The increasing global accumulation of End-of-Life (EoL) tires and the growing demand for fossil industrial Carbon Black (CB) call for sustainable alternative solutions. In this context, tire pyrolysis and the resulting recycled raw material recovered Carbon Black (rCB), are considered potential alternatives. In the study, various rCBs were incorporated into new elastomer compounds using a laboratory internal mixer and their properties were investigated. The compounds were selected based on examples of applications such as bicycle inner tubes and hydraulic membranes. By comparing the in-rubber properties of rCB-based compounds with CB reference compounds, an initial assessment of the potential use of rCB for the chosen products was derived. Compared to industrial carbon black, the use of rCB leads to a reduction in performance. Although increasing the filler content partially compensated for the mineral content in rCB and led to a slight improvement, it could not fully offset the performance loss.
Recovered Carbon Black (rCB) from scrap tire pyrolysis offers a potential alternative to fossil-based virgin Carbon Black (CB) in the context of a circular economy. This study investigated the influence of pyrolysis process parameters on rCB yield and quality at laboratory and semi-industrial scales. The resulting rCBs were characterized and found to have surface and structural properties comparable to N500 and N600 series CBs, but with higher mineral and volatile contents. The quality of rCB is influenced by the feedstock composition, particularly the ratio of organic to inorganic components as well as key process parameters such as heating rate, pyrolysis temperature and residence time. Higher heating rates accelerate degradation and shift product distribution toward increased oil yield and reduced rCB formation, while higher pyrolysis temperatures lead to lower volatile content in rCB. Additionally, reactor and process design affect heat distribution, transfer efficiency, and mixing behavior, further shaping rCB properties. However, further testing is required to evaluate the actual in-rubber properties of rCBs. Therefore, additional tests are planned, incorporating rCB into butyl and nitrile rubber-based elastomer compounds, which will be addressed in a follow-up study. In addition, data from the current experiments will support a comprehensive Life Cycle Assessment (LCA) to evaluate the environmental impacts of tire pyrolysis and rCB production compared to other recycling methods, with details to follow in a future publication.
The thermoplastic injection moulding process is very important in the plastics industry, as it enables automated production, supports high productivity and allows the production of plastic parts with complex geometries. It is possible to split into two large groups of polymers: amorphous and semicrystalline. Cooling rate and other injection moulding parameters have a great influence on the final properties of the plastic part. Regarding the use of aluminium as cavity material in injection moulds, new variables must be included in the analysis, since its thermal properties are significantly different from those presented by steels, which are traditionally used. In this way, the purpose of this study was to evaluate the effect of aluminium and steel cavities on different types of thermoplastics belonging to the two classes of polymers by assessing the injection parameters of a high-production part (automotive cup holder). In terms of productivity factors, moulds made of aluminium using semicrystalline polymers showed more significant reductions in cycle time compared to amorphous materials. Specifically, polypropylene exhibited a cycle time reduction between 40.6% and 52.5% when compared to steel moulds, while polyamide showed an even more substantial reduction, ranging between 56% and 63.5%. As for warpage, the amorphous materials displayed the lowest values for both types of moulds, but they also exhibited greater variations in isothermal simulations compared to semicrystalline materials. In relation to the mould materials, aluminium mould exhibited the lowest warping results and smaller variations compared to the isothermal analyses for all polymers.
This study investigates the fabrication of alumina-based (Al2O3) ceramics using pressureless sintering, employing hematite (Fe2O3) as a sintering aid. Fe2O3 powders were synthesized via combustion and incorporated into Al2O3 concentrations of 0.5, 1.0, and 2.0 wt.%. The samples were sintered at 1400 °C and characterized by X-ray diffraction (XRD) with Rietveld refinement, thermogravimetric analysis (TG/DTG), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), and density measurements using the Archimedes method. The results demonstrated that the addition of Fe2O3 increased the densification of Al2O3 ceramics, with the highest densification (~85%) observed in samples containing 1.0 and 2.0 wt.% Fe2O3. XRD analysis identified only the corundum phase of Al2O3, suggesting that Fe2O3 was incorporated without forming secondary phases. However, Rietveld refinement calculations revealed distortions in the unit cell volume, which contributed to lowering the melting temperature of Al2O3, thereby facilitating sintering. SEM images showed that Fe2O3 acted as a grain growth inhibitor, resulting in finer microstructures with smaller grains. EDX mapping indicated that Fe ions preferentially accumulated in regions with higher pore concentrations. Thermal analysis demonstrated improved thermal stability in Fe2O3-containing samples. Overall, the study confirms that Fe2O3 serves as an effective sintering aid, enhancing densification and thermal stability while refining the microstructure of Al2O3 ceramics. These findings contribute to the development of optimized ceramic materials for high-performance applications.
Life Cycle Assessment (LCA) of additive manufacturing (AM) evaluates the environmental impacts associated with each stage of the process, from raw material extraction to end-of-life disposal. Unlike conventional manufacturing, AM offers significant advantages, such as reduced material waste, optimized designs for lightweight structures, and localized production, which can decrease transportation emissions. However, its environmental benefits are context-dependent, as energy-intensive processes like laser powder bed fusion or high reliance on specific materials can offset these gains. LCA provides a comprehensive framework to assess these trade-offs, guiding sustainable decision-making by identifying hotspots in energy use, material efficiency, and recyclability, ultimately driving innovation towards greener AM practices. This research conducted a cradle-to-gate study of a cylindrical dog-bone tensile specimen. The life-cycle inventory data were obtained from Ecoinvent for conventional manufacturing, while data from the literature review and our research were employed for laser-based powder bed fusion. The results obtained show that the additive manufacturing process is more environmentally friendly. Although the environmental impact is minor, this process consumes a large amount of energy, mainly due to the atomization process and the high laser power. Regarding the mechanical response, AM reduced the ductility but increased the yield strength and achieved the same fracture strength.
Recycling high-density polyethylene (HDPE) is crucial to addressing plastic waste challenges. This study investigates the mechanical properties of blends composed of HDPE, polybutylene terephthalate (PBT), and polyamide 6 (PA6). Blends with varying HDPE content (0, 70, 80, 90, and 100%) were analyzed using injection molding to determine their impact toughness and structural characteristics. PBT and PA6 (blended in a 50:50 ratio) were combined with HDPE to create composites with enhanced properties. Testing included unnotched impact strength analysis and scanning electron microscopy (SEM). HDPE, a flexible thermoplastic, was paired with PBT and PA6, known for their strength and heat resistance, to produce a blend with superior mechanical performance. Results reveal that incorporating HDPE enhances the impact toughness of the composites compared to the pure PBT/PA6 blend, offering promising potential for many diverse applications in materials engineering in the automotive industry, household products, and protective casings of electronic products.
Carbon nanotubes (CNTs) are essential for providing polymers with mechanical reinforcement and multifunctional properties. This study investigated two groups of nitrile butadiene rubber (NBR) nanocomposites containing single-walled carbon nanotubes (SWCNTs) and multi-walled carbon nanotubes (MWCNTs), respectively. SWCNTs were purified to remove appro-ximately 20 wt.% of impurities, and both CNTs were modified with polyethylene glycol tert-octylphenyl ether (Triton X-100) before emulsion compounding and 2-roll milling with NBR. MWCNTs were found to disperse in the elastomer matrix relatively uniformly, while SWCNTs formed aggregates. Consequently, NBR/MWCNT nanocomposites exhibited superior mechanical properties, e.g. a tensile strength of 10.8 MPa at 4.02 vol.% MWCNTs, compared to 5.6 MPa for NBR/SWCNT nanocomposites. Additionally, NBR/MWCNT nanocomposites exhibited more remarkable electrical conductivity and swelling resistance to toluene. The diameter of elastomer macromolecules (0.2–0.5 nm) is close to that of SWCNTs (1–2 nm), and their single graphene wall with a hollow structure makes SWCNTs almost as flexible as elastomer macromolecules. This similarity suggests that SWCNTs should be treated as a special type of polymer. SWCNTs cannot disperse as uniformly as MWCNTs in the elastomer matrix, likely due to their smaller size and lower sensitivity to mechanical shearing during the emulsion compounding and 2-roll milling process.
Quantum spin liquids of frustrated magnets are among the most attractive and basic systems in physics. Frustrated magnets exhibit exceptional properties as insulators and metals, making them advanced materials that represent materials for future technologies. Therefore, a reliable theory describing these materials is of great importance. The fermion condensation theory provides an analytical description of various frustrated quantum spin liquids capable of describing the thermodynamic and transport properties of magnets based on the idea of spinons, represented by chargeless fermions filling the Fermi sphere up to the Fermi momentum pF . We show that the low temperature thermodynamic of Sr3CuNb2O9 in magnetic fields is defined by strongly correlated quantum spin liquid. Our calculations of its thermodynamic properties agree well with recent experimental facts and allow us to reveal their scaling behavior, which is very similar to that observed both in heavy-fermion metals and in frustrated magnets or insulators. We demonstrate for the first time that Sr3CuNb2O9 belongs to the family of strongly correlated Fermi systems that form a new state of matter.
The growing demand for sustainable materials in the automotive industry has prompted research into natural fiber-reinforced composites. To reduce carbon footprints and enhance product sustainability, the sectors increasingly focus on renewable and biodegradable materials. Composites made from natural fibers, such as coir and hemp, offer a promising solution for creating lightweight, high-performance components with a reduced environmental impact.In this study, an experimental investigation was conducted to examine the impact of single and hybrid and treated and untreated fibers, on the properties of epoxy-based composites. Untreated hemp fiber with treated Coir fiber was used for the research. The composites were fabricated through the open mould hand lay-up technique. Samples were prepared by randomly dispersing the fibers in the epoxy matrix before pouring them into the respective moulds prepared according to ASTM standards. Tensile, impact, and hardness tests were conducted on the cured samples to determine their mechanical properties, while a scanning electron microscope was used to evaluate the fractured surface. Water absorption tendencies were also determined. The results showed that the sample denoted as 5CF wt.% had the best property combination with tensile strength (32.4 MPa), tensile modulus (11.9 GPa), flexural strength (167.0 MPa), and impact strength (46.8 kJ/mm2). It was discovered that hemp fiber-based composites were not enhanced properly due to lack of fiber surface modifications. Though optimum results were obtained from treated coir fiber-based single/distinct composite, untreated hemp fiber was discovered to aid some flexural modulus and hardness properties in the hybrid composite based on the best results obtained in its distinct-based composite. Therefore, untreated hemp fiber can be used in hybrid form with treated coir fiber where one of the fibers is scarce or when fiber surface medication is difficult to achieve. Thus, the results showed that 5CH-based composites are the most suitable composition for automotive components development where high-mechanical properties are essential.
The diagnosis of paper breakage faults during the papermaking process is of great significance for improving product quality and maintaining stability in the production process. This paper develops a cross-condition transfer learning fault diagnosis model. This study proposes a fault diagnosis method based on transfer learning to address the issue of single-condition diagnostic models performing poorly when applied to different conditions..This method uses both parameter transfer and feature transfer to diagnose faults across different conditions. At the same time, in response to the issue of insufficient small sample operating data, we introduce federated learning technology to explore the impact of model compression rates on the diagnostic accuracy of the federated global model during the federated model training process. The results indicate that compared to single operating condition models, fault diagnosis performance based on transfer learning across different operating conditions has improved. The diagnostic model based on feature transfer performs even better, achieving accuracy rates of 98.31%, 94.64%, and 96.43% under different transfer tasks, allowing for accurate classification of the majority of samples. Additionally, the federated learning method provides an effective solution for fault diagnosis in small sample operating conditions, and an appropriate model compression rate can ensure diagnostic accuracy while protecting data privacy.
