Flexible supercapacitors have gained significant attention in recent times due to their many advantages such as high specific capacitance, lightweight, long lifespan, high energy density, high flexibility, and high-power density. These benefits make them ideal for various high-power applications in various industries. Copper oxide is particularly attractive as an electrode material because of its high theoretical specific capacitance, low cost, and eco-friendliness. Copper oxide is the most promising electrode material in energy storage systems among metal oxides due to its higher theoretical value of specific capacitance (1800 F/g). In the present study, the synthesis of a thin film of copper oxide on a flexible copper substrate through electrodeposition was carried out to produce a flexible and lightweight supercapacitor. The supercapacitor's performance was evaluated using cyclic voltammetry (CV) and galvanostatic charge–discharge analysis in a 1 M KOH electrolyte. The results showed that the copper oxide/copper-based supercapacitor had a large specific capacitance of 983.3 F/g and good performance even after 2200 cycles, with a capacity retention of 89.70%. The flexibility of the electrode was measured at various bending angles. The electrode showed a capacity retention of 87.5% after a 180° bending angle with a good coulombic efficiency of 79.15%. Hence, it could be a promising material for flexible supercapacitor electrodes. This demonstrates that copper oxide has great potential as a material for flexible supercapacitor electrodes. The newer applications for supercapacitors in industries such as wearable electronics, flexible displays, and energy harvesting systems can be explored.

This study presents a robust machine learning framework based on Gaussian process regression (GPR) to predict the tensile strength of polymer nanocomposites reinforced with various nanofillers and processed under diverse techniques. A comprehensive dataset comprising 25 polymer matrices, 22 surface functionalization methods, and 24 processing routes was constructed from the literature. GPR, coupled with Monte Carlo sampling across 2000 randomized iterations, was employed to capture nonlinear dependencies and uncertainty propagation within the dataset. The model achieved a mean coefficient of determination (R²) of 0.96, RMSE of 12.14 MPa, MAE of 7.56 MPa, and MAPE of 31.73% over 2000 Monte Carlo iterations, outperforming conventional models such as support vector machine (SVM), regression tree (RT), and artificial neural network (ANN). Sensitivity analysis revealed the dominant influence of Carbon Nanotubes (CNT) weight fraction, matrix tensile strength, and surface modification methods on predictive accuracy. The findings demonstrate the efficacy of the proposed GPR framework for accurate, reliable prediction of composite mechanical properties under data-scarce conditions, supporting informed material design and optimization.

This investigation focuses on the wear resistance and surface morphology of multi-walled carbon nanotube (MWCNT)-filled bio-based epoxy composites. This study examines the impact of different MWCNT concentrations (0 Wt.%, 0.25 Wt.%, 0.50 Wt.%, and 0.75 Wt.%) on the wear properties of these composites. Techniques such as scanning electron microscopy (SEM) and atomic force microscopy (AFM) were utilized for comprehensive surface characterization. The results demonstrated a direct correlation between the MWCNT content and the wear resistance of the composites, which were corroborated by robust statistical analysis. Furthermore, SEM and AFM observations disclosed incremental enhancements in both wear resistance and surface quality as the MWCNT concentration increased. This research not only augments the understanding of wear mechanisms in bio-based epoxy composites but also aligns with the burgeoning focus on sustainable materials.

The global push for sustainable materials has intensified the research on natural fiber-reinforced composites. This study investigates the potential of sugarcane bagasse fibers, combined with a bio-based epoxy matrix, as a sustainable alternative for high-performance composites. A comprehensive approach was adopted, including wear testing, thermal and structural characterization, and machine learning predictive modeling. Ethylene dichloride-treated fibers exhibited the lowest wear rate (0.245 mg/m) and the highest thermal stability (T20% = 260 °C, char yield = 1.3 mg), highlighting the role of optimized surface modifications. XRD (X-ray diffraction) analysis revealed that pre-treated fibers achieved the highest crystallinity index of 62%, underscoring the importance of structural alignment in fiber-matrix bonding. Machine learning insights using a Random Forest model identified fiber treatment as the most significant parameter influencing wear performance, with accurate predictions validated through experimental results. This work demonstrates the transformative potential of sugarcane bagasse fibers in sustainable polymer composites, offering a pathway for environmentally friendly, lightweight, and durable material solutions. These findings integrate experimental rigor with computational insights, paving the way for advancements in natural fiber-based composite technologies.

Eggshells are a potentially polluting industrial waste that are disposed of as landfill which has proven to be hazardous to the environment. The usage of chicken eggshells as a biofiller for polymer matrix composites instead of its disposal as landfill has proven advantageous in various studies. On the other hand, using eggshells as a filler material to replace inorganic calcium carbonate usage would be another environment friendly act. The present study is focused on studying the effects of eggshell filler addition and post-curing on polymer composites which could be utilised for domestic applications. Herein, uncarbonised and carbonised eggshell filler material were processed from waste eggshells. Hybridisation of the carbonised and uncarbonised eggshell filler was carried out. All three variants of eggshell fillers (10 wt.%) were used in the fabrication of composites. A hand lay-up technique was employed in the fabrication of unfilled composites along with three variants of filled composites, namely, uncarbonised, carbonised, and hybrid eggshell filled composites. The fabricated and cured composites were further subjected to post-curing at a temperature of 60 °C for a period of 2 h. All four variants of post-cured composites were then subjected to mechanical testing according to American Society for Testing and Materials (ASTM) standards. The tests revealed that all three variants of filled composites possess better mechanical properties in comparison with unfilled composites. Further, in comparison with unfilled composites, the carbonised eggshell filled composites showcased 42% and 49% improvement in flexural and tensile properties, respectively. The modes of failure of the specimens were observed and tabulated. SEM imaging revealed that the eggshell filler contributed to the strengths of the composites by means of arresting and deviating cracks. It was also observed that the post-cured specimens displayed improved properties when compared with our previous studies on non-post-cured specimens. In summary, the study showcased the benefits of eggshell filler addition and the post-curing of polymer composites.

Surface modified montmorillonite nanoclay created tortuous path to flow of moisture and enhanced bonding of PALF with epoxy, which resulted in lower diffusion coefficient and reduced impact of moisture diffusion in epoxy PALF nanoclay composites.