Journal of the American Chemical Society,
Journal Year:
2024,
Volume and Issue:
unknown
Published: April 9, 2024
Hydrolytically
stable
materials
exhibiting
a
wide
range
of
programmable
water
sorption
behaviors
are
crucial
for
on-demand
systems.
While
notable
advancements
in
employing
metal–organic
frameworks
(MOFs)
as
promising
adsorbents
have
been
made,
developing
robust
yet
easily
tailorable
MOF
scaffold
specific
operational
conditions
remains
challenge.
To
address
this
demand,
we
employed
topology-guided
linker
installation
strategy
using
NU-600,
which
is
zirconium-based
(Zr-MOF)
that
contains
three
vacant
crystallographically
defined
coordination
sites.
Through
judicious
selection
N-heterocyclic
auxiliary
linkers
lengths,
installed
them
into
designated
sites,
giving
rise
to
six
new
MOFs
bearing
different
combinations
predetermined
positions.
The
resulting
MOFs,
denoted
NU-606
NU-611,
demonstrate
enhanced
structural
stability
against
capillary
force-driven
channel
collapse
during
desorption
due
the
increased
connectivity
Zr6
clusters
MOFs.
Furthermore,
incorporating
these
with
various
hydrophilic
N
sites
enables
systematic
modulation
pore-filling
pressure
from
about
55%
relative
humidity
(RH)
parent
NU-600
down
below
40%
RH.
This
topology-driven
offers
precise
control
properties
highlighting
facile
route
design
use
applications.
Advanced Materials,
Journal Year:
2024,
Volume and Issue:
36(24)
Published: March 12, 2024
Abstract
Modern
human
civilization
deeply
relies
on
the
rapid
advancement
of
cutting‐edge
electronic
systems
that
have
revolutionized
communication,
education,
aviation,
and
entertainment.
However,
electromagnetic
interference
(EMI)
generated
by
digital
poses
a
significant
threat
to
society,
potentially
leading
future
crisis.
While
numerous
efforts
are
made
develop
nanotechnological
shielding
mitigate
detrimental
effects
EMI,
there
is
limited
focus
creating
absorption‐dominant
solutions.
Achieving
EMI
shields
requires
careful
structural
design
engineering,
starting
from
smallest
components
considering
most
effective
wave
attenuating
factors.
This
review
offers
comprehensive
overview
structures,
emphasizing
critical
elements
design,
mechanisms,
limitations
both
traditional
shields,
common
misconceptions
about
foundational
principles
science.
systematic
serves
as
scientific
guide
for
designing
structures
prioritize
absorption,
highlighting
an
often‐overlooked
aspect
Chemical Society Reviews,
Journal Year:
2024,
Volume and Issue:
53(14), P. 7328 - 7362
Published: Jan. 1, 2024
This
Tutorial
Review
on
atmospheric
water
harvesting
evaluates
sorbents’
essential
mechanisms
and
design
principles,
focusing
chemical
material
system-level
strategies
to
enhance
production
efficiency
address
global
scarcity.
Digital Discovery,
Journal Year:
2024,
Volume and Issue:
3(3), P. 491 - 501
Published: Jan. 1, 2024
The
integration
of
artificial
intelligence
into
scientific
research
opens
new
avenues
with
the
advent
GPT-4V,
a
large
language
model
equipped
vision
capabilities.
Journal of the American Chemical Society,
Journal Year:
2025,
Volume and Issue:
unknown
Published: March 8, 2025
The
successful
integration
of
large
language
models
(LLMs)
into
laboratory
workflows
has
demonstrated
robust
capabilities
in
natural
processing,
autonomous
task
execution,
and
collaborative
problem-solving.
This
offers
an
exciting
opportunity
to
realize
the
dream
chemical
research
on
demand.
Here,
we
report
a
robotic
AI
chemist
powered
by
hierarchical
multiagent
system,
ChemAgents,
based
on-board
Llama-3.1-70B
LLM,
capable
executing
complex,
multistep
experiments
with
minimal
human
intervention.
It
operates
through
Task
Manager
agent
that
interacts
researchers
coordinates
four
role-specific
agents─Literature
Reader,
Experiment
Designer,
Computation
Performer,
Robot
Operator─each
leveraging
one
foundational
resources:
comprehensive
Literature
Database,
extensive
Protocol
Library,
versatile
Model
state-of-the-art
Automated
Lab.
We
demonstrate
its
versatility
efficacy
six
experimental
tasks
varying
complexity,
ranging
from
straightforward
synthesis
characterization
more
complex
exploration
screening
parameters,
culminating
discovery
optimization
functional
materials.
Additionally,
introduce
seventh
task,
where
ChemAgents
is
deployed
new
chemistry
lab
environment
autonomously
perform
photocatalytic
organic
reactions,
highlighting
ChemAgents's
scalability
adaptability.
Our
multiagent-driven
showcases
potential
on-demand
accelerate
democratize
access
advanced
across
academic
disciplines
industries.
Chemical Science,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Dec. 9, 2024
Large
language
models
(LLMs)
have
emerged
as
powerful
tools
in
chemistry,
significantly
impacting
molecule
design,
property
prediction,
and
synthesis
optimization.
This
review
highlights
LLM
capabilities
these
domains
their
potential
to
accelerate
scientific
discovery
through
automation.
We
also
LLM-based
autonomous
agents:
LLMs
with
a
broader
set
of
interact
surrounding
environment.
These
agents
perform
diverse
tasks
such
paper
scraping,
interfacing
automated
laboratories,
planning.
As
are
an
emerging
topic,
we
extend
the
scope
our
beyond
chemistry
discuss
across
any
domains.
covers
recent
history,
current
capabilities,
design
agents,
addressing
specific
challenges,
opportunities,
future
directions
chemistry.
Key
challenges
include
data
quality
integration,
model
interpretability,
need
for
standard
benchmarks,
while
point
towards
more
sophisticated
multi-modal
enhanced
collaboration
between
experimental
methods.
Due
quick
pace
this
field,
repository
has
been
built
keep
track
latest
studies:
https://github.com/ur-whitelab/LLMs-in-science.
Journal of the American Chemical Society,
Journal Year:
2024,
Volume and Issue:
146(29), P. 19654 - 19659
Published: July 11, 2024
We
evaluate
the
effectiveness
of
pretrained
and
fine-tuned
large
language
models
(LLMs)
for
predicting
synthesizability
inorganic
compounds
selection
precursors
needed
to
perform
synthesis.
The
predictions
LLMs
are
comparable
to─and
sometimes
better
than─recent
bespoke
machine
learning
these
tasks
but
require
only
minimal
user
expertise,
cost,
time
develop.
Therefore,
this
strategy
can
serve
both
as
an
effective
strong
baseline
future
studies
various
chemical
applications
a
practical
tool
experimental
chemists.
