The accurate prediction of the mechanical behaviour of silica–epoxy nanocomposites is essential for advancing their application in high-performance industries, including aerospace, automotive, and structural engineering. Conventional experimental characterization methods are often time-consuming and costly, highlighting the need for efficrelianceient computational alternatives. This study proposes a machine learning based on Random Forest Regression to predict the stress–strain behaviour of silica–epoxy nanocomposites with high accuracy. The model employs two independent and physically meaningful input parameters—SiO₂ nanoparticle concentration (wt%) and strain—to predict stress, thereby capturing the true constitutive relationship of the material. The model was trained and validated on an extensive experimental dataset of 7422 observations across five compositions (0–4 wt% SiO₂), obtained from systematic tensile testing following the ASTM D638 standard. Rigorous stratified 10-fold cross-validation confirmed excellent generalization (mean R² = 0.9977 ± 0.0023) with minimal overfitting (training–validation gap < 0.005). The performance of the test set (R² = 0.9948, mean absolute error (MAE) = 0.0404 MPa) surpasses recent literature benchmarks by nearly 5%, establishing state-of-the-art accuracy in nanocomposite property prediction. Error analysis revealed stable prediction accuracy throughout the elastic and plastic regimes (error variance < 0.004 MPa² for strain), with a physically consistent increase in error near failure due to complex damage mechanisms. Feature importance analysis indicated that strain and SiO₂ concentration contributed 78.4% and 21.6%, respectively, to predictive accuracy. This is consistent with constitutive modelling principles, in which deformation state primarily determines stress magnitude, while composition modulates the functional relationship. Mechanical property extraction from experimental curves showed optimal performance at 2–3 wt% SiO₂, yielding balanced enhancements in tensile strength (+1–2%) and failure strain (+36–64%) relative to neat epoxy. The validated framework reduces material development time by 65–80% and cost by 60–75% compared with conventional trial-and-error methods, offering a robust, data-driven tool for the efficient design and optimization of silica–epoxy nanocomposites. A comprehensive discussion of limitations and applicability boundaries ensures the framework’s responsible and reliable deployment in engineering practice.

Epoxy occupies an important place in many engineering applications, so it is necessary to improve its properties such as adhesion strength, wear resistance, and toughness. In this study epoxy was modified by incorporation of disulfide bonds. A coating layer was prepared by adding ethanol disulfide to epoxy resin at various weight percent: 0.5, 1.5, 2.5 and 3.5 wt.%. Fourier-transform infrared spectroscopy was employed to investigate any chemical interaction. Differential scanning colometry (DSC) analysis provides valuable information about thermal behaviour of the prepared epoxy coating. Tensile, hardness, impact, fracture toughness and adhesion properties were tested. Wear behaviour was investigated by pin-on-disc device. Results show that epoxy containing disulfide bonds (EP+SS) offering excellent impact resistance with good fracture toughness compared to neat epoxy. Additionally, contact angle test results show an enhancement of wettability between the modified epoxy coating layer and steel substrate. Adhesion strength of epoxy layer enhanced by incorporating ethanol disulfide into epoxy resin reaching a superior value. Comparison to neat epoxy, the coefficient of friction of (EP+SS) highly decreased reaching about 89% and wear rate decreased by 76% as the percentage of the added ethanol disulphide increases. In conclusion, a tough superior adhesive epoxy coating layer was synthesised by incorporation of the ethanol disulfide compound into epoxy resin.

Refinery waste management is a critical procedure for mitigating the environmental impact of petroleum refining operations and is integral to supporting sustainable industrial practices, necessitating the effective treatment, recycling, and safe disposal of waste streams. This work addresses this challenge through the high-value valorization of a spent fluid catalytic cracking (FCC) catalyst by incorporating it into an epoxy matrix to produce a novel composite material. Composites were fabricated with spent catalyst loadings of 5, 10, 15, and 20 weight percent (wt.%), and their mechanical and tribological properties, specifically toughness, friction, and wear resistance, were systematically investigated. The results demonstrate the successful transformation of spent FCC catalyst, a problematic industrial waste, into a functional, high-performance engineering material, establishing a viable pathway for the circular economy. Specifically, the impact strength (toughness) was dramatically improved, reaching a 120% increase at the optimal filler concentration of 5 wt.%, which identifies this concentration as critical for maximizing the composite’s toughness. Furthermore, the spent Catalyst proved highly effective as a tribological modifier, substantially reducing the wear rate of the epoxy matrix by 31% to 71.6% across the tested range of filler loadings. Although the epoxy hardness was only marginally affected, showing a maximum increase of 4% at 20 wt.%, the overall property enhancement confirms the spent Catalyst’s efficacy as a reinforcing filler, providing a sustainable waste management strategy coupled with the development of advanced materials with superior performance.