Food Bioengineering,
Journal Year:
2024,
Volume and Issue:
3(1), P. 14 - 28
Published: March 1, 2024
Abstract
Industry
4.0
integrates
the
physical,
digital,
and
biological
realms
by
applying
digital
automation
in
systems,
processes,
manufacturing
facilities.
is
actively
shaping
development
of
intelligent
food
processing
industries
cultivated
meat
(CM)
sector.
This
integration
plays
a
crucial
role
accelerating
progress
within
global
CM
sector,
facilitating
achievement
its
objectives
related
to
sustainability,
security,
human
health,
environmental
concerns,
hygiene.
Incorporating
into
systems
empowers
upstream
downstream
production
processes
become
more
capable
self‐optimisation.
However,
enabling
rapid
adoption
emerging
startups
small
medium‐sized
enterprises
industry
necessitates
thorough
understanding
prerequisites
evaluation
technological
limitations.
Challenges
include
substantial
initial
costs
associated
with
establishing
infrastructure,
robust
cybersecurity
measures
ensure
effective
risk
management,
acquiring
skilled
professionals
proficient
both
operational
maintenance
roles.
Integrating
evolving
sector
presents
an
exciting
opportunity
foster
business‐to‐business
investments
across
various
domains,
including
local
markets,
export
opportunities,
broader
consumer
ecosystem.
Materials,
Journal Year:
2023,
Volume and Issue:
16(17), P. 5927 - 5927
Published: Aug. 30, 2023
The
integration
of
artificial
intelligence
(AI)
algorithms
in
materials
design
is
revolutionizing
the
field
engineering
thanks
to
their
power
predict
material
properties,
de
novo
with
enhanced
features,
and
discover
new
mechanisms
beyond
intuition.
In
addition,
they
can
be
used
infer
complex
principles
identify
high-quality
candidates
more
rapidly
than
trial-and-error
experimentation.
From
this
perspective,
herein
we
describe
how
these
tools
enable
acceleration
enrichment
each
stage
discovery
cycle
novel
optimized
properties.
We
begin
by
outlining
state-of-the-art
AI
models
design,
including
machine
learning
(ML),
deep
learning,
informatics
tools.
These
methodologies
extraction
meaningful
information
from
vast
amounts
data,
enabling
researchers
uncover
correlations
patterns
within
structures,
compositions.
Next,
a
comprehensive
overview
AI-driven
provided
its
potential
future
prospects
are
highlighted.
By
leveraging
such
algorithms,
efficiently
search
analyze
databases
containing
wide
range
identification
promising
for
specific
applications.
This
capability
has
profound
implications
across
various
industries,
drug
development
energy
storage,
where
performance
crucial.
Ultimately,
AI-based
approaches
poised
revolutionize
our
understanding
materials,
ushering
era
accelerated
innovation
advancement.
Journal of Chemical Information and Modeling,
Journal Year:
2024,
Volume and Issue:
64(3), P. 799 - 811
Published: Jan. 18, 2024
The
pursuit
of
designing
smart
and
functional
materials
is
paramount
importance
across
various
domains,
such
as
material
science,
engineering,
chemical
technology,
electronics,
biomedicine,
energy,
numerous
others.
Consequently,
researchers
are
actively
involved
in
the
development
innovative
models
strategies
for
design.
Recent
advancements
analytical
tools,
experimentation,
computer
technology
additionally
enhance
design
possibilities.
Notably,
data-driven
techniques
like
artificial
intelligence
machine
learning
have
achieved
substantial
progress
exploring
applications
within
science.
One
approach,
ChatGPT,
a
large
language
model,
holds
transformative
potential
addressing
complex
queries.
In
this
article,
we
explore
ChatGPT's
understanding
science
by
assigning
some
simple
tasks
subareas
computational
findings
indicate
that
while
ChatGPT
may
make
minor
errors
accomplishing
general
tasks,
it
demonstrates
capability
to
learn
adapt
through
human
interactions.
However,
issues
output
consistency,
probable
hidden
errors,
ethical
consequences
should
be
addressed.
Energy & Fuels,
Journal Year:
2024,
Volume and Issue:
38(3), P. 1593 - 1617
Published: Jan. 16, 2024
This
review
illuminates
the
pivotal
synergy
between
machine
learning
(ML)
and
biopolymers,
spotlighting
their
combined
potential
to
reshape
sustainable
energy,
fuels,
biochemicals.
Biobased
polymers,
derived
from
renewable
sources,
have
garnered
attention
for
roles
in
energy
fuel
sectors.
These
when
integrated
with
ML
techniques,
exhibit
enhanced
functionalities,
optimizing
systems,
storage,
conversion.
Detailed
case
studies
reveal
of
biobased
polymers
applications
industry,
further
showcasing
how
bolsters
efficiency
innovation.
The
intersection
also
marks
advancements
biochemical
production,
emphasizing
innovations
drug
delivery
medical
device
development.
underscores
imperative
harnessing
convergence
future
global
sustainability
endeavors
collective
evidence
presented
asserts
immense
promise
this
union
holds
steering
a
innovative
trajectory.
Advanced Materials,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Jan. 5, 2025
The
article
explores
materials
sustainability
through
a
bio-inspired
lens
and
discusses
paradigms
that
can
reshape
the
understanding
of
material
synthesis,
processing,
usage.
It
addresses
various
technological
fields,
from
structural
engineering
to
healthcare,
emphasizes
natural
cycles
as
blueprint
for
efficient
recycling
reuse.
study
shows
functionality
depends
on
both
chemical
composition
modifications,
which
role
processing.
identifies
strategies
such
mono-materiality
multifunctionality,
how
responsivity,
adaptivity,
modularity,
cellularity
simplify
assembly
disassembly.
Bioinspired
reusing
materials,
defect
tolerance,
maintenance,
remodeling,
healing
may
extend
product
lifespans.
principles
circularity,
longevity,
parsimony
are
reconsidered
in
context
"active
materiality",
dynamic
paradigm.
This
concept
expands
traditional
focus
science
structure-function
relationships
include
development
capable
responding
or
adapting
external
stimuli.
Concrete
examples
demonstrate
being
applied
technology
enhance
materials.
concludes
by
emphasizing
interdisciplinary
collaboration
key
factor
developing
sustainable
resilient
economy
harmony
with
nature's
cycles.
Proceedings of the National Academy of Sciences,
Journal Year:
2025,
Volume and Issue:
122(4)
Published: Jan. 24, 2025
The
design
of
new
alloys
is
a
multiscale
problem
that
requires
holistic
approach
involves
retrieving
relevant
knowledge,
applying
advanced
computational
methods,
conducting
experimental
validations,
and
analyzing
the
results,
process
typically
slow
reserved
for
human
experts.
Machine
learning
can
help
accelerate
this
process,
instance,
through
use
deep
surrogate
models
connect
structural
chemical
features
to
material
properties,
or
vice
versa.
However,
existing
data-driven
often
target
specific
objectives,
offering
limited
flexibility
integrate
out-of-domain
knowledge
cannot
adapt
new,
unforeseen
challenges.
Here,
we
overcome
these
limitations
by
leveraging
distinct
capabilities
multiple
AI
agents
collaborate
autonomously
within
dynamic
environment
solve
complex
materials
tasks.
proposed
physics-aware
generative
platform,
AtomAgents,
synergizes
intelligence
large
language
(LLMs)
collaboration
among
with
expertise
in
various
domains,
including
retrieval,
multimodal
data
integration,
physics-based
simulations,
comprehensive
results
analysis
across
modalities.
concerted
effort
multiagent
system
allows
addressing
problems,
as
demonstrated
examples
include
designing
metallic
enhanced
properties
compared
their
pure
counterparts.
Our
enable
accurate
prediction
key
characteristics
highlight
crucial
role
solid
solution
alloying
steer
development
alloys.
framework
enhances
efficiency
multiobjective
tasks
opens
avenues
fields
such
biomedical
engineering,
renewable
energy,
environmental
sustainability.
Batteries,
Journal Year:
2025,
Volume and Issue:
11(2), P. 51 - 51
Published: Jan. 28, 2025
This
strategic
review
examines
the
pivotal
role
of
sustainable
methodologies
in
battery
recycling
and
recovery
critical
minerals
from
waste
batteries,
emphasizing
need
to
address
existing
technical
environmental
challenges.
Through
a
systematic
analysis,
it
explores
application
green
organic
solvents
mineral
processing,
advocating
for
establishing
eco-friendly
techniques
aimed
at
clipping
boosting
resource
utilization.
The
escalating
demand
shortage
essential
including
copper,
cobalt,
lithium,
nickel
are
comprehensively
analyzed
forecasted
2023,
2030,
2040.
Traditional
extraction
techniques,
hydrometallurgical,
pyrometallurgical,
bio-metallurgical
processes,
efficient
but
pose
substantial
hazards
contribute
scarcity.
concept
arises
as
crucial
step
towards
ecological
conservation,
integrating
practices
lessen
footprint
extraction.
advancement
solvents,
notably
ionic
liquids
deep
eutectic
is
examined,
highlighting
their
attributes
minimal
toxicity,
biodegradability,
superior
efficacy,
thus
presenting
great
potential
transforming
sector.
emergence
such
palm
oil,
1-octanol,
Span
80
recognized,
with
advantageous
low
solubility
adaptability
varying
temperatures.
Kinetic
(mainly
temperature)
data
different
extracted
previous
studies
computed
machine
learning
techniques.
coefficient
determination
mean
squared
error
reveal
accuracy
experimental
data.
In
essence,
this
study
seeks
inspire
ongoing
efforts
navigate
impediments,
embrace
technological
advancements
artificial
intelligence,
foster
an
ethos
stewardship
metals
batteries.
