Publications by authors named "Esma Sezer"

The purpose of this study was the polymerization of carbazole (Cz), pyrrole (Py) and copolymerization of them onto polyester (PES) textile with chemical oxidative method by using FeCl. First, in order to determine the optimum conditions, the effect of polymerization steps, the immersion order of the PES to the oxidant and monomer solutions, time, monomer, oxidant, and surfactant concentrations and types on the conductivities of PES/PCz composite were investigated and at this conditions, conductive composite, PES/P[Py-co-Cz] was obtained. The highest conductivities were obtained as 12.

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In this study, single-side polypyrrole (PPy) coated conductive stretchable polyurethane (PU) / PPy composite films were synthesized by chemical oxidative method in the acetonitrile (ACN)/HO interface as a new alternative to two-side coated PU films which have already reported in the literature. One surface of the PU film was contacted with aqueous Py solution and the other surface with cerium ammonium nitrate (CAN) solution in ACN, so that only one surface of the film was coated with PPy and the resulting composite was named PU/PPy (PUP). This composite was characterized by four-point probe conductivity measurement, Fourier-transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), scanning electron microscopy (SEM) measurements, and mechanical testing.

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A series of indole (In) and carbazole (Cz) derivative monomers have been synthesized, such as 4-[3-carbazolyl] indole (4In-3Cz), 5-[3-carbazolyl] indole (5In-3Cz), 6-[3-carbazolyl] indole (6In-3Cz), 7-[3-carbazolyl] indole (7In-3Cz). The comonomers synthesized by Stille coupling reaction were characterized by H-NMR and elemental analysis. Potentiodynamic method was used for electropolymerization of comonomers, Indole, Cz, and the mixture of In and Cz.

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This study aimed at synthesizing hydrogels to simulate opaque breast tissue (BT) and coloured cancerous tissues (CT) at different densities of the designed phantom to improve the biopsy-related skills along with ultrasonography. Both tissues are tear-resistant and therefore, the phantom can be trained multiple times in order to lower the price and improve the eye-hand coordination of users. For this purpose, self-healing (SH) polyacrylamide (PAAm) hydrogels (SH hydrogel) obtained by free-radical polymerization of AAm, in the presence of chemical cross-linker, BAAm, physical cross-linker stearyl methacrylate, C18, and ammonium persulfate APS as initiator were used in the design of phantoms.

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Titanium dioxide/phthalocyanine (TiO/Pc), TiO/fluor containing phthalocyanine (TiO/FPc), and TiO/fluor containing cobalt phthalocyanine (TiO/FCoPc) had been successfully fabricated by a simple combination of phthalocyanines obtained by in-situ synthesis on the surface of TiO nanofibers prepared by electrospinning. Scanning electron microscopy micrographs and X-ray diffraction analysis indicated that the phthalocyanines uniformly immobilized on the surface of TiO nanofibers. Photocatalytic activity of TiO, TiO/Pc, TiO/FPc, TiO/FCoPc nanofibers for methylene blue in water was comparatively investigated firstly by ultraviolet-visible absorption measurements with time, and kinetic parameters were calculated.

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Nitro-explosives contain reducible aromatic -NO2 groups or cyclic >N-NO2 bonds that may undergo reductive cleavage. This work reports the development of a cyclic voltammetric (CV) assay for nitro-aromatics (trinitrotoluene (TNT), dinitrotoluene (DNT)) and nitramines (1,3,5-trinitro-1,3,5-triazacyclohexane (RDX) and octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (HMX)) using a glassy carbon electrode. This determination was first used for these energetic materials by resolving current responses of reduction potentials primarily due to one constituent but partly contributed by other constituents.

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In this study, it has been intended to perform an automatic classification of Electroencephalography (EEG) signals via Artificial Neural Networks (ANN) and to investigate these signals using Wavelet Transform (WT) for diagnosing epilepsy syndrome. EEG signals have been decomposed into frequency sub-bands using WT and a set of feature vectors which were extracted from the sub-bands. Dimensions of these feature vectors have been reduced via Principal Component Analysis (PCA) method and then classified as epileptic or healthy using Multilayer Perceptron (MLP) and ELMAN ANN.

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In this study, it has been intended to analyze Electroencephalography (EEG) signals by Wavelet Transform (WT) for diagnosis of epilepsy, to employ various Artificial Neural Networks (ANNs) for the signals' automatic classification. Furthermore, carrying out a performance comparison has been aimed. Three EEG signals have been decomposed into frequency sub bands by WT and the feature vectors have been extracted from these sub bands.

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