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    An investigation of gamma ray mass attenuation from 80.1 to 834.86 keV for fabric coating pastes used in textile sector
    (Springer Singapore Pte Ltd, 2020) Erenler, Alev; Bayram, Tuncay; Demirel, Yusuf; Cengiz, Erhan; Bayrak, Riza
    In the present study, we investigate several textile coating pastes used in the market based on their radiation protection capability for gamma rays. The gamma ray mass absorption coefficients of some coating pastes doped with antimony, boron and silver elements have been investigated. It has been determined that the gamma ray mass attenuation coefficient decreases rapidly as the energy of the gamma rays increases. It was determined that the doping of the main printing paste with silver and antimony considerably increased the gamma ray absorption capability of main paste. However, the doping of the paste with boron reduces the mass absorption of gamma rays. In particular, the gamma ray mass absorption power of the main paste doped with silver and antimony was determined to be useful in the gamma energy range from 80 to 140 keV. This indicates that the newly doped textile material may be considered for radiation protection in the case of low-energy gamma rays .
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    Öğe
    INVESTIGATION AND PREDICTION OF CHOSEN COMFORT PROPERTIES ON WOVEN FABRICS FOR CLOTHING
    (Ege Universitesi, 2015) Erenler, Alev; Ogulata, R. Tugrul
    In the content of the study, it was investigated that the effects of various production parameters on the fabric comfort properties of clothing aimed woven fabrics by statistical analyze and it was tried to predict the comfort properties of fabrics by using production parameters. In the scope of the study, it was analyzed by using statistical methods that the effects of selected production parameters which were weft fiber type, weft density, weft yarn count, weaving pattern, fabric thickness and fabric weight on the fabric comfort properties which were fabric air permeability, stiffness and relative wafer vapor permeability(RWVP) Also it was established suitable Artificial Neural Network (ANN) Models by using MATLAB(R) programme for predicting fabric air permeability, fabric stiffness and fabric relative water vapor permeability with using selected production parameters as inputs. As a consequence, the statistical models established for each one of the comfort specialties was seen to be meaningful with the value of p<0.0001. Also the production parameters examined in the study were defined to be meaningful on the comfort specialties statistically. In the content of the study, it was revealed that the fabric comfort specialties can be predicted successfully before manufacture via established ANN Models.