Hello.
I'm
Dr
Michael
Nazarkovasky,
Ukrainian
researcher
in
chemistry
and
materials
science
from
Brazil.
Evolved
data
driven
solutions
in
my
area
of
knowledge
and
expertise.
The
presentation
is
made
in
collaboration
with
Ukrainian
colleagues
from
National
Academy
of
Sciences
of
Ukraine,
supporting
and
promoting
their
scientific
research
programs
during
such
a
difficult
period.
The
subject
says
on
statistical
and
data
analysis
approaches,
deepen
conception
behind
the
experimental
results
and
phenomena.
In
particular,
unsupervised
methods
are
helpful
for
multivariate
cases
like
this
when
two
or
more
classes
of
materials
are
characterized
by
a
large
body
of
the
parameters
measured
or
calculated
in
the
course
of
the
lab
analysis.
This
case
is
about
hybrid
materials
which contain mixed
nanoxides
and
carbon
phases.
The
nano
hybrids
combine
properties
of
both
components;
well
ordered
micro
and
meso
porosity,
a large
surface
area
and
high
porosity
in
general,
high
corrosion
resistance,
thermal
and
mechanical
stability,
hydrophobicity
and
high
conductivity
due
to
the
presence
of
carbon,
developed for active sites
attributed
to
the
metal
oxide
nanoparticles.
Hence
reasons
of
the
subject
Nanomaterials' I
mportance
exists in
the
variety
of
their
applications;
Catalysis, adsorption,
sensors,
energy
field,
and
hydrogen
adsorption.
Typical
preparation
of
binary
oxides,
non
carbon
oxide
nanocomposites
consists
in
three
stages.
On
the
first
stage,
preparation
of
the
homogeneous
dispersion
of
silica
in
the
aqueous
solution
of
the
corresponding
metal
acetate
under
stirring
at
room
temperature
was
conducted.
On
the
second
stage,
dispersions
were
dried
at
130
degree
C
during
five
hours
and
sift through
the
0.5
millimeter
mesh.
At
the
last
stage
all
of
the
10
powders
were
[inaudible 00:02:26]
for
2
hours
at
600
degree
C
in
air.
Also
the
reference
sample
of
fumed silica
without
metals
was
treated
under
the
same
conditions,
by
bringing
all
these
three
stages;
homogenization of
the
aqueous
dispersion,
drying,
grinding,
sieving,
and
carbonization
at
the
same
temperature.
The
process
of
impregnation
of
fumed
silica
with
an
aqueous
solution
of
metal
acetate
and
subsequent
removal
of
the
solvent,
the
adsorbed acetates,
are
distributed
uniformly
over
the
matrix
surface.
Modification
of
reserves
of
resorcinal
formaldehyde polymer
by
oxide
nano
composite
was
carried
out
by
easing the
process
mixing
resorcinal formaldehyde,
and
this
binary
pre-synthesized nanocomposites
reference
silica
the
weight
ratio
of
an
aqueous solution
under
stirring
at
room
temperature.
All
these
samples
were
sealed
and
placed
in
a
thermostatic
oven
for
polymerization,
and
all
synthesized
composites
were
seized
with
further
drying
at
90
degrees
C
for
10
hours.
Carbonization
of
the
samples
was
carried
out
in
a
tubular
furnace
under
nitrogen
atmosphere
upon
heating
from
room
temperature
up
to
800
degrees
C
at
a
heating
rate
of
5
degrees
C
per
minute,
and
kept
at
a
maximum
temperature
for
two
hours.
Hence
the
carbonized
samples
are
labeled
as
C
and
the
initial
materials
which
do
not
contain
carbon
are
[inaudible 00:04:29].
Actual properties
of theâ¦
Or
in
other
word,
parameters
of
porosity
were
calculated
using
modified Nguyen-Do
method
from
the
low
temperature
nitrogen
adsorption-
desorption.
This
is
a
standard
[inaudible 00:04:49]
method
for
porosity
and
it's
called
the
[inaudible 00:04:51]
.
The
calculated
parameters
are
assigned
as
variables
for
further
data
processing
using JMP.
To
be
more
exact,
the
specific surface
area
and
total pore
volume
were
derived
from
the
BET
measurements
and
then served
to
calculate
respective
portions
of
micro,
meso,
and
macro
porosity.
In
this
case
we
have
a
set
of inward
variables.
For
example, Nguyen-Do
method
was
developed
initially
for
calculation
of
carbon
materials
with
a
sleeve-like
porosity,
afterward
by
one
of
the
co
authors
of
the
presentation,
Professor
Vladimir Gun'ko
.
The
method
was
modified
and
extended
through
a
large
variety
of
other
materials
which may
contain
cylinders,
also
slits,
and
voids
among
the
particles
within
the
aggregates
and
agglomerates
of
aggregates.
Not
only
for
carbon
materials
and
the
method
serves
also
for
hybrids,
as
for
individual
materials
as
for
hybrids
also.
So
let's
start
from
the
basics.
In
multivariate
analysis
indicates
that
not
all
the
parameters
around
the
health
are
well
related
with
each
other
in
case
of
non
carbon
materials.
Specifically
there
a meso
porosity
dominates
overly
serious
as
shown
on
the
heat
map
and
from
the
table.
The
parameters
corresponding
to
microporosity
are
demonstrating
correlation
only
within
their
group
and
group
and
with
macroporosity.
Yes,
surprisingly.
Contrastingly,
the
heat
map
of
the
carbonized
nano
hybrid
speaks
for
more
consistent
and
more
ordered
structure
with
almost
complete
correlation
between
all
the
types
of
the
porosity,
whereas
the
role
of
microporosity
is
significantly
increased.
Comparison
between
parameters
or
variances
reveals
the
differences
especially
for
micro
porosity.
For the specific
surface
area,
as
for a total pore
volume,
for example, total pore volume
and
volume
of
the
mesop
ores
can
be
also
considered
as
factors
to
claim
the
difference
between
both
types
of
the nanomaterials.
All
eight
parameters
were
taken
to
perform
hierarchical
clustering
and
it
is
easy
to
see
that
the
minimal
optimal
in
the
same
time
model
can
be
offered
for
three
clusters
on
the
cluster
path
and
on
the
constellation
plant.
Think oxide
sample, it cannot
separate
within
the
non
carbon
group
but
can
be
attributed
in
other
carbonized
cluster.
Well,
main
parameters
as
seen
from
the
summary
are
the
volume
and
surface
of
the
micro
pores.
In
other
words,
micro
porosity.
Indeed,
some
parameters;
surface
and
volume
of
macro
and
mesopores
are
out
of
the
group
samples
of
the
non
carbon
nanomaterials.
I'm
talking
about,
namely
for
a
sync oxide.
Anyway,
the
mean
values
for
both
parameters
do
not
match
over
the
whole
parallel
plot
of
their
clusters.
Principal
components
analysis
help
to
represent
all
variables
in
three
linear
combinations.
According
to
the
[inaudible 00:09:02]
,
the eigen
values
less
than 1
are
not
taken
into
consideration.
This
is
why
we
have
only
three
components
whose
values
are
higher
than
1.
As
the
two
first
describe
almost
80%
of
the
samples
or
nine
samples
from
12,
with
the
help
of
the
third
component,
the
least
important,
almost
all
the
samples
fit
such
a
model.
The
first
component
comprises
both
micropores
parameters
[inaudible 00:09:39]
and
volume
of
the
meso
pores.
The
other
variables
take
a
secondary
role
in
the
second
and
third
components,
describing
together
another
half
of
the
temples.
Mapping
the
points
over
the
score
plots
in
3D,
it
is
easy
to
conclude
that
both
groups,
carbonized
and
non
carbonized,
can
be
separated
into
two
clusters
defined
by
three
principal
components
and two
almost
flat
clusters
comprising
the
points
situated
on
the
plane
are
set
by
two
main
clustering
algorithms
and described,
yes,
by
these
three
components.
Taking
a
closer
look
at
the
results
of
predictor
screening
by
boosting,
again
microporosity
is
referred
to
be
the
main
property.
The simplified
analysis,
two
variables
can
be
extracted
and
plotted
with
the
PCA
cluster,
I mean using
the
same
PCA,
however,
with
completely
different
amounts,
one
instead
of three
components,
and
yes,
whereas
a
single
principal
component
serves
to
describe
the
cluster
model.
The
cluster
formula
and
equation
for
the
principal
component
are
provided.
It
can
be
recommended
for
future
classification
or
for
a
qualitative
analysis
of
synthesized
samples.
As
conclusions, I
can
say
that
the
presented
synthesis
method
makes
it
possible
to
obtain
mesoporous
carbon
nanocomposites
uniformly
filled
with
metal
and
metal
oxide
phases
which
were
pre-synthesized
in
silicon
matrix.
With
the carbonization,
the
portion
of
micro
pores
is
growing,
yes,
the
specific
surface
area
increased
with
decreased
total
porosity,
total
pore
volume.
High
order
hybrid
carbon
oxide
nanocomposites
with large specific
surface
area.
The
controlled
size
distribution
of
the
modifier,
which
is
important
from
the
clinical
point
of
view,
and
significantly
expands
the
scope
of
application
of
the
synthesized
materials.
Parameters
of
textural
properties
are
effective
variable
helpful
to
identify
classified
nanooxide
materials.
Data visualization
has
given
insights
to
select
adequate
approaches
to
analyze
the
experimental
data.
K-Means
Clustering,
Self
Organized
Map,
and
Hierarchical C
lustering
have
proven
to
be
powerful
tools
for
classification
of
the
subject
materials
by
actual
properties.
Principal
Component
Analysis
in
turn
had
reduced
the
set
of
variables
for
a
definite
and
simple
classification.
The
study
claims
two
cluster
models
described
by
three
or
even
one
principal
component
to
classify
carbonized
and
carbon-
free
hybrid
nano
composites.
I'm
thankful
also
for
the
financial
support
for
Brazilian
agency.
I'm
very
thankful
to
my
colleague
David
Kirmayer
from
the
Hebrew
University
of
Jerusalem
and
one
of
the
co
authors
Maria
Galaburda,
who actually
synthesized
these
samples.
Thanks to P
olish
Foundations
and
International
Visegrad
Fund.
Thank
you
very
much
for
your
attention.
It
was
a
pleasure
for
me
to
make
such
a
presentation.
