Case Studies in Construction Materials,
Journal Year:
2024,
Volume and Issue:
20, P. e03248 - e03248
Published: May 7, 2024
Lithium
slag
(LS)
is
a
waste
residue
generated
during
lithium
extraction,
posing
significant
environmental
challenge
due
to
its
extensive
accumulation.
This
study
proposes
method
of
utilizing
thermally
activated
LS
as
the
sole
precursor
for
geopolymer
synthesis.
The
thermal
activation
mechanism
was
investigated
using
XRD,
FTIR,
ICP,
and
SEM
techniques,
while
exploring
optimal
composition
geopolymers(LSG).
research
revealed
that
heating
at
700
°C
increased
amorphous
content
from
15.9
%
48.1
%,
altering
chemical
structure
aluminosilicates
enhancing
leaching
capacity
in
alkaline
environments,
thereby
boosting
reactivity.
activator
modulus
alkali
equivalent
were
found
significantly
influence
strength
microstructure
LSG.
As
1.0
1.4,
initially
rose
before
declining,
whereas
progressively
with
0.10
0.16.
synthesized
1.2
0.16
exhibited
highest
compressive
53.1
MPa
after
28
days.
Test
results
indicated
internal
LSG
primarily
comprised
unreacted
particles,
N(C)-A-S-H
gel,
microcracks.
facilitated
dissolution
Si4+
Al3+
within
LS,
intensifying
geopolymerization
produce
more
densifying
strength.
elucidates
by
which
high-temperature
calcination
enhances
reactivity,
well
impact
on
microstructural
properties
LSG,
offering
new
insights
into
engineering
applications
high-performance
cementitious
materials.
Green Energy and Resources,
Journal Year:
2024,
Volume and Issue:
2(1), P. 100062 - 100062
Published: March 1, 2024
As
a
common
industrial
solid
waste,
fly
ash
requires
proper
processing
and
utilization
to
alleviate
environmental
pressure.
In
contrast
earlier
low-value
treatment
methods
for
ash,
such
as
its
use
in
construction
materials,
it
is
more
practical
explore
the
high-value
of
considering
elemental
ingredient
morphological
characteristics.
Herein,
this
work
comprehensively
reviews
research
progress
extracting
preparing
silica,
alumina,
zeolite
respectively
derived
from
silicon
aluminum
elements
ash.
Specifically,
mechanisms
processes
various
are
elucidated
detail,
virtues
drawbacks
production
technologies
compared
identify
economical
environmentally
friendly
method.
Importantly,
first
energy
storage
electrode
materials.
Different
synthesis
strategies
thoroughly
examined,
especially
fully
utilizing
primary
resource,
converting
into
Finally,
paper
summarizes
opportunities
challenges
associated
with
E3S Web of Conferences,
Journal Year:
2023,
Volume and Issue:
430, P. 01203 - 01203
Published: Jan. 1, 2023
This
article
investigates
the
possible
synergy
between
geopolymers
and
plastics
as
a
method
for
sustainable
composite
materials,
addressing
growing
worldwide
need
environmentally
responsible
solutions.
Geopolymers,
which
provide
low-carbon
alternatives
to
traditional
building
are
being
studied
alongside
plastics,
recognised
their
flexibility
lightweight
properties.
The
research
emphasises
ability
of
this
attain
increased
mechanical,
thermal,
chemical
qualities
by
investigating
molecular-level
interaction
processes,
enhanced
material
properties,
applications
in
diverse
sectors.
Furthermore,
assesses
environmental
consequences,
such
decreased
carbon
emissions
energy
usage,
while
also
analysing
manufacturing
scaling
problems.
work
lays
way
unique
route
science,
poised
greatly
contribute
more
resilient
built
environment,
giving
insights
into
both
present
accomplishments
future
possibilities.
Smart Construction and Sustainable Cities,
Journal Year:
2023,
Volume and Issue:
1(1)
Published: Nov. 10, 2023
Abstract
Efforts
to
reduce
the
weight
of
buildings
and
structures,
counteract
seismic
threat
human
life,
cut
down
on
construction
expenses
are
widespread.
A
strategy
employed
address
these
challenges
involves
adoption
foam
concrete.
Unlike
traditional
concrete,
concrete
maintains
standard
composition
but
excludes
coarse
aggregates,
substituting
them
with
a
agent.
This
alteration
serves
dual
purpose:
diminishing
concrete’s
overall
weight,
thereby
achieving
lower
density
than
regular
creating
voids
within
material
due
agent,
resulting
in
excellent
thermal
conductivity.
article
delves
into
presentation
statistical
models
utilizing
three
different
methods—linear
(LR),
non-linear
(NLR),
artificial
neural
network
(ANN)—to
predict
compressive
strength
These
formulated
based
dataset
97
sets
experimental
data
sourced
from
prior
research
endeavors.
comparative
evaluation
outcomes
is
subsequently
conducted,
leveraging
benchmarks
like
coefficient
determination
(
R
2
),
root
mean
square
error
(RMSE),
absolute
(MAE),
aim
identifying
most
proficient
model.
The
results
underscore
remarkable
effectiveness
ANN
evident
model’s
value,
which
surpasses
that
LR
model
by
36%
22%.
Furthermore,
demonstrates
significantly
MAE
RMSE
values
compared
both
NLR
models.
