The aim of this study was to develop a polyethylene/polyamide (R-PE/R-PA) regranulated product made from post-consumer wastes grafted with polyethylene-graft-maleic anhydride (PE-g-MAH) by reactive extrusion in a twin-screw extruder equipped with an external mixing zone. The compatibility effect of PE-g-MAH used as a modifier in R-PE/R-PA blends was evaluated by means of differential scanning calorimetry (DSC) and dynamic mechanical thermal analysis (DMTA), while the analysis of the chemical structure of this blend was carried out by Fourier transform infrared spectroscopy (FT-IR). The thermal properties, complex viscosity, and selected usage properties of R-PE/R-PA blends compatibilized with PE-g-MAH, i.e., density and water absorption, were evaluated. The morphology of the blends with and without the compatibilizer was observed by scanning electron microscopy. The R-PE/R-PA/MAH blend shows heterogenic structure, which is a result of the chemical reaction in reactive extrusion between functional groups of PE-g-MAH used as modifier and the end groups of R-PA6. The results show that the R-PE/R-PA blend with increased PE-g-MAH content showed increased hardness, stiffness, and ultimate tensile strength due to the increased degree of crystallinity. The increase in crystallinity is proportional to the improvement of the mechanical properties. Moreover, it is shown that 1 wt.% PE-g-MAH added to the R-PE/R-PA waste blend increases the interfacial interactions and compatibility between R-PE and R-PA, resulting in decreased polyamide particle size. Finally, the results show that it is possible to produce good quality regranulated products with advantageous properties and structure from immiscible polymer waste for industrial applications.

The aim of this work was to assess the numerous approaches to structural and material modeling of brain tissue under dynamic loading conditions. The current technological improvements in material modeling have led to various approaches described in the literature. However, the methods used for the determination of the brain’s characteristics have not always been stated or clearly defined and material data are even more scattered. Thus, the research described in this paper explicitly underlines directions for the development of numerical brain models. An important element of this research is the development of a numerical model of the brain based on medical imaging methods. This approach allowed the authors to assess the changes in the mechanical and geometrical parameters of brain tissue caused by the impact of mechanical loads. The developed model was verified through comparison with experimental studies on post-mortem human subjects described in the literature, as well as through numerical tests. Based on the current research, the authors identified important aspects of the modeling of brain tissue that influence the assessment of the actual biomechanical response of the brain for dynamic analyses.

The demand for lightweight, strong structural profiles is currently high in the transport industry, mechanical engineering, and construction. Therefore, it is important to evaluate their properties, especially mechanical properties. The main objective of this paper is to determine energy absorption coefficients and evaluate the crush resistance of thin-walled aluminum profiles using numerical simulation and empirical verification. This paper presents the compression results of testing of thin-walled aluminum profiles filled with a porous material (cast aluminum foam). The numerical analysis was conducted using the software Abaqus/CAE. Aluminum material data were obtained from a static tensile test performed on a Shimadzu machine. The experiment was performed on an Instron CEAST 9450HES dynamic hammer. Profiles with three shapes of crush initiators filled with aluminum foam measuring 40 mm–200 mm in 20 mm increments were numerically tested. A sample with a concave initiator filled with foams of 40 mm, 60 mm, 80 mm, and 120 mm in length was used to verify the numerical analyses. Energy absorption coefficients were determined from the analyses. The results of both analyses were tabulated to show the percentage differences. The study showed an increase in the Crush Load Efficiency (CLE) index by up to 33% for samples with the same crush initiator. In addition, it was noted that the use of porous fill does not increase the value of initiating Peak Crushing Force (PCF), which indicates the generation of much smaller overloads dangerous for vehicle passengers.