Effect of cenosphere on dielectric properties of low density polyethylene

Sharma, Janu; Chand, Navin; Bapat, Meera

doi:10.1016/j.rinp.2012.03.003

Cited by 20 publications

(10 citation statements)

References 19 publications

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“…Bruggeman model provides an extension of Maxwell model, where the interactions among the randomly distributed fillers are considered. 23 However, in view of Figure 3, none of the models fit perfectly the TC behavior. This may be because none of these micromechanical models takes into account the percolation phenomenon and the subsequent formation of a conductive network in the system from a critical amount of CNT (threshold).…”

Section: Resultsmentioning

confidence: 92%

Role of rheology in tunning thermal conductivity of polyamide 12/polyamide 6 composites with a segregated multiwalled carbon nanotube network

Arboleda-Clemente

García-Fonte

Abad

et al. 2017

Journal of Composite Materials

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Effect of multiwalled carbon nanotubes in thermal conductivity of an immiscible blend of polyamides, 50/50 (wt%/wt%) polyamide 12/polyamide 6, was analyzed as function of nanofiller amount and temperature. Effect of the molding temperature in the structure of conductive network was investigated by rheology. Data show that 5 vol% multiwalled carbon nanotubes caused an increase of 41% in thermal diffusivity and 78% in thermal conductivity respect to polyamide blend values. Thermal conductivity improvement could be described by percolation theory, with a low threshold composition (φc = 0.09 vol% carbon nanotube). Fitting parameters obtained from Agari’s adjustment model show that polyamides structure is not affected by carbon nanotubes and the nanofillers can easily form conductive paths in the polyamide 12/polyamide 6 matrix. The temperature increase facilitates nanofiller dispersion causing the formation of a denser carbon nanotube network and rising the thermal diffusivity of carbon nanotube composites with low percolation level, as was proved on annealed samples at 255℃.

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Section: Resultsmentioning

confidence: 92%

Role of rheology in tunning thermal conductivity of polyamide 12/polyamide 6 composites with a segregated multiwalled carbon nanotube network

Arboleda-Clemente

García-Fonte

Abad

et al. 2017

Journal of Composite Materials

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“…The need for composite materials with advanced properties and reduced weight has been widely exploited with cenosphere filler particles. [24,25] The lightweight properties of these polymers make them suitable for weightsensitive structural applications. The high cost of the polymer is sometimes a limiting factor, and hence, lowcost fillers are preferred for reducing the overall cost of the components for various applications.…”

Section: Introductionmentioning

confidence: 99%

Effect of micro‐ and nanofiller hybrids on the dynamic mechanical properties of glass reinforced epoxy composites

Bommegowda

Renukappa

Rajan³

2021

Polymer Composites

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Reinforcement of epoxy glass fabric composites with nano-and micro-fillers has resulted in the development of polymer composites with good electrical, thermal, and mechanical properties. The present work attempts to estimate the impact of hybrid fillers on dynamic-mechanical properties of the epoxy composites with a different filler. The dynamic mechanical parameters such as storage modulus, loss modulus, and damping factor over a temperature of 25 C-250 C have been investigated. The viscoelastic properties of composites are also confirmed with a Cole-Cole plot. From the results, the storage modulus of the composites is observed to lie in the range of 8000 to 12,500 MPa, and the epoxy composite with silicon carbide filler shows the highest storage modulus. Composite with 5 wt% of alumina shows the maximum loss modulus of 2100 MPa. The glass transition temperature of the base epoxy composite is 135 C and it increases to 137 C with the incorporation of hybrid cenosphere and molybdenum sulfide fillers. The storage modulus shows only marginal differences as compared to their counterparts with nanofillers but is higher by about 10-15% in comparison to the micron filler-based composites. However, the differences in the loss modulus of the nanocomposites, hybrid composites, and the composites with micron-sized filler are not significant.

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“…polimerlerin elektriksel özelliklerini incelemeye ve kullanım alanına uygun şekilde onların elektriksel yalıtım özelliklerini geliştirmeye yönelik teorik ve deneysel çalışmalar büyük bir ivme kazanmıştır. Özellikle son otuz yılda akademik ve ARGE çalışmaları, saf polimerlere farklı dolgu malzemesi katkısı yapılarak elektrik, mekanik ve ısısal performanslarının iyileştirilmesi ve kullanım amacına uygun modifiye kompozitler üretimine yönelmiştir [9][10][11][12][13]. Bu bakımdan iletken bir polimer olan PANI, polistiren (PS), polietilen (PE), polipropilen (PP) gibi termoplastik polimerler için dikkat çekici bir dolgu malzemesidir.…”

Section: Gi̇ri̇ş (Introduction)unclassified

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

Kılıç

Eyecioğlu

Özdemir

et al. 2019

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

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 GRNN model for the estimation of dielectric parameters of LDPE/PANI composites was investigated.  Dielectric parameters depending on temperature and PANI additive were estimated with the accuracy varying between 93% and 99%.  GRNN model can suggest optimum PANI additive level and working temperature for LDPE/PANI composites. Since estimate made with generalized regression neural networks (GRNN) provide both time and cost savings, application of GRNN to many disciplines including geotechnical engineering, stock market forecasts, weather predictions, etc. become very important in recent years. Particularly, the widespread use of GRNN in the field of physics and materials science will provide a significant advantage in time-consuming and costly processes. Figure A. The application of GRNN method in prediction of static dielectric constant. Purpose: This work is devoted to investigate the accuracy of estimation of dielectric parameters of polymer/polymer composite dielectric materials by GRNN. Theory and Methods: Low Density Polyethylene/Polyaniline (LDPE/PANI) films with the PANI contribution of 0.7 wt.%, 1 wt.% and 3 wt.% were produced by using hot press molding technique. The dielectric parameters (ε' and ε'') of the LDPE/PANI films were determined for 20 °C, 50 °C and, 80 °C by dielectric spectroscopy measurements realized by an impedance analyzer between 100 Hz-6.75 MHz frequency interval. The generalized regression neural networks (GRNN) method was applied to estimate the dielectric parameters of both experimentally prepared LDPE/PANI films for different temperatures (35 °C, 65 °C and 95 °C) and experimentally unproduced LDPE/PANI films consisting of PANI in the wt.% of 1.5 and 6 for the temperature interval of 20-95 °C. Results: As a result of comparison of experimental data and GRNN results, it was determined that GRNN is a reliable method for estimating dielectric parameters. Conclusion: It was concluded that the optimum PANI additive level for and the optimum working temperature for LDPE films can be determined for various technological applications without any experimental production by using GRNN model.

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Effect of cenosphere on dielectric properties of low density polyethylene

Cited by 20 publications

References 19 publications

Role of rheology in tunning thermal conductivity of polyamide 12/polyamide 6 composites with a segregated multiwalled carbon nanotube network

Role of rheology in tunning thermal conductivity of polyamide 12/polyamide 6 composites with a segregated multiwalled carbon nanotube network

Effect of micro‐ and nanofiller hybrids on the dynamic mechanical properties of glass reinforced epoxy composites

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

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