DYPE/PANI kompozit filmlerin sıcaklığa ve PANI katkı konsantrasyonuna bağlı olarak dielektrik parametrelerinin GRSA ile tahmini

Year 2020, Volume: 35 Issue: 2, 1077 - 1088, 25.12.2019

Mehmet Kılıç Önder Eyecioğlu Zeynep Güven Özdemir Ümit Alkan

https://doi.org/10.17341/gazimmfd.422448

Cited By: 4

Abstract

Bu çalışmada, düşük
yoğunluklu polietilenin (DYPE) kompleks dielektrik fonksiyonunun gerçek ve sanal
bileşenlerinin (e’ ve e’’
) hem polianilin (PANI) katkısına hem de sıcaklığa bağlı değişimlerinin, genelleştirilmiş
regrasyon sinir ağları (GRSA) metoduyla yüksek doğrulukla tahmin edilebileceği gösterilmiştir.
Bunun için öncelikle, saf DYPE ve kütlece % 0,7, %1 ve %3 PANI katkılandırılmış
DYPE/PANI kompozitler fimler hazırlanmış ve ilgili numunelerin 20 °C, 50 °C ve
80 °C’de e’ ve e’’ bileşenlerinin frekansa
bağlı değişimleri dielektrik spektroskopisi yöntemiyle deneysel olarak
belirlenmiştir. Ardından, dielektrik parametrelerin tahmin değerlerine karşılık
gerçek değerlerine göre çizilen grafikler yardımıyla, GRSA modelinin ilgili
parametrelerin tayinindeki başarı performansı R_e’ =0,9998 ve R_e’’ =0,9365
olarak tespit edilmiştir. Bu noktadan hareketle, GRSA modeli önce mevcut
numunelerin 35 °C, 65 °C ve 95 °C sıcaklıklarda frekansa bağlı olarak e’
ve e’’  bileşenlerinin
değişimini tahmin etmekte kullanılmıştır. Ardından, deneysel olarak hiç
üretilmemiş iki faklı kompozit için (%1,5 ve %6 PANI katkılı DYPE) 20 °C, 35 °C,
50 °C, 65 °C, 80 °C ve 95 °C’de e’ ve e’’
  bileşenlerinin
frekansa bağlı değişimleri GRSA metodu ile önerilmiştir. Böylelikle, hiç
deneysel olarak üretilmemiş bu numunelerin dielektrik parametreleri sıcaklığa
ve frekansa bağlı olarak belirlenebilmiştir. 

Keywords

Genelleştirilmiş regrasyon sinir ağı, dielektrik spektroskopisi, polietilen, polianilin

References

• Moez A. A., Aly S.S., Elshaer Y.H., Effect of gamma radiation on low density polyethylene (LDPE) films: Optical, dielectric and FTIR studies, Spectrochimica Acta Part A, 93, 203–207, 2012.
• Nowak B., Pajak J., Drozd-Brakowicz M., Rymarz G., Microorganisms participating in the biodegradation of modified polyethylene films in different soils under laboratory conditions, Int. Biodeterior. Biodegrad., 65 (6), 757–767, 2011.
• Nand A. V., Ray S., Travas-Sejdic J., Kilmartin P. A., Characterization of antioxidant low density polyethylene/polyaniline blends prepared via extrusion, Materials Chemistry and Physics, 135 (2–3), 903–911, 2012.
• Devilliers C., Fayolle B., Laiarinandrasana L., Oberti S., Gaudichet-Maurin E., Kinetics of chlorine-induced polyethylene degradation in water pipes, Polym. Degrad. Stab., 96 (7), 1361–1368, 2011.
• Causin V., Marega C., Carresi P., Schiavone S., Marigo A., A quantitative differentiation method for plastic bags by infrared spectroscopy, thickness measurement and differential scanning calorimetry for tracing the source of illegal drugs, Forensic. Sci. Int. 164 (2-3), 148–154, 2006.
• Shaber E. R., Vertical interpositional augmentation genioplasty with porous polyethylene, Int. J. Oral Maxillofac. Surg. 16 (6), 678–681, 1987.
• Lingaraj K., Morris H., Barlett J., Polyethylene thickness in unicompartmental knee arthroplasty, 18 (3), 165–167, 2011.
• Schwope A. D., Till D. E., Ehntholt D.J., Sidman K.R., Whelan R.H., Schwartz P.S., Reid R.C., Migration of BHT and Irganox 1010 from low-density polyethylene (LDPE) to foods and food-simulating liquids, Food Chem. Toxical. 25 (4), 317–326, 1987.
• Murakami Y., Nemoto M., Okuzumi S., Masuda S., Nagao M., DC conduction and electrical breakdown of MgO/LDPE nanocomposite, IEEE Trans. Dielectr. Electr. Insul., 15, 33–39, 2008.
• Huang R., Xu X., Lee S., Zhang Y., Kim B.-J., Wu Q., High Density Polyethylene Composites Reinforced with Hybrid Inorganic Fillers: Morphology, Mechanical and Thermal Expansion Performance, Materials, 6, 4122–4138, 2013.
• Hao W., Shuang J. C., and Jun Z., Surface treatment of LLDPE and LDPE blends by nitric acid, sulfuric acid & chromic acid etching, Colloid Polymer Science, 287, 541–548, 2009.
• Gaska K., Xu X., Gubanski S., Kádár R., Electrical, mechanical, and thermal properties of LDPE graphene nanoplatelets composites produced by means of melt extrusion process, Polymers, 9 (1), 11 (1–12), 2017.
• Sharma J., Chand N., Bapat M. N., Effect of cenosphere on dielectric properties of low density polyethylene, Results Phys., 2, 26–33, 2012.
• Bhadra, S., Khastgir, D., Singha, N. K., Lee, J. H., Progress in preparation, processing and applications of polyaniline. Progress in Polymer Science, 34, 783–810, 2009.
• Boeva Zh. A., Sergeyev V. G., Polyaniline: Synthesis, Properties, and Application, Polymer Science Series C, 56 (1), 144–153, 2014.
• Taipalus R., Harmia T., Friedrich K., Short Fibre Reinforced PP/PANI-Complex Blends and their Mechanical and Electrical Properties, Applied Composite Materials, 6, 167–175, 1999.
• Taipalus R., Harmia T., Friedrich K., Influence of PANI-Complex on the Mechanical and Electrical Properties of Carbon Fiber Reinforced PolypropyIene Composites. Polymer Composites, 21, 396–416, 2000.
• Schackletta, L.W., Han, C. C., Luly, M. H., 1993. Polyaniline Blends in Thermoplastics. Synthetic Metals, 55–57, 3532–3537.
• Abd Razak S. I., Abdul Rahman W. A. W., Hashim S., Yahya M.Y., 2013. Polyaniline and their Conductive Polymer Blends: A Short Review. Malaysian Journal of Fundamental and Applied Sciences, 9, 74–80.
• Eyecioglu O., Kilic M., Karabul Y., Alkan U., Icelli O., Artificial Neural Networks Study on Prediction of Dielectric Permittivity of Basalt/PANI Composites, International Journal of Engineering Technologies, 2 (2), 42–48, 2016.
• Habibi-Yangjeh A., Prediction dielectric constant of different ternary liquid mixtures at various temperatures and compositions using artificial neural networks, Physics and Chemistry of Liquids, 45 (4), 471–478, 2007.
• Selva P., Cherrier O., Budinger V., Lachaud F., Morlier J., Smart monitoring of aeronautical composites plates based on electromechanical impedance measurements and artificial neural networks, Engineering Structures, 56, 794–804, 2013.
• Schweitzer R. C., Morris J. B., The development of a quantitative structure property relationship (QSPR) for the prediction of dielectric constants using neural networks. Analytica Chimica Acta, 384, 285–303, 1999.
• Habibi-Yangjeh A., Prediction dielectric constant of different ternary liquid mixtures at various temperatures and compositions using artificial neural networks, Physics and Chemistry of Liquids, 45 (4), 471-478, 2007.
• Guo D., Wang Y, Nan C, Li L., Xia J., Application of artificial neural network technique to the formulation design of dielectric ceramics, Sensors and Actuators A, 102, 93–98, 2002.
• Yu X., Yi B., Liu F., Wang X., Prediction of the dielectric dissipation factor tand of polymers with an ANN model based on the DFT calculation, Reactive & Functional Polymers, 68, 1557–1562, 2008.
• İnal M., Aras F., Yalıtkan Malzemelerin Dielektrik Özelliklerinin Yapay Sinir Ağlarıyla Belirlenmesi. Journal of the Faculty of Engineering and Architecture of Gazi University, 20 (4), 455–462, 2005.
• D. F. Specht, “A general regression neural network,” IEEE Trans. Neu. Net., vol. 2, pp. 568–576, 1991.
• Nagasawa S., Fujimori A., Masuko T., Iguchi M., Crystallization of polypropylene containing nucleators. Polymer, 46 (14), 5241–5250, 2005.
• Ju S., Chen M., Zhang H., Zhang Z., Dielectric properties of nanosilica/low-density polyethylene composites: The surface chemistry of nanoparticles and deep traps induced by nanoparticles, eXPRESS Polymer Letters, 8 (9), 682–691, 2014.