Identification of Silver Nanoparticles on Polyester Fiber on Raman Spectrograms of the in the Conditions of Information Uncertainty

The paper shows the results of method development identification of colloidal silver nanoparticles on the components of the Raman spectra, using the conditions information uncertainty decision to increase the reliability evaluating the presence nanoparticles at the surface polyester fibers.


Introduction
To provide biomedical, therapeutic and protective properties of textile materials using silver nanoparticles is necessary to use convergence nano-, bio-, info-, cognitive science and technology.
In physical effect used Raman light scattering (SERS) [1][2][3], which is based on plasmon enhancement signal components from the Raman spectrum in the presence of silver nanoparticles.In addition, the applied polarizing effect of the laser beam Raman spectrometer PE fibers with silver nanoparticles, which provides additional gain of combination of background and fluorescent components of the Raman spectrogram.

Experimental
To improve the reliability of the control of presence of small amounts of colloidal silver nanoparticles on polyester (PE) fibers used Raman spectrometer, followed by separation of the spectral components of informative and processing on mathematical models in conditions of information uncertainty.
In the experiment, selected PE fiber to which silver nanoparticles were deposited from a colloidal solution of silver nanoparticles AgBion-2 (TU 2499-003-44471019-2006, concern "Nanoindustry").Obtained following fiber samples: Sample 1-no nanoparticles; sample 2-nano-particulate dried in vivo; sample 3-nanoparticulate dried in an oven.The measurements were performed with a scanning probe microscope (SPM) with a confocal Raman and fluorescence spectrometer OmegaScope ™ -.
After the transfer of the digital portion of the Raman spectrograms of the program Spekwin32 in Mathcad, obtained spectrograms shown in Figure 1.Total of 10 spectrograms obtained.In order to take into account the background fluorescent component, which is present on the spectrograms presented should make the mathematiccal modeling of the background components for minimum data Raman spectrum [4,5].

Results and Discussions
We construct a matrix S0-S9 the intensities of all the peaks EE-EE9 each spectrogram Figures 1(a)-( From Figure 1 and matrix S0-S9 shows the maximum peak intensity E5 i,0 = 5820 cm −1 is observed in the spectrum of the fiber with silver nanoparticles dried in vivo.The intensity of the peak E5 i,0 = 5819.5cm −1 in said sample is S5 5,1 = 12539 that 4.65 times the peak E i,0 = 5820.8cm −1 range of the fiber without silver nanoparticles S0 5,1 = 2685.8. To eliminate uncertainty and identify patterns in the distribution of the parameters of the spectrograms spectrograms were ranked by the intensity of the peaks for the determination of the maximum, minimum and intermediate values of all the components of each of spectrogram peaks in the set of spectrograms fibers separately for fibers without nanoparticles (S0, S1, S2), with the nanoparticles in drying in vivo (S3, S4, S5, S6) and dried in oven (S7, S8, S9).
In the set of spectrograms fibers without nanoparticles S0, S1, S2 identified by rating (1)  spectrogram fibers nanoparticles: a minimum intensity peaks S6, with maximum intensity peaks S5 and intermediate intensity peaks S4 and S3.
In the spectrograms fibers nanoparticles S7, S8, S9 identified assessment (3) Figure 2 shows the spectrogram PE fibers with and without silver nanoparticles at different drying conditions with the combination of the samples peaks at minimum, maximum, and intermediate values of the peak intensities.
A pooled analysis of the results of modeling estimates (1-3) has shown that there is a significant difference in the intensities of the peaks of the spectrograms of PE fibers coated with silver nanoparticles and uncoated nanoparticles.Thus the spectrograms with minimum values of all the peaks S0, S6, S7 in Figure 2(a) clearly differs spectrum S0 fibers without silver nanoparticles having minimum peaks compared to spectrograms S6, S7 fibers with silver nanoparticles.
Thus, the method of the experiments and mathematical analysis of the spectrograms can be pro control of colloidal silver nanoparticles on the surface of PE fibers.
The work was supported by the Russian Ministry of Education on chnology in SEC-Nanoelectronics SWSU and ISSP.

Conclusions
1) Due to the larg parameters in the co ter (PE) fibers and considerable uncertainty in the laws of their manifestation is the most suitable method of mathematical processing parameters constituting spectrograms for informational uncertainty (fuzzy logic) when deciding on the presence of nanoparticles.
2) To eliminate uncertainty and identify patterns in the distribution of the parameters of the spect grams were ranked by the intensity of the peaks for the determination of the maximum, minimum and intermediate values of all the components of each of spectrogram peaks in many spectrograms fibers separately for fibers without nanoparticles, the nanoparticles upon drying under natural conditions and on drying cabinet.
3) A pooled analysis of simulation results showed that revealed a significant difference in the intensities o aks of the spectrograms of PE fibers coated with silver nanoparticles and uncoated nanoparticles.4) Identified in the spectrograms with the lowest va-es of all peaks S0, S6, S7 distinctly different spectrum S0 fibers without silver nanoparticles having a minimum peaks in comparison with the spectrograms S6, S7 fibers with silver nanoparticles.
5) In the spectrograms with aks S2, S5, S9 also revealed distinctly different spectrum S2 fibers without silver nanoparticles having a minimum peak in comparison with the spectrograms S5, S9 fibers with silver nanoparticles.
6) For spectrograms with interm aks S1, S3, S8 and clearly differs spectrum S1 fibers without silver nanoparticles having minimum peaks compared to spectrograms S3, S8 fibers with silver nanoparticles.

Figure 1 .
Figure 1.Spektrogram PE fibers without the background of fluorescent components: (a, b, c): Spectrogram PE fibers without silver nanoparticles; (d, e, f, g): Spectrogram PE fibers with silver nanoparticles deposited dried in natural conditions; (h, k, m): Spectrogram PE fibers deposited silver nanoparticles, dried in an oven.
nanoparticles: a minimum intensity peaks S8, with the maximum intensity peaks S9 and with intermediate intensity peaks S7.

Figure 2 .
Figure 2. Ranged spectrogram largest intensities of all the peaks: (a): Spectrogram PE fibers with the lowest values of the peak intensities: S0 without nanoparticles and silver nanoparticles, S6 and S7; (b): Spectrogram PE fibers with maximum values of peak intensities: S2 without nanoparticles and silver nanoparticles, S5 and S9; (c): Spectrogram PE fibers with intermediate values of the peak intensities: not S1 nanoparticles and silver nanoparticles in vivo dried S3 and oven S8.

Figure 2 (
b) also clearly differs spec-mum peaks compared to spectrograms S5, S9 fibers with silver nanoparticles.For spectrograms trum S2 fibers without silver nanoparticles having mini-with intermediate values of all the pe This is illustrated by mathematical modeling parame-aks S1, S3, S8 in Figure 2(c) also clearly differ spectrum S1 fibers without silver nanoparticles having minimum peaks compared to spectrograms S3, S8 fibers with silver nanoparticles.ters peaks with intensities expectation of spectrogram pe   Mathematical modeling parameter M min = 1.634 × 10 3 spectrograms without silver nanoparticles has a minim posed as methods of the equipment Regional Center for Nanote e scatter in the values of information ntrol of silver nanoparticles on polyes rograms spectro f the pe lu the highest values of all pe ediate values of all the pe isually checked difference from expectation spectro aks fibers (4) without silver nanoparticles as S0, S2, S1; nanoparticle and S6, S5, S3 and S7, S9, S8.