Synthesis and properties of radiation‐induced acrylamide–acrylic acid hydrogels

Acrylamide (AAm)/acrylic acid (AAc) hydrogels in the cylindirical form were prepared by γ‐irradiating binary systems of AAm/AAc with 2.6–20.0 kGy γ‐rays. The effect of the dose and relative amounts of AAc and pH on the swelling properties, diffusion behavior of water, diffusion coefficients, and network properties of hydrogel systems was investigated. The swelling capacities of AAm/AAc hydrogels were in the range of 1000–3000%, while poly(acrylamide) (PAAm) hydrogels swelled in the range of 450–700%. Water diffusion into hydrogels was found to be non‐Fickian‐type diffusion. Diffusion coefficients of AAm/AAc hydrogels were found between 0.79 × 10^?5 and 2.78 × 10^?5 cm² min^?1. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 86: 3570–3580, 2002     

Effect of composite preparation techniques on electrical and mechanical properties and morphology of nylon 6 based conductive polymer composites

Nylon 6/carbon black conductive composites were prepared using two different methods, masterbatch dilution and melt mixing. Their effect on the size and distribution of carbon black agglomerates in the matrix was studied in terms of electrical and mechanical properties and morphology. The electrical resistivity of composites prepared by both methods decreased with increasing filler composition. The electrical resistivity of the diluted masterbatch composites and the melt mixed composites was reduced from the resistivity of pure nylon 6, 10¹⁵ “ohm, cm”, to 10⁷ “ohm, cm” at 1 and 6 wt % of carbon black, respectively. As the filler content increased, elongation at break and impact strength decreased, but tensile modulus increased. Masterbatch dilution method provided smaller carbon black clusters in composites compared to melt mixing method. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 102: 2520–2526, 2006     

Effects of polyamide 6 incorporation to the short glass fiber reinforced ABS composites: an interfacial approach

The properties of 30 wt% short glass fiber (SGF) reinforced acrylonitrile-butadiene-styrene (ABS) terpolymer and polyamide 6 (PA6) blends prepared with extrusion were studied using the interfacial adhesion approach. Work of adhesion and interlaminar shear strength values were calculated respectively from experimentally determined interfacial tensions and short beam flexural tests. The adhesion capacities of glass fibers with different surface treatments of organosilanes were evaluated. Among the different silanes tested, γ-aminopropyltrimethoxysilane (APS) was found to be the best coupling agent for the glass fibers, possibly, because of its chemical compatibility with PA6. Tensile test results indicated that increasing amount of PA6 in the polymer matrix improved the strength and stiffness of the composites due to a strong acid–base interaction at the interface. Incorporation of PA6 to the SGF reinforced ABS reduced the melt viscosity, broadened the fiber length distributions and increased the toughness of the composites. Fractographic analysis showed that the incorporation of PA6 enhanced the interactions between glass fibers and the polymeric matrix.     

Wavelet based Neuro-Detector for low frequencies of vibration signals in electric motors

This study presents a Wavelet based Neuro-Detector approach employed to detect the aging indications of an electric motor. Analysis of the aging indications, which can be seen in the low frequency region, is performed using vibration signals. More specifically, two vibration signals are observed for healthy and faulty (aged) cases which are measured from the same electric motor. Multi Resolution Wavelet Analysis (MRWA) is applied in order to obtain low and high frequency bands of the vibration signals. Thus for detecting the aging properties in the spectra, the Power Spectral Density (PSD) of the subband for the healthy case is used to train an Auto Associative Neural Network (AANN). The PSD amplitudes, which are computed for the faulty case, are applied to input nodes of the trained network for the re-calling process of AANN. Consequently, the simulation results show that some spectral properties defined in low frequency region are determined through the error response of AANN. Hence, some specific frequencies of the bearing damage related to the aging process are detected and identified.     

Numerical comparison of methods for solving fractional differential–algebraic equations (FDAEs)

In this paper, the variational iteration method (VIM) and the Adomian decomposition method (ADM) are implemented to give approximate solutions for fractional differential–algebraic equations (FDAEs). Both methods in applied mathematics can be used as alternative methods for obtaining analytic and approximate solutions for different types of fractional differential equations. This paper presents a numerical comparison between these two methods and the homotopy analysis method (HAM) for solving FDAEs. Numerical results reveal that the VIM and the ADM are quite accurate and applicable.     

Estimation of adaptive neuro-fuzzy inference system parameters with the expectation maximization algorithm and extended Kalman smoother

In this paper, we propose a new supervised learning method for adaptive neuro-fuzzy inference system (ANFIS) training, which uses the expectation maximization (EM) algorithm and extended Kalman smoother (EKS) together; we refer to it here as the EM-EKS training method. While the EKS tunes the ANFIS parameters, the EM algorithm estimates the parameters of the Kalman filter and avoids non-optimal performance. Besides, we also propose a new algorithm to select the initial values of the EKS parameters. We compare the EM-EKS method of ANFIS training with traditional ANFIS training. Although the new training method requires more computing time, it yields improved RMSE values in function approximation and prediction problems. Examples of benchmark function approximation and prediction illustrate the effectiveness of the EM-EKS ANFIS training method.     

Tuberculosis disease diagnosis by using adaptive neuro fuzzy inference system and rough sets

A correct diagnosis of tuberculosis disease can be only stated by applying a medical test to patient’s phlegm. The result of this test is obtained after a time period of about 45 days. The purpose of this study is to develop a data mining solution that makes diagnosis of tuberculosis as accurate as possible and helps deciding whether it is reasonable to start tuberculosis treatment on suspected patients without waiting for the exact medical test results. We proposed the use of Sugeno-type “adaptive-network-based fuzzy inference system” (ANFIS) to predict the existence of mycobacterium tuberculosis. Data set collected from 503 different patient records which are obtained from a private health clinic (consent of physicians and patients). Patient record has 30 different attributes which covers demographical and medical test data. ANFIS model was generated by using 250 records. Also, rough set method was implemented by using the same data set. The ANFIS model classifies the instances with correctness of 97 %, whereas rough set algorithm does the same classification with correctness of 92 %. This study has a contribution on forecasting patients before the medical tests.     

Synthesis,characterization and cytotoxicity of novel modified poly[(maleic anhydride)‐ co‐(vinyl acetate)]/noradrenaline conjugate

Poly[(maleic anhydride)‐co‐(vinyl acetate)] (MAVA) copolymer was synthesized by free radical polymerization reaction, in methyl ethyl ketone at 80 °C, using benzoyl peroxide as the initiator. The copolymer was then modified with a biomolecule, noradrenaline (NA). The modification reaction was performed at 70 °C in dimethylformamide containing triethylamine as the catalyst. The modified polymer was named MAVA/NA. Structural characterization of the copolymer and the modified product was carried out by Fourier transform infrared (FTIR) and ¹H NMR and ¹³C NMR spectroscopy. The FTIR, ¹H NMR and ¹³C NMR spectra confirmed that NA was successfully covalently bound to the MAVA copolymer backbone. Surface morphology was visualized by atomic force microscopy. The cumulative release of NA from MAVA/NA was determined in phosphate buffered saline solution for 7 days at 37 °C and compared with MAVA. Cytotoxicity of the MAVA/NA was evaluated by using a mouse fibroblast cell line (L929). Results obtained indicated that MAVA/NA had almost no toxicity and no negative effect on cell viability at 250 µg mL^?1 concentration. © 2012 Society of Chemical Industry     

Plasma-activated direct bonding of diamond-on-insulator wafers to thermal oxide grown silicon wafers

Diamond-on-insulator (DOI) wafers featuring ultrananocrystalline diamond are studied via atomic force microscopy, profilometer and wafer bow measurements. Plasma-activated direct bonding of DOI wafers to thermal oxide grown silicon wafers is investigated under vacuum. DOI wafer with chemical mechanical polishing (CMP) on the diamond surface makes a poor bonding to silicon wafers with thermal oxide. Our results show that plasma enhanced chemical vapor deposition of silicon dioxide on top of the DOI wafer, CMP of the oxide layer and annealing are essential to achieve very high quality direct bonding to thermal oxide grown on silicon wafers. Plasma activation results in the formation of high quality bonds without exceeding 550 °C in the direct wafer bonding process.     

Heat Transfer and Pressure Drop of Nanofluids in a Microchannel Heat Sink

The aim of this study is to determine the upper limitations of the particle volume fraction for heat transfer performance of TiO₂–water nanofluids in microchannels. Nanofluids were prepared by the addition of TiO₂ metallic nanoparticles into distilled water chosen as base fluid at five different volumetric ratios (0.25%, 0.5%, 1.0%, 1.5%, and 2.0%). The effects of the Reynolds number (100–750) and particle volume fraction at constant microchannel height (200 μm) on heat transfer and pressure drop characteristics were analyzed experimentally. Adding metallic oxide particles with nano dimensions into the base fluid did not cause excessive increase of friction coefficient but provided higher heat transfer than that of pure water. It was also observed that water–TiO₂ nanofluid increased heat transfer up to 2.0 vol%, but heat transfer decreased after 2.0 vol%. Furthermore, the thermal resistance was calculated and it was seen that adding nanoparticles with an average diameter smaller than 25 nm into the base fluid caused the thermal resistance to decrease.