,
No information about this author
Yahan Meng,
No information about this author
Xiang Li
No information about this author
et al.
Chemical Engineering Journal,
Journal Year:
2025,
Volume and Issue:
507, P. 160615 - 160615
Published: Feb. 13, 2025
Language: Английский
AI unveils metal-support interaction principle to optimize catalyst design
Haobo Li
No information about this author
Chem Catalysis,
Journal Year:
2025,
Volume and Issue:
5(1), P. 101231 - 101231
Published: Jan. 1, 2025
Language: Английский
Computational Single-Atom Catalyst Database Empowers the Machine Learning Assisted Design of High-Performance Catalysts
Meng Huang,
No information about this author
Ruiyang Shi,
No information about this author
Heng Liu
No information about this author
et al.
The Journal of Physical Chemistry C,
Journal Year:
2025,
Volume and Issue:
unknown
Published: March 3, 2025
Language: Английский
Role of artificial intelligence in the design and discovery of next-generation battery electrolytes
Chemical Physics Reviews,
Journal Year:
2025,
Volume and Issue:
6(1)
Published: March 1, 2025
Adverse
climate
change,
global
warming,
and
energy
security
have
emerged
as
challenges,
demanding
advancements
in
high-performance
battery
technologies
to
drive
sustainability.
In
this
scenario,
developing
electrolytes
has
gained
significant
momentum
among
various
innovations,
given
their
critical
role
determining
safety
performance.
However,
the
conventional
trial-and-error
approach
electrolyte
discovery
is
costly,
complex,
time-consuming,
often
inefficient.
Recent
artificial
intelligence
(AI)
over
past
decade
catalyzed
innovations
across
diverse
fields,
ranging
from
nanotechnology
space
explorations,
are
now
emerging
a
powerful
tool
for
materials
discovery.
Numerous
studies
demonstrated
effectiveness
of
AI
screening
characterizing
next-generation
electrolytes.
This
review
offers
comprehensive
outlook
on
transformative
designing
novel
Examination
key
parameters
that
influence
electrochemical
performance
batteries
conducted.
The
challenges
opportunities
using
design
with
tailored
properties
explored.
Furthermore,
futuristic
vision
integrating
science-driven
AI-based
approaches
existing
experimental
theoretical
methods
accelerate
presented.
By
offering
such
understanding,
aims
provide
researchers,
industries,
policymakers
insights
into
how
can
be
leveraged
electrolytes,
paving
way
toward
progress
technology.
Language: Английский
Novel approaches to aqueous zinc-ion batteries: Challenges, strategies, and prospects
Wei Lv,
No information about this author
J.W. Liu,
No information about this author
Zilei Shen
No information about this author
et al.
eScience,
Journal Year:
2025,
Volume and Issue:
unknown, P. 100410 - 100410
Published: April 1, 2025
Language: Английский
Chemical perspectives on synthesis, functionalization, artificial intelligence, and energy storage applications of layered double hydroxides-based nanomaterials: A comprehensive review
Jai Kumar,
No information about this author
Nadeem Hussain Solangi,
No information about this author
Rana R. Neiber
No information about this author
et al.
Coordination Chemistry Reviews,
Journal Year:
2025,
Volume and Issue:
537, P. 216705 - 216705
Published: April 16, 2025
Language: Английский