Browsing by Subject "Trace analysis"
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Item Open Access Highly fluorescent pyrene-functional polystyrene copolymer nanofibers for enhanced sensing performance of TNT(American Chemical Society, 2015) Senthamizhan, A.; Celebioglu A.; Bayir, S.; Gorur, M.; Doganci, E.; Yilmaz, F.; Uyar, TamerA pyrene-functional polystyrene copolymer was prepared via 1,3-dipolar cycloaddition reaction (Sharpless-type click recation) between azide-functional styrene copolymer and 1-ethynylpyrene. Subsequently, nanofibers of pyrene-functional polystyrene copolymer were obtained by using electrospinning technique. The nanofibers thus obtained, found to preserve their parent fluorescence nature, confirmed the avoidance of aggregation during fiber formation. The trace detection of trinitrotoluene (TNT) in water with a detection limit of 5 nM was demonstrated, which is much lower than the maximum allowable limit set by the U.S. Environmental Protection Agency. Interestingly, the sensing performance was found to be selective toward TNT in water, even in the presence of higher concentrations of toxic metal pollutants such as Cd2+, Co2+, Cu2+, and Hg2+. The enhanced sensing performance was found to be due to the enlarged contact area and intrinsic nanoporous fiber morphology. Effortlessly, the visual colorimetric sensing performance can be seen by naked eye with a color change in a response time of few seconds. Furthermore, vapor-phase detection of TNT was studied, and the results are discussed herein. In terms of practical application, electrospun nanofibrous web of pyrene-functional polystyrene copolymer has various salient features including flexibility, reproducibility, and ease of use, and visual outputs increase their value and add to their advantage.Item Open Access Novel molecular building blocks based on the boradiazaindacene chromophore: applications in fluorescent metallosupramolecular coordination polymers(2009) Bozdemir, Ö. A.; Büyükcakir, O.; Akkaya, E. U.We designed and synthesized novel boradiazaindacene (Bodipy) derivatives that are appropriately functionalized for metal-ion-mediated supramolecular polymerization. Thus, ligands for 2-terpyridyl-, 2,6-terpyridyl-, and bipyridyl-functionalized Bodipy dyes were synthesized through Sonogashira couplings. These fluorescent building blocks are responsive to metal ions in a stoichiometry-dependent manner. Octahedral coordinating metal ions such as Zn II result in polymerization at a stoichiometry corresponding to two terpyridyl ligands to one Zn II ion. However, at increased metal ion concentrations, the dynamic equilibria are re-established in such a way that the monomeric metal complex dominates. The position of equilibria can easily be monitored by 1H NMR and fluorescence spectroscopies. As expected, although open-shell Fe II ions form similar complex structures, these cations quench the fluorescence emission of all four functionalized Bodipy ligands. © 2009 Wiley-VCH Verlag GmbH & Co. KGaA.Item Open Access Target classification with simple infrared sensors using artificial neural networks(IEEE, 2008-10) Aytaç, T.; Barshan, BillurThis study investigates the use of low-cost infrared (IR) sensors for the determination of geometry and surface properties of commonly encountered features or targets in indoor environments, such as planes, corners, edges, and cylinders using artificial neural networks (ANNs). The intensity measurements obtained from such sensors are highly dependent on the location, geometry, and surface properties of the reflecting target in a way which cannot be represented by a simple analytical relationship, therefore complicating the localization and classification process. We propose the use of angular intensity scans and feature vectors obtained by modeling of angular intensity scans and present two different neural network based approaches in order to classify the geometry and/or the surface type of the targets. In the first case, where planes, 90° corners, and 90° edges covered with aluminum, white cloth, and Styrofoam packaging material are differentiated, an average correct classification rate of 78% of both geometry and surface over all target types is achieved. In the second case, where planes, 90° edges, and cylinders covered with different surface materials are differentiated, an average correct classification rate of 99.5% is achieved. The method demonstrated shows that ANNs can be used to extract substantially more information than IR sensors are commonly employed for. © 2008 IEEE.