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 the… 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.
