Nano Letters,
Journal Year:
2024,
Volume and Issue:
24(37), P. 11632 - 11640
Published: Sept. 3, 2024
High-entropy
alloys
(HEAs)
present
both
significant
potential
and
challenges
for
developing
efficient
electrocatalysts
due
to
their
diverse
combinations
compositions.
Here,
we
propose
a
procedural
approach
that
combines
high-throughput
experimentation
with
data-driven
strategies
accelerate
the
discovery
of
HEA
hydrogen
evolution
reaction
(HER).
This
enables
rapid
preparation
arrays
various
element
composition
ratios
within
model
system.
The
intrinsic
activity
is
swiftly
screened
using
scanning
electrochemical
cell
microscopy
(SECCM),
providing
precise
composition-activity
data
sets
An
ensemble
machine
learning
(EML)
then
used
predict
database
subspace
Based
on
these
results,
two
groups
promising
catalysts
are
recommended
validated
through
actual
electrocatalytic
evaluations.
approach,
which
strategies,
provides
new
pathway
electrocatalysts.
Angewandte Chemie International Edition,
Journal Year:
2023,
Volume and Issue:
62(39)
Published: Aug. 4, 2023
The
vast
possibilities
in
the
elemental
combinations
of
high-entropy
alloys
(HEAs)
make
it
essential
to
discover
activity
descriptors
for
establishing
rational
electrocatalyst
design
principles.
Despite
increasing
attention
on
potential
zero
charge
(PZC)
hydrogen
evolution
reaction
(HER)
electrocatalyst,
neither
PZC
HEAs
nor
impact
HER
at
has
been
described.
Here,
we
use
scanning
electrochemical
cell
microscopy
(SECCM)
determine
and
activities
various
compositions
a
Pt-Pd-Ru-Ir-Ag
thin-film
HEA
materials
library
(HEA-ML)
with
high
statistical
reliability.
Interestingly,
is
linearly
correlated
its
composition-weighted
average
work
function.
current
density
acidic
media
positively
correlates
PZC,
which
can
be
explained
by
preconcentration
H+
electrical
double
layer
potentials
negative
PZC.
Chemical Science,
Journal Year:
2024,
Volume and Issue:
15(23), P. 8664 - 8722
Published: Jan. 1, 2024
High-entropy
alloys
hold
significant
promise
as
electrode
materials,
even
from
industrial
aspect.
This
potential
arises
their
ability
to
optimize
electronic
structures
and
reaction
sites,
stemming
complex
adjustable
composition.
Communications Materials,
Journal Year:
2024,
Volume and Issue:
5(1)
Published: May 17, 2024
Abstract
High-entropy
alloys
(HEAs)
have
attracted
extensive
attention
in
recent
decades
due
to
their
unique
chemical,
physical,
and
mechanical
properties.
An
in-depth
understanding
of
the
structure–property
relationship
HEAs
is
key
discovery
design
new
compositions
with
desirable
Related
this,
materials
genome
strategy
has
been
increasingly
used
for
discovering
better
performance.
This
review
paper
provides
an
overview
advances
this
fast-growing
area,
along
current
challenges
potential
opportunities
HEAs.
We
also
discuss
related
topics,
such
as
high-throughput
preparation,
characterization,
computation
HEAs,
data-driven
machine
learning
accelerating
alloy
development.
Finally,
future
research
directions
perspectives
genome-assisted
are
proposed
discussed.
Current Opinion in Chemical Engineering,
Journal Year:
2024,
Volume and Issue:
44, P. 101020 - 101020
Published: April 16, 2024
High-entropy
alloys
(HEAs)
possess
unique
physical
and
chemical
properties
clearly
distinguishable
from
those
of
traditional
alloys,
making
them
promising
candidates
for
various
applications,
including
electrocatalysis.
While
the
electrocatalytic
performance
these
has
been
assessed
in
detail,
electrochemical
stability
is
often
assumed
to
be
improved
compared
with
single
metals
simple
alloys.
Such
an
assumption
rarely
supported
by
theoretical
or
experimental
data
might
misleading
further
successful
implementation
HEAs
real
devices.
In
this
review,
we
provide
a
brief
overview
current
state
research
direction,
identify
common
pitfalls
assessing
alloy
stability,
discuss
need
advanced
coupled
experimental/computational
studies
directed
toward
understanding
partial
dissolution
elements
Nano Letters,
Journal Year:
2024,
Volume and Issue:
24(37), P. 11632 - 11640
Published: Sept. 3, 2024
High-entropy
alloys
(HEAs)
present
both
significant
potential
and
challenges
for
developing
efficient
electrocatalysts
due
to
their
diverse
combinations
compositions.
Here,
we
propose
a
procedural
approach
that
combines
high-throughput
experimentation
with
data-driven
strategies
accelerate
the
discovery
of
HEA
hydrogen
evolution
reaction
(HER).
This
enables
rapid
preparation
arrays
various
element
composition
ratios
within
model
system.
The
intrinsic
activity
is
swiftly
screened
using
scanning
electrochemical
cell
microscopy
(SECCM),
providing
precise
composition-activity
data
sets
An
ensemble
machine
learning
(EML)
then
used
predict
database
subspace
Based
on
these
results,
two
groups
promising
catalysts
are
recommended
validated
through
actual
electrocatalytic
evaluations.
approach,
which
strategies,
provides
new
pathway
electrocatalysts.
