Synthesis and characterization of poly(N-(2-(thiophen-3-yl)methylcarbonyloxyethyl)maleimide) and its spectroelectrochemical properties

A new monomer; N-(2-(thiophen-3-yl)methylcarbonyloxyethyl) maleimide (NMT) was synthesized. The chemical structure of the monomer was characterized by Nuclear Magnetic Resonance (¹H-NMR) and Fourier Transform Infrared (FTIR) Spectroscopy. Electrochemical polymerization of NMT was performed in acetonitrile (AN)/borontrifloride ethylether (BFEE) solvent mixture (1:1, v/v) where tetrabutylammonium tetrafluoroborate (TBAFB) was utilized as the supporting electrolyte. The resulting conducting polymer was characterized by Fourier Transform Infrared (FTIR) Spectroscopy, thermal analyses and Scanning Electron Microscopy (SEM). Electrical conductivity was measured by the four-probe technique. The spectroelectrochemical behavior and switching ability of P(NMT) film were investigated by UV–Vis spectrophotometry. P(NMT) revealed color changes between yellow and blue in the reduced and oxidized states respectively.     

Determination of flavan-3-ols and trans-resveratrol in grapes and wine using HPLC with fluorescence detection

Concentrations of trans-resveratrol, catechin and epicatechin were analyzed in musts and wines produced from seven red and four white grape cultivars from various wine growing regions of Turkey. Phenolics were quantified using an HPLC method optimized for the separation of wine phenolics. Wine samples contained higher phenolics levels than the corresponding musts. With the exception of Semillion, white wines and musts contained lower concentrations of phenolics than red wines and musts. However, the white cultivar Semillion had the highest concentrations of catechin and epicatechin among all wine and must samples. Semillion wine catechin and epicatechin were 13.7 and 11.8 mg/L, respectively. The highest level of trans-resveratrol among the white cultivars was found in Narince wine (1.93 mg/L). Within the red wine and must cultivars, Bo?azkere, Öküzgozü, and Cabernet contained the highest concentrations of flavan-3-ols and trans-resveratrol. Catechin was the major phenolic in all wines and most musts. Epicatechin was the major phenolic in 6 of the 11 must samples, but none of the wine samples. trans-Resveratrol was generally found in lowest concentrations in both wines and musts.     

Numerical modelling of honeycomb core crush behaviour

In this work several numerical techniques for modelling the transverse crush behaviour of honeycomb core materials were developed and compared with test data on aluminium and Nomex™ honeycomb. The methods included a detailed honeycomb micromechanics model, a homogenised material model suitable for use in FE code solid elements, and a homogenised discrete/finite element model used in a semi-adaptive numerical coupling (SAC) technique. The micromechanics model is shown to be suitable for honeycomb design, since it may be used to compute crush energy absorption for different honeycomb cell sizes, cell wall thicknesses and cell materials. However, the very fine meshes required make it unsuitable for analysis of large sandwich structures. The homogenised FE model may be used for such structures, but gives poor agreement when failure is due to core crushing. The SAC model is shown to be most appropriate for use in structural simulations with extensive compression core crushing failures, since the discrete particles are able to model the material compaction during local crushing.     

3D Model compression using Connectivity-Guided Adaptive Wavelet Transform built into 2D SPIHT

Connectivity-Guided Adaptive Wavelet Transform based mesh compression framework is proposed. The transformation uses the connectivity information of the 3D model to exploit the inter-pixel correlations. Orthographic projection is used for converting the 3D mesh into a 2D image-like representation. The proposed conversion method does not change the connectivity among the vertices of the 3D model. There is a correlation between the pixels of the composed image due to the connectivity of the 3D mesh. The proposed wavelet transform uses an adaptive predictor that exploits the connectivity information of the 3D model. Known image compression tools cannot take advantage of the correlations between the samples. The wavelet transformed data is then encoded using a zero-tree wavelet based method. Since the encoder creates a hierarchical bitstream, the proposed technique is a progressive mesh compression technique. Experimental results show that the proposed method has a better rate distortion performance than MPEG-3DGC/MPEG-4 mesh coder.     

Donor-acceptor-donor type conjugated polymers for electrochromic applications: benzimidazole as the acceptor unit

Donor-acceptor-donor (DAD) type benzimidazole (BIm) and 3,4 ethylenedioxythiophene (EDOT) bearing monomers were synthesized and electrochemically polymerized. Pendant group at 2-C position of the imidazole ring was functionalized with phenyl (P1), EDOT (P2) and ferrocene (P3) in order to observe substituent effect on electrochemical and electrochromic properties of corresponding polymers. Spectroelectrochemical results showed that different pendant groups resulted in polymers with slightly different optical band gaps (1.75, 1.69 and 1.77 eV respectively) and different number of achievable colored states. Optoelectronic performance and comparison of results with other well known π-accepting benzazole bearing DAD type polymers were reported in detail.     

Prediction of the pH and the temperature-dependent swelling behavior of -alginate hydrogels by artificial neural networks

This paper considers the possibility of using artificial neural network models to identify model for swelling behavior as new techniques. Multi-layer feed-forward, radial basis function and generalized regression neural network models were employed to predict the swelling behaviors of Ca²⁺-alginate hydrogels under different environmental conditions of pH and temperature. The results show that an excellent correlation between the experimental and predicted swelling ratios was obtained by the artificial neural networks. Generalized regression neural network has a better performance than the other neural network models. The absolute mean error, the determination coefficient and the standard error of prediction were used as performance criteria. In addition, the performances of the neural network models are significantly superior compared with those of second-order swelling kinetics, quadratic and cubic models of response surface methodology.     

Investigation of Sn-Zn electrodeposition from acidic bath on EQCM

Tin-zinc (Sn-Zn) alloy with low tin content was deposited on gold electrode and steel substrate with use of chronoamperometric technique from an acidic bath. In order to evaluate coating efficiency of Sn-Zn alloy in 0.5 M NaCl solution, open circuit potential-time curve (E_OCP-t), polarization curves, mass change of the electrode (Δm-t) using quartz crystal microbalance (QCM) were compared to those of pure Sn and Zn coatings. Anodic stripping measurements were carried out simultaneously with the mass loss of the deposit. Scanning electron microscopy (SEM) and energy dispersive X-ray spectra (EDS) analysis were performed to characterize the surface morphology. Anodic stripping experiment and EDS analysis indicated that Sn, Zn, and SnO₂ formed on the electrode surface when Sn-Zn was coated from acidic bath. Furthermore, local mapping demonstrated homogeneous distribution of Sn and Zn atoms throughout the surface.     

From engineering diagrams to engineering models: Visual recognition and applications

We present a computational recognition approach to convert network-like, image-based engineering diagrams into engineering models with which computations of interests, such as CAD modeling, simulation, information retrieval and semantic-aware editing, are enabled. The proposed approach is designed to work on diagrams produced using computer-aided drawing tools or hand sketches, and does not rely on temporal information for recognition. Our approach leverages a Convolutional Neural Network (CNN) as a trainable engineering symbol recognizer. The CNN is capable of learning the visual features of the defined symbol categories from a few user-supplied prototypical diagrams and a set of synthetically generated training samples. When deployed, the trained CNN is applied either to the entire input diagram using a multi-scale sliding window or, where applicable, to each isolated pixel cluster obtained through Connected Component Analysis (CCA). Then the connectivity between the detected symbols are analyzed to obtain an attributed graph representing the engineering model conveyed by the diagram. We evaluate the performance of the approach with benchmark datasets and demonstrate its utility in different application scenarios, including the construction and simulation of control system or mechanical vibratory system models from hand-sketched or camera-captured images, content-based image retrieval for resonant circuits and sematic-aware image editing for floor plans.     

Sliding mode control of a bioreactor

In this paper, sliding mode control (SMC) of a bioreactor is considered and is compared with PID control. The magnitude of the error in SMC is found to be lower than that in PID control. Moreover, the magnitudes of cells and nutrients were very close to the selected reference values in SMC, whereas they were quite different in PID control. Overall, SMC was more robust against disturbances and had better performance than PID control.     

Effects of chromium (VI) on the activities of ureolytic mixed culture

BACKGROUND: Ureolytic microbial carbonate precipitation (MCP) is a novel process for removing excess calcium from industrial effluents. This process is based on the hydrolization of urea to provide a suitable medium for the precipitation of Ca as CaCO₃. RESULT: A toxicity identification evaluation was conducted on synthetic wastewater simulating wastewater from paper recycling to determine the inhibition or toxicity of chromium(VI) ions on the activities of a ureolytic mixed culture (UMC) with respect to the removal rate of COD and removal of Ca²⁺ in batch reactors. The 50% inhibiting concentration (IC₅₀) and the 25% inhibiting concentration (IC₂₅) values of Cr(VI) on UMC were determined as 40 and 18 mg L^?1, respectively, for an exposure time of 1 day. The inhibitory effects of Cr(VI) were lower on UMC after longer exposure times of 3, 4 and 5 days. COD removal rate, ammonium production and Ca removal of samples with 512 mg Cr(VI) L^?1 were significantly inhibited. CONCLUSION The present study has shown that the effects of different metallic species, organic toxicants and other environmental factors should be taken into account when removing problematic Ca and other possible elements from the environment using UMC. Copyright © 2008 Society of Chemical Industry