Compatible green polymer electrolytes based on methyl cellulose (MC) were prepared for energy storage electrochemical double-layer capacitor (EDLC) application. X-ray diffraction (XRD) was conducted for structural investigation. The reduction in the intensity of crystalline peaks of MC upon the addition of sodium iodide (NaI) salt discloses the growth of the amorphous area in solid polymer electrolytes (SPEs). Impedance plots show that the uppermost conducting electrolyte had a smaller bulk resistance. The highest attained direct current DC conductivity was 3.01 × 10⁻³ S/cm for the sample integrated with 50 wt.% of NaI. The dielectric analysis suggests that samples in this study showed non-Debye behavior. The electron transference number was found to be lower than the ion transference number, thus it can be concluded that ions are the primary charge carriers in the MC–NaI system. The addition of a relatively high concentration of salt into the MC matrix changed the ion transfer number from 0.75 to 0.93. From linear sweep voltammetry (LSV), the green polymer electrolyte in this work was actually stable up to 1.7 V. The consequence of the cyclic voltammetry (CV) plot suggests that the nature of charge storage at the electrode–electrolyte interfaces is a non-Faradaic process and specific capacitance is subjective by scan rates. The relatively high capacitance of 94.7 F/g at a sweep rate of 10 mV/s was achieved for EDLC assembly containing a MC–NaI system.

In the current study, the film fabrication of polystyrene (PS) based polymer nanocomposites (NCs) with tuned refractive index and absorption edge was carried out using the solution cast method. X-ray diffraction (XRD) and ultraviolet-visible (UV-Vis) light characterization techniques were performed. The structural and optical properties of the prepared films were specified. The hump of PS decreased significantly when SnTiO₃ nanoparticles (NPs) were introduced. Sharp and high intense peaks of SnTiO₃ NPs at a high filler ratio were observed. The crystalline size was determined for SnTiO₃ NPs from the sharp crystalline peaks using Debye-Scherrer’s equation and was found to be 25.179 nm, which is close enough to that described by the supplier. Several optical parameters, such as absorption coefficient (α), refractive index (n), and optical dielectric properties, were investigated. The absorption spectra were tuned with increasing SnTiO₃NPs. Upon the addition of the NPs to the PS host polymer, the absorption edge undergoes shifting to lesser photon energy sides. The optical dielectric constant (ε′) was correlated to the refractive index. The study of the optical band gap was conducted in detail using both Tauc’s model and the optical dielectric loss (ε″) parameter. The results showed that the ε″ parameter is noteworthy to be measured in the optical band gap study of materials.

In the realm of industrial production, condition monitoring plays a pivotal role in ensuring the reliability and longevity of rotating machinery. Since most of the production facilities rely heavily on vibration analysis, it has become the cornerstone of condition monitoring practices. However, manual analysis of vibration signals is a time-consuming and expertise-intensive task, often requiring specialized domain knowledge. The current research addresses the aforementioned challenges by proposing a novel semi-automated diagnostics system. The approach leverages historical vibration data in the form of Fast Fourier Transform (FFT) spectrums. The system extracts energy features from the frequency domain by dividing the frequency range into a predefined number of bins and summing the energy values within each bin. Subsequently, each datapoint is labeled based on the corresponding machine condition, enabling the system to learn diagnostic patterns by employing machine learning models. This approach facilitates efficient and accurate diagnostics with minimal manual intervention. The resulting dataset effectively represents and provides an interpretable result. Support Vector Machines (SVM), and ensemble algorithms are utilized to diagnose the faults instantaneously and with minimal error rates. The proposed system is capable of providing early warnings and thus prevents further deterioration and unplanned downtimes. Experimental validation using real-world data demonstrates the system's efficacy, achieving an accuracy of over 90%.

In the present work, epoxy resin‐based nanocomposites (NCPs) were fabricated with improved mechanical properties. The epoxy resin (EPR) was mixed with differing amounts of calcium titanate (CaTiO₃) nanoparticles (NPs) and glass fiber. The results showed that the ternary system contained glass fiber exhibits low mechanical performance compared to binary [EPR:CaTiO₃] system. The effect of fiber glass and NPs on the epoxy resin mechanical behavior was determined by conducting a tensile test for various specimen sets. From the mechanical characterizations, it was seen that there is a monotonic relationship between the NPs concentration and Young’s modulus. Additionally, NCPs samples were brittle in nature and the strain rate significantly decreased upon the addition of CaTiO₃ concentration; while the tensile strength was increased. From the X‐ray diffraction evaluation, it can be concluded that the addition of NPs have a great impact on the epoxy structure. Besides, the morphology appearance was in good agreement with structural and mechanical analysis.