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Mesoporous silica SBA-15 containing propyl-iron-phosphonate groups were considered to confirm their molecular structure. To detect the iron-containing group configuration the Mössbauer spectroscopy was used. Both mesoporous silica SBA-15 containing propyl-iron-phosphonate groups and pure doping agent (iron acetylacetate) were investigated using Mössbauer spectroscopy. The parameters such as isomer shift, quadrupole splitting, and asymmetry in57Fe Mössbauer spectra were analyzed. The differences in Mössbauer spectra were explained assuming different local surroundings of Fe nuclei. On this base we were able to conclude about activation of phosphonate units by iron ions and determinate the oxidation state of the metal ion. To examine bonding between iron atoms and phosphonic units the resonance Raman spectroscopy was applied. The density functional theory (DFT) approach was used to make adequate calculations. The distribution of active units inside silica matrix was estimated by comparison of calculated vibrational spectra with the experimental ones. Analysis of both Mössbauer and resonance Raman spectra seems to confirm the correctness of the synthesis procedure. Also EDX elemental analysis confirms our conclusions.

Nowadays, unprecedented amounts of heterogeneous data collections are stored, processed and transmitted via the Internet. In data analysis one of the most important problems is to verify whether data observed or/and collected in time are genuine and stationary, i.e. the information sources did not change their characteristics. There is a variety of data types: texts, images, audio or video files or streams, metadata descriptions, thereby ordinary numbers. All of them changes in many ways. If the change happens the next question is what is the essence of this change and when and where the change has occurred. The main focus of this paper is detection of change and classification of its type. Many algorithms have been proposed to detect abnormalities and deviations in the data. In this paper we propose a new approach for abrupt changes detection based on the Parzen kernel estimation of the partial derivatives of the multivariate regression functions in presence of probabilistic noise. The proposed change detection algorithm is applied to oneand two-dimensional patterns to detect the abrupt changes.

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