Functionalized dendritic memristor of Pt/MoS2@LCO-PVA/Si for Mimicking synaptic behavior
Xueli Geng,
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Qin Gao,
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Jiangshun Huang
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et al.
Chemical Engineering Journal,
Journal Year:
2025,
Volume and Issue:
unknown, P. 161487 - 161487
Published: March 1, 2025
Language: Английский
Kinetically Controlled Self-Assembly of Ag Nanoclusters with Enhanced Luminescence
Fengjuan Chang,
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Mengting Zhang,
No information about this author
Wanying Chen
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et al.
ACS Applied Materials & Interfaces,
Journal Year:
2024,
Volume and Issue:
16(30), P. 39847 - 39856
Published: July 18, 2024
Constructing
self-assembly
with
definite
assembly
structure-property
correlation
is
of
great
significance
for
expanding
the
property
richness
and
functional
diversity
metal
nanoclusters
(NCs).
Herein,
a
well-designed
liquid
reaction
strategy
was
developed
through
which
highly
ordered
nanofiber
superstructure
enhanced
green
photoluminescence
(PL)
obtained
via
individual
silver
(Ag
NCs).
By
visual
monitoring
kinetic
process
using
time-dependent
in
situ
spectroscopy
measurements,
assembling
structure
growth
structure-determined
luminescence
mechanisms
were
revealed.
The
as-prepared
nanofibers
featured
series
advantages
involving
high
emission
efficiency,
large
Stokes
shift,
homogeneous
chromophore,
excellent
photostability,
temperature,
pH
sensibility.
virtue
these
merits,
they
successfully
employed
various
fields
luminescent
inks,
encryption
anticounterfeiting
platforms,
optoelectronic
light-emitting
diode
(LED)
devices.
Language: Английский
A new carbon modification strategy aimed to fully address the issues of NiO as an electrode material for supercapacitors
Journal of Alloys and Compounds,
Journal Year:
2024,
Volume and Issue:
984, P. 174027 - 174027
Published: March 2, 2024
Language: Английский
Memristive Ion Dynamics to Enable Biorealistic Computing
Chemical Reviews,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Dec. 27, 2024
Conventional
artificial
intelligence
(AI)
systems
are
facing
bottlenecks
due
to
the
fundamental
mismatches
between
AI
models,
which
rely
on
parallel,
in-memory,
and
dynamic
computation,
traditional
transistors,
have
been
designed
optimized
for
sequential
logic
operations.
This
calls
development
of
novel
computing
units
beyond
transistors.
Inspired
by
high
efficiency
adaptability
biological
neural
networks,
mimicking
capabilities
structures
gaining
more
attention.
Ion-based
memristive
devices
(IMDs),
owing
intrinsic
functional
similarities
their
counterparts,
hold
significant
promise
implementing
emerging
neuromorphic
learning
algorithms.
In
this
article,
we
review
mechanisms
IMDs
based
ion
drift
diffusion
elucidate
origins
diverse
dynamics.
We
then
examine
how
these
operate
within
different
materials
enable
with
various
types
switching
behaviors,
leading
a
wide
range
applications,
from
emulating
components
realizing
specialized
requirements.
Furthermore,
explore
potential
be
modified
tuned
achieve
customized
dynamics,
positions
them
as
one
most
promising
hardware
candidates
executing
bioinspired
algorithms
unique
specifications.
Finally,
identify
challenges
currently
that
hinder
widespread
usage
highlight
research
directions
could
significantly
benefit
incorporating
IMDs.
Language: Английский
Diffusive Memristor with CuS Nanoparticles Embedded in Polymeric Film as Artificial Nociceptor
ACS Applied Materials & Interfaces,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Sept. 11, 2024
The
threshold
behavior
and
the
ion
diffusion
dynamics
in
diffusive
volatile
memristors
have
a
very
uncanny
resemblance
to
transduction
process
of
biological
nociceptors.
Hence,
are
considered
most
suited
for
making
artificial
nociceptive
systems.
To
facilitate
their
widespread
adoption,
it
is
imperative
develop
polymeric
or
organic-inorganic
hybrid
material-based
that
economical,
biocompatible,
easily
processable.
In
this
study,
we
present
cluster-type
memristor
where
copper
used
as
active
top
electrode.
switching
medium
comprises
copper(II)
sulfide
(CuS)
nanoparticles
embedded
poly(ethylene
oxide)
(PEO).
devices
show
electrochemical
metalization
(ECM)-type
bidirectional
memory
with
high
nonlinearity
(10
Language: Английский