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Textiles and Polymers - Volume:4 Issue: 2, Spring 2016

Journal of Textiles and Polymers
Volume:4 Issue: 2, Spring 2016

  • تاریخ انتشار: 1395/07/30
  • تعداد عناوین: 7
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  • Mina Emadi, Maryam Sharzehee, Algy Kazlauciunas Pages 53-59
    A feasibility study on the possible growth of rod-shaped nano size zinc oxide particles on the surface of polyester fabric was investigated. The nanoparticles were produced using a hydrolysis method, with a zinc compound being utilized as the starter material. Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy/energy dispersive X-ray spectrometry (SEM/EDS) and X-ray diffraction (XRD) have been used to characterize the composition of the nano particles, as well as their shape, size and crystallinity. The application of nano zinc oxide onto the polyester fabric was carried out in aqueous solutions at high temperatures, using the exhaustion method. The effective parameters such as time, temperature, the concentration of the dispersed nanogel and also the rate of heating, were selected to study the adsorption of the nanoparticles on the polyester fabric. The elemental analysis technique SEM/EDS was used to evaluate the amount of zinc on each sample. Finally, the possible growth of nanorod zinc oxide on a prepared sample using the sol gel technique was tested and desirable results were confirmed by means of SEM analysis.
    Keywords: exhaustion method, nanorod, nano zinc oxide, polyester fabric
  • Jalal Chachi, S. Mahmoud Taheri, Saeed Fattahi, S. Abdolkarim Hosseini Ravandi Pages 60-68
    Using the generalized Hausdorff-metric, two least-absolutes (LA) approaches to multiple fuzzy regression modeling are introduced for the case of crisp input-fuzzy output data. The main advantage of the proposed models is that they are not so sensitive to the outlier data points. The proposed models as well as two common fuzzy least-squares (LS) models are employed in a case study to estimate imperfections of cotton yarn using fiber properties in a reallife data. In order to derive the fuzzy regression models between imperfections of cotton yarn and fiber properties, first, effective variables are selected by the statistical stepwise test. Then, four fuzzy models, including two new LA models and two LS models, are sought to fit the data set.
    Finally, two criteria are employed to evaluate the goodness-of-fit of models. Moreover, a predictive ability index is introduced and employed to evaluate the predictability of the models. Using these criteria, a comparative study between the proposed fuzzy least-absolutes regression models and fuzzy least-squares regression models has also been addressed. The comparison results reveal that the LA-fuzzy models perform better than the LS-fuzzy models in imperfections of cotton yarn estimation for the particular data set used in this study.
    Keywords: cross-validation, fuzzy least-absolutes regression, fuzzy least-squares regression, outlier, yarn quality properties
  • Analyzing the Tensile Behavior of Warp-Knitted Fabric Reinforced Composites Part I: Modeling the Geometry of Reinforcement
    Pages 68-74
  • Leila Salmani, Mahdi Nouri Pages 75-81
    A new method for preparing silk fibroin (SF) nanofibers with an improved texture and a porous surface is proposed. In order to prepare such nanofibers, SF/Poly (ethylene glycol) (PEG) blend nanofibers were electrospun and then PEG was extracted via selective dissolution technique using methanol. The morphological structure of the electrospun nanofibers were characterized using fieldemission scanning electron microscope (FE-SEM), infrared spectroscopy (FTIR, ATR), transmission electron microscopy (TEM), differential scanning calorimetry (DSC), and atomic force microscopy (AFM). Mechanical properties of the nanofiber mats were evaluated with the aid of stress-strain curves. FE-SEM and AFM images revealed a smooth surface for electrospun SF and SF/PEG blend nanofibers with an average fiber diameter in the range of 100-180 nm, while PEG extracted nanofibers showed a rough and porous surface with an average diameter in the range of 100-150 nm. The analysis of the ATR, DSC, and TEM tests revealed that SF/PEG blend is an immiscible two phase system in which PEG accumulated on the surface of the electrospun blend nanofibers. PEG extracted nanofibers showed no internal cavities with a remarkable increase in the surface roughness in comparison to SF and SF/PEG blend nanofibers.
    Keywords: characterization, electrospinning, poly (ethylene glycol), silk fibroin, surface roughening
  • Neda Dehghan, Pedram Payvandy, Mohammad Ali Tavanaie Pages 83-91
    Extracting two component nanofibers from blend polymers is one of the interesting methods of industrial production of nanofibers. Knowing the morphology of nanofibers structure is essential for improving their efficiency. Fiber diameter is one of the major structural properties, which is typically determined by manual measuring methods. However, manual fiber diameter calculation is a tedious and time consuming task as well as being sensitive to human errors. Therefore, an accurate and automated technique to measure the diameter of fibers is desired. In recent years, image processing methods have been commonly used to measure the diameter of nanofibers. In this study, image segmentation based on Fuzzy Clustering Method (FCM) and Distance Transform Method (DTM) was used to measure the diameter of nanofibers extracted from blend fiber. The diameter of nanofibers was calculated using proposed method and results were compared with other image processing algorithms and the manual method. The presented results showed that the FCM approach can be helpful for measuring the nanofiber diameter within a fibrous network.
    Keywords: diameter, FCM, image processing, nanofiber
  • Milad Fonouni, Reza Yegani, Akram Tavakkoli, Sanaz Mollazadeh Pages 92-100
    Wet chemical functionalization is an easy and efficient method, which connects the polar functional groups to the surface of polymeric membranes. In this work, KClO3, K2Cr2O7 and KMnO4 were dissolved in sulfuric acid and used to functionalize microporous polypropylene (PP) membranes, fabricated via thermally induced phase separation (TIPS) method. The optimum concentration of oxidizing agents and sulfuric acid as well as the membrane immersion time in oxidizing solutions were determined. The percentage of re-construction phenomenon was about 3.1%, 24% and 34.7% for PP membrane treated by KClO3, K2C2O7 and KMnO4, respectively. The results showed that the absorbance intensities of -OH and C=O peaks as well as the variety of functional groups in the samples treated by KClO3 are remarkably higher than that of the samples treated by K2Cr2O7 and KMnO4. BSA filtration experiments revealed that the total fouling ratio (TFR) and irreversible fouling ratio (IFR) decreased from 75% and 63.6% for pristine membrane to 50.3%, 53.4%, 55.6% and 27.6%, 27.6%, 27.3% for modified membranes by KClO3, K2Cr2O7 and KMnO4, respectively. The flux recovery (FR) of treated membranes was about twice higher than that of the nascent membrane. The results indicated that incorporation of hydrophilic functional groups on the surface of PP membranes improves the fouling resistance behavior.
    Keywords: functionalization, KClO3, K2Cr2O7, KMnO4, microporous polypropylene (PP) membrane, wet chemical oxidation
  • Parvaneh Kheirkhah Barzoki, Morteza Vadood, Majid Safar Johari Pages 101-105
    In this research, the compact-core spun yarns have been produced using RoCoS roller and the effects of filament pre-tension, yarn count and type of sheath fibers were investigated on the physical and mechanical properties of produced yarns such as strength, elongation percentage, hairiness, and abrasion resistance. After statistically analysis on the obtained results, for modeling the core-compact yarn properties, the regression and artificial neural network (ANN) were used to predict the physical and mechanical properties. Trial and error method was considered for determining the best of ANN topology. For this aim, 1110 topologies of ANN (with different hidden layers and neurons in each hidden layer) were investigated for each property. Moreover, to evaluate the accuracy of the created ANN three indexes were used, namely mean absolute percentage error (MAPE), mean square error (MSE), and correlation coefficient (R-value). It was observed that the most accurate results were obtained based on MAPE and the best topology for predicting all properties is a two-hidden layer ANN (maximum MAPE
    Keywords: artificial neural network, compact-core yarn, modeling, physical, mechanical properties, RoCoS roller