Materials Today Bio,
Journal Year:
2024,
Volume and Issue:
27, P. 101134 - 101134
Published: June 20, 2024
Biomimetic
functionalized
metal-organic
frameworks
(Fn-MOFs)
represent
a
cutting-edge
approach
in
the
realm
of
cancer
vaccines.
These
multifunctional
agents,
inspired
by
biological
systems,
offer
unprecedented
opportunities
for
development
next-generation
The
vast
surface
area,
tunable
pore
size,
and
diverse
chemistry
MOFs
provide
versatile
scaffold
encapsulation
protection
antigenic
components,
crucial
vaccine
stability
delivery.
This
work
delves
into
innovative
design
application
Fn-MOFs,
highlighting
their
role
as
carriers
immune
enhancement
potential
to
revolutionize
By
mimicking
natural
processes,
with
ability
be
myriad
chemical
entities,
exhibit
superior
biocompatibility
stimuli-responsive
behavior
facilitate
targeted
delivery
tumor
sites.
review
encapsulates
latest
advancements
Fn-MOF
technology,
from
synthesis
modification
integration
combination
therapies.
It
underscores
significance
biomimetic
approaches
overcoming
current
challenges
development,
such
antigen
evasion.
leveraging
nature
this
paves
way
strategies
vaccines,
aiming
induce
potent
long-lasting
responses
against
malignancies.
Journal of Inorganic and Organometallic Polymers and Materials,
Journal Year:
2024,
Volume and Issue:
34(9), P. 3907 - 3930
Published: May 8, 2024
Abstract
Wastewater
treatment
is
designed
to
eradicate
toxic
pollutants
emanating
from
the
industrial
effluent
surface
and
underground
water.
The
efficiency
limitations
of
most
existing
water
techniques
such
as
coagulation/flocculation,
photocatalysis,
membrane
technologies
adsorption
in
remediation
have
been
established.
However,
success
reported
for
each
these
usually
associated
with
environmental
friendliness
products
applied.
MOFs-based
are
one
materials
serving
an
alternative
chemically
synthesized
products,
their
application
has
extensively
but
not
systematically
documented.
In
this
review,
authors
endeavoured
comprehensively
provide
insights
into
recent
product
synthesis
different
applications,
especially
treatment.
key
factors
influencing
MOFs,
including
choice
metal
ions,
organic
linkers,
conditions,
along
latest
developments
scalable
cost-effective
fabrication
discussed.
routes,
limitation
performances
adsorbent,
photocatalyst
additives
removal
elaborated.
prospects
large-scale
production
real
applications
critically
reviewed
study.
Overall,
a
well-curated
MOFs
hereby
generated
best
resources
accessible
through
literature.
Nanomaterials,
Journal Year:
2023,
Volume and Issue:
13(18), P. 2612 - 2612
Published: Sept. 21, 2023
Zinc–air
batteries
(ZABs)
have
garnered
significant
interest
as
a
viable
substitute
for
lithium-ion
(LIBs),
primarily
due
to
their
impressive
energy
density
and
low
cost.
However,
the
efficacy
of
zinc–air
is
heavily
dependent
on
electrocatalysts,
which
play
vital
role
in
enhancing
reaction
efficiency
stability.
This
scholarly
review
article
highlights
crucial
significance
electrocatalysts
explores
rationale
behind
employing
Fe-Co-Ni-Zn-based
metal–organic
framework
(MOF)-derived
hybrid
materials
potential
electrocatalysts.
These
MOF-derived
offer
advantages
such
abundancy,
high
catalytic
activity,
tunability,
structural
Various
synthesis
methods
characterization
techniques
are
employed
optimize
properties
Such
exhibit
excellent
stability,
selectivity,
making
them
suitable
applications
ZABs.
Furthermore,
they
demonstrate
notable
capabilities
realm
ZABs,
encompassing
elevated
density,
efficacy,
prolonged
longevity.
It
imperative
continue
extensively
researching
developing
this
area
propel
advancement
ZAB
technology
forward
pave
way
its
practical
implementation
across
diverse
fields.
Chinese Physics Letters,
Journal Year:
2024,
Volume and Issue:
41(7), P. 077103 - 077103
Published: June 1, 2024
Abstract
While
density
functional
theory
(DFT)
serves
as
a
prevalent
computational
approach
in
electronic
structure
calculations,
its
demands
and
scalability
limitations
persist.
Recently,
leveraging
neural
networks
to
parameterize
the
Kohn–Sham
DFT
Hamiltonian
has
emerged
promising
avenue
for
accelerating
computations.
Despite
advancements,
challenges
such
necessity
computing
extensive
training
data
explore
each
new
system
complexity
of
establishing
accurate
machine
learning
models
multi-elemental
materials
still
exist.
Addressing
these
hurdles,
this
study
introduces
universal
model
trained
on
matrices
obtained
from
first-principles
calculations
nearly
all
crystal
structures
Materials
Project.
We
demonstrate
generality
predicting
across
whole
periodic
table,
including
complex
systems,
solid-state
electrolytes,
Moiré
twisted
bilayer
heterostructure,
metal-organic
frameworks.
Moreover,
we
utilize
conduct
high-throughput
crystals
GNoME
datasets,
identifying
3940
with
direct
band
gaps
5109
flat
bands.
By
offering
reliable
efficient
framework
properties,
lays
groundwork
advancements
diverse
fields,
easily
providing
huge
set
also
making
design
table
possible.
