Optimised quantity of nano‐aluminium (Al) filler mixed with epoxy resin using percolation threshold criteria was adapted to fabricate nanocomposite with desired space‐charge and charge‐trap properties. Surface potential variation due to charge deposition on to the insulating material under transient voltages was investigated, and it showed the improved decay rate with reduced trap depth for the nanocomposites with nanofillers addition in the range between 0.5 and 5 wt%. Threshold electric field calculated based on space‐charge density variation by pulsed electroacoustic measurement was found to be between 7–9 kV/mm and maximum electric field calculated through polarity reversal test is about 9 kV/mm, for 5 wt% (for the optimised value of nanofiller) epoxy nano‐Al composites.

Hydrothermal and chemical aging tests on a 230 kV cross-linked polyethylene (XLPE) insulation cable were carried out in the present study to evaluate the degradation and aging levels qualitatively. The samples were subjected to water aging at a temperature of 80 °C, and in an aqueous ionic solution of CuSO₄ at room temperature. The diffusion coefficient results indicated that the ion migration was not at the same rate in the aging conditions. The diffusion coefficient–D–of the sample immersed in an aqueous CuSO₄ solution was lower than the hydrothermally aged specimens. The hydrophobicity of aged specimens decreased considerably compared to unaged samples. The distribution of trapped charges was quantitatively characterized. The presence of shallow trap energy states were observed in unaged XLPE, whereas the deep trap sites were noticed in aged specimens. In addition, the charge trap characteristics were different for positive and negative charge deposition. Various material characterization techniques, viz. dynamic mechanical analysis (DMA), tensile, contact angle, and LIBS, were further employed on the aged and virgin specimens. The tensile behavior of the hydrothermally aged specimen was degraded due to the oxidised regions, which had formed a weak spot against the mechanical stress. Reduced glass transition temperature and increased loss tangent measurements were noticed for aged specimens over their unaged counterparts. Machine learning techniques, such as the principal component analysis (PCA) and the artificial neural network (ANN) analysis, were performed on LIBS spectral data of the samples to classify the aging mechanisms qualitatively.

In the present study, hybrid composites are prepared by reinforcing various concentrations of high permittivity zirconia nanofiller into epoxy/CNT compositions to test their usability in EMI shielding applications in the X and Ku bands. ZrO ₂ nanofiller is added in different proportions to improve absorbance shielding while maintaining the composite conductivity uniform by adding constant CNT concentration to restrict the reflectance shielding. The microscopic studies have revealed an efficient dispersion of ZrO ₂ nanoparticles in the CNT networks and provided a smoother surface. The presence of zirconia nanofillers increased the dielectric properties, viz. the dielectric constant (194 at 0.1 Hz) and loss tangent (1.57 at 0.1 Hz), respectively, whereas the conductivity was found to be invariantly constant. The increased permittivity of composites enhanced the shielding by absorption, while the shielding by reflection is least influenced by the addition of zirconia nanofiller. The addition of zirconia nanofillers increased the permittivity and tan delta, allowing charges to accumulate at the interfacial areas for incoming EM radiations, resulting in increased absorbance shielding. Limiting the CNT concentration in all composites to the same level resulted in the formation of conductive networks, thus resulting in uniform reflectance shielding for all the hybrid composites in the present study. The dynamic mechanical analysis showed the improvement in the storage modulus and activation energy due to the enhanced interfacial adhesion and cross-linked polymer density.

Silicone rubber (SR) micro nanocomposites formed with micro-ATH and nano-alumina particles have been subjected to different levels of gamma-ray irradiation to understand the characteristic variation in the surface condition of the material through contact angle measurement, Atomic Force Microscopy (AFM) studies, water droplet-initiated corona inception voltage (CIV) measurement and by laser-induced breakdown spectroscopy (LIBS) analysis. It is realized that the recovery rate of silicone rubber micro nanocomposites is less compared with the base SR material. It is observed that, irrespective of the level of irradiation, the contact angle and water droplet initiated CIV of the specimen have shown direct correlation. FTIR analysis clearly indicates variation in methyl group formation on irradiation is less with S2 specimen. It is also observed that fractal dimension calculated by three different techniques for the surface profile data obtained using AFM is in directly in proportion with average surface roughness. In addition, the lacunarity values of the irradiated samples followed the same pattern as that of the average surface roughness. The variation in thermal degradation temperature has been analyzed by adopting thermogravimetric analysis (TGA). The calculated plasma temperature values from laser-induced breakdown spectroscopy and the crater depth formed due to laser ablation have shown inverse relationship. Artificial neural network (ANN) has been employed successfully using LIBS data, to understand the level of irradiation of the SR samples. Overall, the silicone rubber micro-nanocomposite sample S2 filled with micro-ATH and nano Alumina has reflected relatively lesser degradation after being exposed to gamma irradiation, over other test specimens.

Silicone rubber (SR) samples are coated with various types of artificially prepared pollutants, in order to identify and distinguish them by employing laser induced breakdown spectroscopy (LIBS). LIBS analysis is successful in identifying the elemental composition of the various types of pollutants. The presence of copper sulphate as well as carbon-based compounds such as fly ash, coal and calcium-based compounds such as cement and calcium phosphate (fertilizer) have been identified by the increment in the normalized intensity ratio of the copper, carbon and calcium peaks respectively. LIBS spectral data has been used in conjunction with several machine learning (ML) algorithms such as linear discriminant analysis, decision tree, K-nearest neighbors and various gradient boosting techniques to classify seven different types of contaminated SR samples. When compared to the other ML approaches utilized in the present study, classification using the Light gradient boosting technique has reflected better classification accuracy of 97.43% with a reasonable computation time of 5.1 s.