Langmuir,
Journal Year:
2024,
Volume and Issue:
41(1), P. 858 - 867
Published: Dec. 30, 2024
The
first
protocells
are
speculated
to
have
arisen
from
the
self-assembly
of
simple
abiotic
carboxylic
acids,
alcohols,
and
other
amphiphiles
into
vesicles.
To
study
complex
process
vesicle
formation,
we
combined
laboratory
automation
with
AI-guided
experimentation
accelerate
discovery
specific
compositions
underlying
principles
governing
formation.
Using
a
low-cost
commercial
liquid
handling
robot,
automated
experimental
procedures,
enabling
high-throughput
testing
various
reaction
conditions
for
mixtures
seven
(7)
amphiphiles.
Multitemplate
multiscale
template
matching
(MMTM)
was
used
automate
confocal
microscopy
image
analysis,
us
quantify
formation
without
tedious
manual
counting.
results
were
create
Gaussian
surrogate
model,
then
active
learning
iteratively
direct
experiments
reduce
model
uncertainty.
Mixtures
containing
primarily
trimethyl
decylammonium
decylsulfate
in
equal
amounts
formed
vesicles
at
submillimolar
critical
concentrations,
more
than
20%
glycerol
monodecanoate
prevented
forming
even
high
total
amphiphile
concentrations.
ACS Sustainable Chemistry & Engineering,
Journal Year:
2024,
Volume and Issue:
12(34), P. 12695 - 12707
Published: Aug. 6, 2024
The
accelerating
depletion
of
natural
resources
undoubtedly
demands
a
radical
reevaluation
research
practices
addressing
the
escalating
climate
crisis.
From
traditional
approaches
to
modern-day
advancements,
integration
automation
and
artificial
intelligence
(AI)-guided
decision-making
has
emerged
as
transformative
route
in
shaping
new
methodologies.
Harnessing
robotics
high-throughput
alongside
intelligent
experimental
design,
self-driving
laboratories
(SDLs)
offer
an
innovative
solution
expedite
chemical/materials
timelines
while
significantly
reducing
carbon
footprint
scientific
endeavors,
which
could
be
utilized
not
only
generate
green
materials
but
also
make
process
itself
more
sustainable.
In
this
Perspective,
we
examine
potential
SDLs
driving
sustainability
forward
through
case
studies
discovery
optimization,
thereby
paving
way
for
greener
efficient
future.
While
hold
immense
promise,
discuss
challenges
that
persist
their
development
deployment,
necessitating
holistic
approach
both
design
implementation.
Advanced Functional Materials,
Journal Year:
2024,
Volume and Issue:
34(52)
Published: Aug. 24, 2024
Abstract
Unlike
single‐component
2D
metal
halide
perovskites
(MHPs)
exhibiting
sharp
excitonic
photoluminescence
(PL),
a
broadband
PL
emerges
in
mixed
Pb‐Sn
lattices.
Two
physical
models
–self‐trapped
exciton
and
defect‐induced
Stokes‐shift
–
are
proposed
to
explain
this
unconventional
phenomenon.
However,
the
explanations
provide
limited
rationalizations
without
consideration
of
formidable
compositional
space,
thus,
fundamental
origin
remains
elusive.
Herein,
high‐throughput
automated
experimental
workflow
is
established
systematically
explore
MHPs,
employing
PEA
(Phenethylammonium)
as
model
cation
known
work
rigid
organic
spacer.
Spectrally,
becomes
further
broadened
with
rapid
2
PbI
4
phase
segregation
increasing
Pb
concentrations
during
early‐stage
crystallization.
Counterintuitively,
MHPs
high
exhibit
prolonged
lifetimes.
Hyperspectral
microscopy
identifies
substantial
those
films,
hypothesizing
that
establishment
charge
transfer
excitons
by
upon
crystallization
at
high‐Pb
compositions
results
distinctive
properties.
These
indicate
two
independent
mechanisms—defect‐induced
Stoke‐shifts
segregation—coexist
which
significantly
correlates
Pb:Sn
ratio,
thereby
simultaneously
contributing
emission
HPs.
Digital Discovery,
Journal Year:
2024,
Volume and Issue:
3(7), P. 1273 - 1279
Published: Jan. 1, 2024
We
share
the
results
of
a
survey
on
automation
and
autonomy
in
materials
science
labs,
which
highlight
variety
researcher
challenges
motivations.
also
propose
framework
for
levels
laboratory
from
L0
to
L5.
Digital Discovery,
Journal Year:
2024,
Volume and Issue:
3(8), P. 1577 - 1590
Published: Jan. 1, 2024
Discovery
of
physical
models
binary
compositions
using
structured
Gaussian
Process
(sGP)
with
physics-informed
mean
functions,
optimizing
materials
post-discovery
to
enhance
design
and
application
efficiency.
Systems,
Journal Year:
2025,
Volume and Issue:
13(4), P. 253 - 253
Published: April 3, 2025
A
self-driving
lab
(SDL)
system
that
automates
experimental
design,
data
collection,
and
analysis
using
robotics
artificial
intelligence
(AI)
technologies.
Its
significance
has
grown
substantially
in
recent
years.
This
study
analyzes
the
overall
SDL
research
trends,
examines
changes
specific
topics,
visualizes
relational
structure
between
authors
to
identify
key
contributors,
extracts
major
themes
from
extensive
texts
highlight
essential
content.
To
achieve
these
objectives,
trend
analysis,
network
topic
modeling
were
conducted
on
352
papers
collected
Web
of
Science
2004
2023.
ensure
validity
results,
a
correlation
matrix
was
also
performed.
The
results
revealed
three
findings.
First,
surged
since
2019,
driven
by
advancements
AI
technologies,
reflecting
heightened
activity
this
field.
Second,
modern
scientific
is
advancing
with
focus
data-driven
approaches,
applications,
optimization
through
utilization
SDLs.
Third,
exhibits
interdisciplinary
convergence,
encompassing
material
optimization,
biological
processes,
predictive
algorithms.
underscores
growing
importance
SDLs
as
tool
across
diverse
academic
disciplines
provides
practical
insights
into
sustainable
future
directions
strategic
approaches.
Chemical Society Reviews,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Jan. 1, 2025
We
offer
ten
diverse
perspectives
exploring
the
transformative
potential
of
artificial
intelligence
(AI)
in
chemistry,
highlighting
many
challenges
we
face,
and
offering
strategies
to
address
them.
APL Machine Learning,
Journal Year:
2025,
Volume and Issue:
3(2)
Published: April 30, 2025
Machine
learning
and
automation
are
transforming
scientific
research,
yet
the
implementation
of
self-driving
laboratories
(SDLs)
remains
costly
complex,
it
difficult
to
learn
how
use
these
facilities.
To
address
this,
we
introduce
Claude-Light,
a
lightweight,
remotely
accessible
instrument
designed
for
prototyping
algorithms
machine
workflows.
Claude-Light
integrates
REST
API,
Raspberry
Pi-based
control
system,
an
RGB
LED
with
photometer
that
measures
ten
spectral
outputs,
providing
controlled
but
realistic
experimental
environment.
This
device
enables
users
explore
at
multiple
levels,
from
basic
programming
design
learning-driven
optimization.
We
demonstrate
application
in
structured
approaches,
including
traditional
scripting,
statistical
experiments,
active
methods.
In
addition,
role
large
language
models
(LLMs)
laboratory
automation,
highlighting
their
selection,
data
extraction,
function
calling,
code
generation.
While
LLMs
present
new
opportunities
streamlining
they
also
challenges
related
reproducibility,
security,
reliability.
discuss
strategies
mitigate
risks
while
leveraging
enhanced
efficiency
laboratories.
provides
practical
platform
students
researchers
develop
skills
test
before
deploying
them
larger-scale
SDLs.
By
lowering
barrier
entry
this
tool
facilitates
broader
adoption
AI-driven
experimentation
fosters
innovation
autonomous
