Carbon nanotubes (CNTs) have attracted growing interest as a filler in rubber nanocomposites due to their mechanical and electrical properties. In this study, the mechanical properties of a NR/BR/IR/SBR compound reinforced with single-wall carbon nanotubes (SWCNTs) were investigated using atomic force microscopy (AFM), tensile tests, hardness tests, and a dynamical mechanical analysis (DMA). The tested materials differed in SWCNT content (1.00–2.00 phr) and were compared with a reference compound without the nanofiller. AFM was used to obtain the topography and spectroscopic curves based on which local elasticity was characterized. The results of the tensile and hardness tests showed a reinforcing effect of the SWCNTs. It was observed that an addition of 2.00 phr of the SWCNTs resulted in increases in tensile strength by 9.5%, Young’s modulus by 15.44%, and hardness by 11.18%, while the elongation at break decreased by 8.39% compared with the reference compound. The results of the temperature and frequency sweep DMA showed higher values of storage and loss moduli, as well as lower values of tangent of phase angle, with increasing SWCNT content.

The incorporation of carbon nanotubes (CNTs) enhances the mechanical performance of rubber composites. This study examines the effect of single-walled CNTs (1–4 phr) on the properties of a natural rubber compound for sealing applications, both before and after thermal aging, as prolonged exposure to elevated temperatures can alter material properties, affecting durability and functionality. The researched nanocomposites were subjected to a series of mechanical tests, dynamic mechanical analysis, and surface investigation using atomic force microscopy (AFM). After adding 4 phr of CNTs, tensile and tear strength increased by 11.73% and 14.64%, respectively, while aging-related strength degradation was reduced by 5%. CNTs also increased complex modulus and hardness but reduced elongation at break and rebound resilience. Residual deformation in tensile and compressive set tests decreased. AFM analysis confirmed improved surface stability after thermal aging compared to the CNT-free compound.

This paper presents an intelligent predictive system designed to support real-time decision making in the control of rubber blend mixing processes. The core of the system is a General Regression Neural Network (GRNN), which accurately predicts key process parameters, such as viscosity (expressed as torque), temperature, and energy consumption across varying masses of the processed material. The model can evaluate the mixing progress based on the initial 10% of input data, allowing early intervention and process optimisation. Experimental validation was conducted using a Brabender Plastograph EC Plus with a natural rubber-based blend in the mass range of 60–75 g. The GRNN kernel width parameter (σ) was optimised through a 10-fold cross-validation. High predictive accuracy was confirmed by values of the coefficient of determination (R²) approaching 1, and consistently low values of the root mean square error (RMSE). This system offers a robust and scalable solution for intelligent process control, productivity enhancement, and quality assurance across diverse industrial applications, beyond rubber blending.

This study aimed to investigate the influence of single-wall carbon nanotube (SWCNT) content on the mechanical properties of polyurethane (PU) nanocomposites. The SWCNT content varied from 0 wt% (reference sample) to 2 wt%. Tensile, hardness and Charpy impact tests as well as dynamic mechanical analysis (DMA) were performed. Based on the test results it was observed that an increase in the content of single-wall carbon nanotubes resulted in significant improvements in material strength and stiffness. Furthermore, atomic force microscopy (AFM) was used to examine microsurface topography of the samples and to obtain spectroscopic curves, based on which local elasticity was evaluated. Overall, performed measurements indicate that the incorporation of SWCNTs into PU matrix makes resultant nanocomposite stiffer and more resistant to deformation. The results highlight the potential of SWCNTs as effective reinforcement of polyurethane-based nanocomposites.

The polyamide PA6 composites reinforced with carbon fibres (CF) are widely studied due to their properties and their high strength to weight ratio. Good adhesion between a filler and a matrix is essential for enhancing properties of a resulting composite. This study investigates the effect of the short CF content and the used CF treatment on mechanical properties of the PA6-CF composites. The composites were subjected to tensile, flexural, compression, hardness and Charpy tests as well as dynamical mechanical analysis. An atomic force microscopy was employed to investigate topography of the CF and the composites. Initially, the properties of the composites were improved through the oxidation of the CF in HNO₃. Subsequently, to further enhance these properties, the oxidized CF were grafted with nano-SiO₂. The CF content in the tested composites varied from 10 wt% to 60 wt%. The most significant improvement of the tested properties was observed at the CF content of 40 wt%.

Modelling the influence of high-energy ionising radiation on the properties of materials with polymeric matrix using advanced artificial intelligence tools plays an important role in the research and development of new materials for various industrial applications. It also applies to effective modification of existing materials based on polymer matrices to achieve the desired properties. In the presented work, the effects of high-energy electron beam radiation with various doses on the dynamic mechanical properties of melamine resin, phenol-formaldehyde resin, and nitrile rubber blend have been studied over a wide temperature range. A new stiffness-temperature model based on Weibull statistics of the secondary bonds breaking during the relaxation transitions has been developed to quantitatively describe changes in the storage modulus with temperature and applied radiation dose until the onset of the temperature of the additional, thermally-induced polymerisation reactions. A global search real-coded genetic algorithm has been successfully applied to optimise the parameters of the developed model by minimising the sum-squared error. An excellent agreement between the modelled and experimental data has been found.