The structure and thermophysical properties of polymer blends polyamide 6/high-density polyethylene with component ratios of 70:30, 45:55 and 30:70, which not only provide phase inversion of the blended polymers, but also the formation of an interpenetrating network, have been investigated by differential scanning calorimetry, scanning electron microscopy and light flash method. The data on the influence of blends composition on their mechanical properties, density, structure, temperature, as well as melting and crystallization heats of polymer components have been obtained. The regularities of changes in thermal diffusion, heat capacities and thermal conductivity coefficients of polyamide 6 and high-density polyethylene individually and as part of the blends in dependence on their composition and temperature have been established. A nonlinear increase of the thermal conductivity coefficient from temperature was revealed when melting a more easily melting component of the blend. It was found that the maximum increase in thermal conductivity occurs in the blend forming an interpenetrating network. A possible way of creating composites with adaptive thermal conductivity by melting one of the components of the blend is proposed.
Dry reforming of methane (DRM) is a promising strategy to closing the carbon loop. DRM valorises CO2 and CH4 by producing synthesis gas (H2 and CO), the precursor to various synthetic fuels. Key limitations of the DRM are the high-temperature requirements (600–1000 °C) and competing side reactions, many of which produce carbon that can deactivate the catalyst. Designing a stable, low-cost and active catalyst remains one of the greatest DRM challenges. One potential strategy to curtail the limitations that hinder DRM is to utilise visible light to access the localised surface plasmon resonance (LSPR) of metal catalysts. The current review discusses the recent developments in designing catalysts for LSPR-assisted thermocatalytic DRM. The thermodynamic and kinetic principles that underpin DRM are first introduced, followed by an overview of thermocatalyst design strategies. The mechanism behind LSPR is discussed, with recent developments and strategies for introducing LSPR to the DRM examined. The review offers a thorough overview of catalyst design for light-assisted DRM and may be used as a guide to developing stable and light-receptive catalysts for the reaction.
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.
The research focuses on the flow-induced motion (FIM) and energy harness of “Cir-Tri-Att” oscillators (CTAO). The wake was photographed by particle image velocimetry (PIV) to explore wake structures. With the increase of the aspect ratios: the ability of oscillators to galloping under self-excitation or external excitation is enhanced. When ζ = 0.033, Ur = 12.5, the maximum amplitude ratio A* = 2.408 for oscillators with α = 1:1. Moreover, oscillators with higher aspect ratios can bear larger loads, which is conducive to energy utilization and conversion. The maximum power output Pharn = 16.588 W and the optimal efficiency ηharn = 24.706% appear in oscillators with α = 1.5:1. Additionally, In the soft galloping (SG), the wake mode is 4P or 3P. The wake vortex is more broken and its scale increases, but the force effect of the oscillators is better and the oscillation is more stable. The pressure difference makes for a longer oscillation period. This paper summarizes the FIM, energy harness and wake structures of the CTAO under different working conditions, which provides theoretical and data support for the optimization oscillators of flow-induced motion tidal energy conversion system.
Double end face grinding machining is a highly efficient surface grinding technique. And grinding temperature is an important factor affecting the surface quality of workpieces. However, it is difficult to monitor the surface temperature of the workpiece in real time because of the covered contact between the grinding wheel and the upper and lower surfaces of the workpiece during the machining process. This paper aims to conduct a mechanistic analysis and experimental investigation of the machining process to address this challenge. Initially, the paper conducts an analysis of the kinematic mechanism, modal analysis, and the grinding force mechanism specific to the double end face grinding process. Afterwards, the mechanisms leading to the generation of grinding heat and the associated heat transfer mechanisms are explored in depth. The paper then proceeds to solve the instantaneous temperature field during double end face grinding by the finite element method (FEM). Furthermore, the micro and macro profile heights of the machined workpiece surfaces are measured and analyzed. The results show that the machined workpiece surface shows a high center and low edge. This is due to the fact that the temperature at the edge of the workpiece is higher than the center during machining, resulting in more material removal. Through these investigations, the study is able to determine the optimal process parameters for the machining process. This in turn improves machining efficiency and product conformity. And these findings not only guide practical production processes but also provide a foundation for future theoretical research in this area.
Nickel-based superalloys are the most reliable material choice for the hot sections of turbines. These superalloys are mainly employed in aircraft engines, particularly in the combustor and turbine sections. In this scenario, the growing need for materials that can endure high temperatures while retaining their strength has driven the development of IN939. Although IN939 holds these significant important properties and applications, it has received less attention in recent literature than other superalloys. This review aims to comprehensively analyze the main research on IN939 over the past 50 years. From 1970 to 1980, research primarily focused on the development of IN939 through casting methods. Between 1980 and 1990, the emphasis shifted to studying its oxidation resistance and microstructural stability during service. The period from 1990 to 2000 focused on repairing components after long service time at high temperatures. In recent decades, advances in additive manufacturing techniques have led to growing interest in developing IN939 using methods like laser powder bed fusion (LPBF). Research in the area has demonstrated that the LPBF technique offers a promising approach to manufacturing high-performance IN939 components.
Porous Cu(Mn):ZnO-MgO composites synthesized by polymeric sol-gel method were characterized. The crystal structure, morphology, spectral properties, the ability of the photogeneration of chemically active singlet oxygen under external visible irradiation, photocatalytic and antibacterial properties of porous composites were studied. Obtained composites consist of small ZnO and MgO crystals having size less than 20 nm. It was found that Cu2+ and Mn2+ ions are embedded into the lattices of ZnO and MgO crystals, altering their crystal cell parameters. The band gap values of obtained composites are 3.41 ÷ 3.42 eV which are slightly higher than the band gap of pure ZnO. Prepared materials demonstrate a high ability of photogeneration of chemically active singlet oxygen under blue light (λ = 405 nm) irradiation. It was found that dependencies of the intensity of singlet oxygen photogeneration from the power density of visible irradiation are linear. Photocatalytic decomposition of the diazo dye Chicago Sky Blue in solutions under UV and blue light irradiation proceeds rapidly in the presence of the prepared composites (constants rate of photocatalytic dye decomposition under UV irradiation are 0.024 min−1 and 0.025 min−1 for ZnO-MgO composites doped with Cu and Mn, correspondingly). Porous composites demonstrate superior antibacterial activity against gram-positive bacteria. These materials are promising for practical application in medicine and photocatalytic technologies of air and water cleaning.
A novel adaptive event-triggered control strategy is proposed for multi-quadrotor systems under intermittent communications, addressing the leader-follower consensus-seeking problem where the leader has an unknown bounded input. Firstly, an activation time ratio condition is proposed, eliminating the reliance on the maximum time interval of intermittent communication. Secondly, a compensation term related to the leader’s unknown bounded input is designed in the controller to compensate for the error caused by intermittent communication in each period. Meanwhile, a prediction method is developed to eliminate the dependence on continuous information of neighboring quadrotors. Zeno behavior is strictly excluded, and communication among quadrotors is efficiently reduced with the designed event-triggering condition. Finally, numerical simulations verify the effectiveness and superiority of the proposed control strategy.
In this work, Cobalt oxide nanoparticles (Co3O4·NPs) were synthesized via a simple sonochemical reaction by using polyethylene glycol (PEG) as a surfactant. Structural, morphological and spectroscopic analysis of obtained powder (Co3O4·NPs) was investigated by X-ray diffraction, FTIR spectroscopy and scanning electron microscope (SEM). The nanocrystalline nature of the sample was confirmed by XRD, which exhibits the cubic face-centered normal spinel structure of (Co3O4·NPs) and the space group of Fd-3m with the average crystallite size around 15 nm. FTIR spectrum shows two strong absorption bands of (Co+2–O) and (Co+3–O) which confirm the spinel structure of Co3O4·NPs. Moreover, SEM micrographs showed that the agglomeration of the nanoparticles was reduced by the addition of (PEG) surfactant and UV-Vis was used to study the synthesized material’s optical properties. The Co3O4 band gap ranged around 2.2 and 3.5 eV.
Unmanned Aerial Vehicles (UAVs) are versatile platforms with potential applications in precision agriculture, disaster management, and more. A core need across these applications is a navigation system that accurately estimates location based on environmental perception. Commercial UAVs use multiple onboard sensors whose fused data improves localization accuracy. The bioinspired Rat-Simultaneous Localization and Mapping (Rat-SLAM) system, is a promising alternative to be explored to tackle the localization and mapping problem of UAVs. Its cognitive capabilities, semi-metric map construction, and loop closure make it attractive for localization in complex environments. This work presents an improved Rat-SLAM algorithm for UAVs, focusing on three innovations. First, Spiking Neural Networks (SNNs) are incorporated into Rat-SLAM’s core modules to emulate biological processing with greater efficiency. Second, Neuromorphic Computing models the neurons of the SNNs, assessing the feasibility of implementing SNNs on specialized hardware to reduce software processing, a key advantage for UAVs with limited onboard resources. Third, SNNs are developed based on the Memristive Leaky Integrate-and-Fire model, integrating memristors into artificial neurons to leverage their low power and memory properties. Our approach was evaluated through trajectory simulations using the Hector Quadrotor UAV in the Gazebo environment within the Robot Operating System, yielding valuable insights and guiding future research directions.