Epoxy nanocomposites with different wt% of alumina was prepared by use of high-speed shear mixer and high-frequency sonicator process, and the electrical and mechanical properties of the materials were investigated. Surface potential variation and trap distribution analysis were carried out with epoxy nanocomposites. Space charge analysis was carried out through Pulsed Electro Acoustic (PEA) technique. The dielectric constant and tan (δ) values of epoxy alumina nanocomposites were measured at different frequencies and temperatures. Contact angle and corona inception voltage caused by a water droplet showed a direct correlation. The viscoelastic properties of the epoxy nanocomposite material were examined through DMA studies. It was concluded that an increase in the supply frequency increases the storage modulus and loss modulus of the epoxy alumina nanocomposites. Glass transition temperature and activation energy increase with an increase in wt% of alumina content in epoxy resin. LIBS analysis indicates that the plasma temperature calculated increases with an increase in the wt% of alumina content in epoxy resin. Also, the measured plasma temperature and hardness of the material showed a direct correlation.
Epoxy-alumina nanocomposites have been coated with different types of pollutants at various concentrations in order to analyze their pollution performance using laser induced breakdown spectroscopy (LIBS) study. Elemental analysis of spectral peaks has been successful in determining the elemental composition of the pollutant present on the surface of the specimens. The conductivity and equivalent salt deposition density (ESDD) values have been determined for all the pollutants at different concentrations. A direct correlation between ESDD and the normalized intensity ratio of LIBS spectral data is noticed. Regression coefficient (R 2 ) has been used as a performance parameter in correlating the ESDD and normalized intensity ratio. Artificial neural network (ANN) technique has been adopted to LIBS spectral data for the classification of the pure epoxy and 3 wt% epoxy-alumina nanocomposite specimen based on conductivity and type of pollutant. ANN developed with conjugate gradient backpropagation with Polak-Ribiere updates training algorithm has reflected higher classification accuracy and required lesser epochs to converge, compared to other training algorithms in classifying the contaminated specimens.
Epoxy alumina nanocomposites were fabricated through shear mixing of alumina nanoparticles in to epoxy resin under optimum process conditions for its potential application as insulator in transformer. The fundamental insight in to the influence of ageing of nanocomposite insulator due to its continuous exposure to UV irradiation and water in terms of their charge trap characteristics was explored. The surface potential decay and strain variation of nanocomposite samples were measured by using Electrostatic voltmeter and Digital Image Correlation technique (DIC), respectively. Diffusion of water into epoxy alumina nanocomposite is less compared with pure epoxy resin. It has been observed that surface potential decay rate and trap depth have shown increasing and decreasing trend, respectively, with the addition of alumina nanoparticles in to epoxy resin. The UV and water aged nanocomposites exhibited a decreasing trend of surface potential decay rate and the governing reasons could be due to surface damage causing increase in deep trap formation. The tensile strength and stiffness of the samples have increased with the addition of alumina nanoparticles in to the epoxy resin. It could be due to the better bonding of alumina particles with epoxy resin. The surface strain induced in to the sample during tensile loading and the surface potential decay rate of nanocomposite has shown a direct correlation, as observed in the present work.
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