Search Results (141)

In this research project a large linear electromagnetic actuator (LLEA) was designed and manufactured. The electromagnetic performance was published in previous works, but in this paper we focus on the technological challenges related to the manufacturing in particular. This LLEA was based on the magnet-free switched-reluctance principle, having six effective energised stator “teeth” and four passive mover parts (4:6 ratio). Various aspects and challenges encountered during the manufacturing, transport, and assembly are discussed. Thermal expansion of steel contributed to the decision of the modular design, with each module having 1.3 m in length, with a 2 mm longitudinal dilatation gap. The initial prototype was tested with a 10.6 m length, with plans to extend the test track to 60 m, which was fully achievable due to the modular design and required 29 tons of electrical steel to be built. The stator laminations were cut by a bespoke progressive tool with stamping, and other parts by a CO₂ laser. Mounting was based on welding (back of the stator) and clamping plates (through insulated bolts). The linear longitudinal force was on the order of 8 kN, with the main air gap of 7.5–10 mm on either side of the mover. The lateral forces could exceed 40 kN and were supported by appropriate construction steel members bolted to the concrete floor. The overall mechanical tolerances after installation remained below 0.5 mm. The technology used for constructing this prototype demonstrated the cost-effective way for a semi-industrial manufacturing scale. Full article

Energy harvesting systems (EHSs) are widely used to power wireless sensors. Piezoelectric harvesters have the advantage of producing an electric signal directly related to the exciting force and can thus be used to power condition monitoring sensors in dynamically loaded structures such as bridges. The need for such monitoring is exemplified by the fact that the condition of close to 25% of public roadway bridges in, e.g., Germany is not satisfactory. Stack-based piezoelectric energy harvesting systems (pEHSs) installed near bridge bearings could provide information about the traffic and dynamic loads on the one hand and condition-dependent changes in the bridge characteristics on the other. This paper presents an approach to co-optimizing the design of the mechanical and electrical components using a nonlinear solver. Such an approach has not been described in the open literature to the best of the authors’ knowledge. The mechanical excitation is estimated through a finite element simulation, and the electric circuitry is modeled in Simulink to account for the nonlinear characteristics of rectifying diodes. We use real traffic data to create statistical randomized scenarios for the optimization and statistical variation. A main result of this work is that it reveals the strong dependence of the energy output on the interaction between bridge, harvester, and traffic details. A second result is that the methodology yields design criteria for the harvester such that the energy output is maximized. Through the case study of an actual middle-sized bridge in Germany, we demonstrate the feasibility of harvesting a time-averaged power of several milliwatts throughout the day. Comparing the total amount of harvested energy for 1000 randomized traffic scenarios, we demonstrate the suitability of pEHS to power wireless sensor nodes. In addition, we show the potential sensory usability for traffic observation (vehicle frequency, vehicle weight, axle load, etc.). Full article

Energy Performance Contracts (EPC) are performance-based financing mechanisms designed to improve energy efficiency and support renewable energy adoption in the public sector. This study examines the economic efficiency of a 1710.72 kWp solar power plant (SPP), implemented under an EPC at Alanya Alaaddin Keykubat University, using a regression-based analysis. The model evaluates the effects of solar radiation, investment cost, and electricity sales price on unit production cost, and its predictions were compared with actual production data. Results show the system exceeded the EPC contract target by 16.2%, producing 2,423,472.28 kWh in its first year and preventing 1168.64 tons of CO₂ emissions. The developed multiple linear regression model achieved a predictive error margin of 14.7%, confirming its validity. This study highlights the technical, economic, and environmental benefits of EPC applications in Türkiye’s public institutions and offers a practical decision-support framework for policymakers. The novelty lies in integrating a regression model with operational data and providing a comparative assessment of planned, predicted, and actual outcomes. Full article

High-Bandwidth Memory (HBM) enables the bandwidth required by modern AI and high-performance computing, yet its three dimensional stack traps heat and amplifies thermo mechanical stress. We first review how conventional solutions such as heat spreaders, microchannels, high density Through-Silicon Vias (TSVs), and Mass Reflow Molded Underfill (MR MUF) underfills lower but do not eliminate the internal thermal resistance that rises sharply beyond 12layer stacks. We then synthesize recent hybrid bonding studies, showing that an optimized Cu pad density, interface characteristic, and mechanical treatments can cut junction-to-junction thermal resistance by between 22.8% and 47%, raise vertical thermal conductivity by up to three times, and shrink the stack height by more than 15%. A meta-analysis identifies design thresholds such as at least 20% Cu coverage that balances heat flow, interfacial stress, and reliability. The review next traces the chain from Coefficient of Thermal Expansion (CTE) mismatch to Cu protrusion, delamination, and warpage and classifies mitigation strategies into (i) material selection including SiCN dielectrics, nano twinned Cu, and polymer composites, (ii) process technologies such as sub-200 °C plasma-activated bonding and Chemical Mechanical Polishing (CMP) anneal co-optimization, and (iii) the structural design, including staggered stack and filleted corners. Integrating these levers suppresses stress hotspots and extends fatigue life in more than 16layer stacks. Finally, we outline a research roadmap combining a multiscale simulation with high layer prototyping to co-optimize thermal, mechanical, and electrical metrics for next-generation 20-layer HBM. Full article

This study investigates the effect of boron carbide (B₄C) ceramic reinforcement on the microstructural, mechanical, electrical, and electrochemical properties of CoCrMo, Ti, and 17-4 PH alloys produced via powder metallurgy for potential biomedical applications. A systematic experimental design was employed, incorporating varying B₄C contents into each matrix through mechanical alloying, cold pressing, and vacuum sintering. The microstructural integrity and dispersion of B₄C were examined using scanning electron microscopy. The performance of the materials was evaluated using several methods, including Vickers hardness, pin-on-disk wear testing, ultrasonic elastic modulus measurements, electrical conductivity, and electrochemical assessments (potentiodynamic polarization and EIS). This study’s findings demonstrated that B₄C significantly enhanced the hardness and wear resistance of all alloys, especially Ti- and CoCrMo-based systems. However, an inverse correlation was observed between B₄C content and corrosion resistance, especially in 17-4 PH matrices. Ti-5B₄C was identified as the most balanced composition, exhibiting high wear resistance, low corrosion rate and elastic modulus values approaching those of human bone. Weibull analysis validated the consistency and reliability of key performance metrics. The results show that adding B₄C can change the properties of biomedical alloys, offering engineering advantages for B₄C-reinforced biomedical implants. Ti-B₄C composites exhibit considerable potential for application in advanced implant technologies. Full article

This study systematically investigates the mechanism by which total ionizing dose (TID) affects the lifetime degradation of NMOS devices induced by hot-carrier injection (HCI). Experiments involved Cobalt-60 (Co-60) gamma-ray irradiation to a cumulative dose of 500 krad (Si), followed by 168 h annealing at 100 °C to simulate long-term stability. However, under HCI stress conditions (V_D = 2.7 V, V_G = 1.8 V), irradiated devices show a 6.93% increase in threshold voltage shift (ΔV_th) compared to non-irradiated counterparts. According to the IEC 62416 standard, the lifetime degradation of irradiated devices induced by HCI stress is only 65% of that of non-irradiated devices. Conversely, when the saturation drain current (I_Dsat) degrades by 10%, the lifetime doubles compared to non-irradiated counterparts. Mechanistic analysis demonstrates that partial neutralization of E’ center positive charges at the gate oxide interface by hot electrons weakens the electric field shielding effect, accelerating ΔV_th drift, while interface trap charges contribute minimally to degradation due to annealing-induced self-healing. The saturation drain current shift degradation primarily correlates with electron mobility variations. This work elucidates the multi-physics mechanisms through which TID impacts device reliability and provides critical insights for radiation-hardened design optimization. Full article

This study investigates a hybrid renewable energy system combining the municipal solid waste (MSW) gasification and solar photovoltaic (PV) for electricity generation in Lobito, Angola. A fixed-bed downdraft gasifier was selected for MSW gasification, where the thermal decomposition of waste under controlled air flow produces syngas rich in CO and H₂. The syngas is treated to remove contaminants before powering a combined cycle. The PV system was designed for optimal energy generation, considering local solar radiation and shading effects. Simulation tools, including Aspen Plus v11.0, PVsyst v8, and HOMER Pro software 3.16.2, were used for modeling and optimization. The hybrid system generates 62 GWh/year of electricity, with the gasifier contributing 42 GWh/year, and the PV system contributing 20 GWh/year. This total energy output, sufficient to power 1186 households, demonstrates an integration mechanism that mitigates the intermittency of solar energy through continuous MSW gasification. However, the system lacks surplus electricity for green hydrogen production, given the region’s energy deficit. Economically, the system achieves a Levelized Cost of Energy of 0.1792 USD/kWh and a payback period of 16 years. This extended payback period is mainly due to the hydrogen production system, which has a low production rate and is not economically viable. When excluding H₂ production, the payback period is reduced to 11 years, making the hybrid system more attractive. Environmental benefits include a reduction in CO₂ emissions of 42,000 t/year from MSW gasification and 395 t/year from PV production, while also addressing waste management challenges. This study highlights the mechanisms behind hybrid system operation, emphasizing its role in reducing energy poverty, improving public health, and promoting sustainable development in Angola. Full article

In the context of autonomous environmental monitoring, this study investigates a surface acoustic wave (SAW) sensor designed for selective carbon dioxide (CO₂) detection. The sensor is based on a LiTaO₃ piezoelectric substrate with copper interdigital transducers and a polyetherimide (PEI) layer, chosen for its high electromechanical coupling and strong CO₂ affinity. Finite element simulations were conducted to analyze the resonance frequency response under varying gas concentrations, film thicknesses, pressures, and temperatures. Results demonstrate a linear and sensitive frequency shift, with detection capability starting from 10 ppm. The sensor’s autonomy is ensured by a piezoelectric energy harvester composed of a cantilever beam structure with an attached seismic mass, where mechanical vibrations induce stress in a piezoelectric layer (PZT-5H or PVDF), generating electrical energy via the direct piezoelectric effect. Analytical and numerical analyses were performed to evaluate the influence of excitation frequency, material properties, and optimal load on power output. This integrated configuration offers a compact and energy-independent solution for real-time CO₂ monitoring in low-power or inaccessible environments. Full article

The process of conceptualisation and prototyping of electric energy products is demanding due to the need for a multifaceted approach to product design. This task becomes even more complex during sustainable development, within which supporting techniques are sought. Energy conversion products such as electric motorcycles require special attention due to their impact on energy efficiency, environmental emissions, and operating and production costs. The research gap refers to the lack of a model to aggregate these aspects simultaneously. The objective of the research was to develop a CQ-LCA model (Cost–Quality–Life Cycle Assessment) supporting the creation of alternative product solutions and their evaluation in terms of the following: (i) environmental impact in the life cycle (LCA), (ii) quality, and (iii) production and/or purchase costs. The model was developed in seven main stages and tested for electric motorcycles and their ten prototypes, which are examples of modern products that convert electrical energy into mechanical energy. Using the EDAS method, the quality of electric motorcycle prototypes was calculated. Then, by the LCA method according to ISO 14040, the CO₂ emissions were estimated and modelled adequately to quality change. Next, by the parametric model based on the static method and the cost value function, including the nominal least squares method, the cost was estimated adequately to quality and environmental change. The model provided a qualitative and quantitative interpretation of electric motorcycle prototypes (CQ-LCA), allowing for the consideration of product characteristics, such as engine power, charging time, and battery capacity, but also environmental impacts and costs. The originality is the provision of a multi-aspect morphological analysis, after which different scenarios of product solutions. The model can be useful for various commonly used energy-converting products. Full article

Droplet-based microfluidics (DBM) has emerged as a powerful tool for a wide range of biochemical applications, from single-cell analysis and drug screening to diagnostics and tissue engineering. This review provides a comprehensive overview of the latest advancements in droplet generation and trapping techniques, highlighting both passive and active approaches. Passive methods—such as co-flow, cross-flow, and flow-focusing geometries—rely on hydrodynamic instabilities and capillary effects, offering simplicity and integration with compact devices, though often at the cost of tunability. In contrast, active methods exploit external fields—electric, magnetic, thermal, or mechanical—to enable on-demand droplet control, allowing for higher precision and throughput. Furthermore, we explore innovative trapping mechanisms such as hydrodynamic resistance networks, microfabricated U-shaped wells, and anchor-based systems that enable precise spatial immobilization of droplets. In the final section, we also examine active droplet sorting strategies, including electric, magnetic, acoustic, and thermal methods, as essential tools for downstream analysis and high-throughput workflows. These manipulation strategies facilitate in situ chemical and biological analyses, enhance experimental reproducibility, and are increasingly adaptable to industrial-scale applications. Emphasis is placed on the design flexibility, scalability, and biological compatibility of each method, offering critical insights for selecting appropriate techniques based on experimental needs and operational constraints. Full article

Among different kinds of renewable energy sources, ocean wave energy offers a promising source of low-carbon electricity. However, despite this potential, ocean wave energy systems can have notable environmental impacts, which remain underexplored. Environmental life cycle assessment (LCA) is a method that can be used to evaluate the environmental impact of these systems. But few LCAs have been conducted for wave energy converters (WECs), and no prior studies specifically address onshore oscillating water column (OWC) devices, leaving a clear gap in this field. This research provides a cradle-to-gate LCA for an OWC device, using the 500 kW LIMPET OWC plant, located on the Isle of Islay in Scotland, as a case study. The assessment investigated the environmental impacts of the plant across 19 impact categories. OpenLCA 2.0 software was used for the analysis, with background data sourced from the Ecoinvent database version 3.8. The ReCiPe 2016 Midpoint (H) and Cumulative Energy Demand (CED) methods were used for the impact assessment. The results revealed a Global Warming Potential (GWP) of 56 kg CO₂ eq/kWh and a carbon payback period of 0.14 years. The energy payback period is significantly higher at 196 years, largely due to the plant’s inefficient energy capture and recurring operational failures reported. These findings highlight that although ocean wave energy is a renewable energy source, WEC’s efficiency and reliability are key factors for sustainable electricity generation. Furthermore, the findings conclude the need for selecting eco-friendly construction materials in OWC construction, namely chamber construction, and the advancement of energy-harnessing mechanisms, such as in Power Take-off (PTO) systems, to improve energy efficiency and reliability. Moreover, the importance of material recycling at the end-of-life stage, which was not accounted for in this cradle-to-gate analysis yet, is underscored for offsetting a portion of the associated environmental impacts. This research contributes novel insights into sustainable construction practices for OWC devices, offering valuable guidance for future wave energy converter designs. Full article

The paper presents the design and development of a universal simulation model named SustainSIM, intended for optimizing the carbon footprint in mechanical engineering production. The objective of this model is to enable enterprises to accurately quantify, monitor, and simulate CO₂ emissions generated during various manufacturing processes, thereby identifying and evaluating effective reduction strategies. The paper thoroughly examines methodologies for data collection and processing, determination of emission factors, and categorization of emissions (Scope 1 and Scope 2), utilizing standards such as the GHG Protocol and associated databases. Through a digital simulation environment created in Unity Engine, the model interactively visualizes the impacts of implementing green technologies—such as solar panels, electric vehicles, and heat pumps—on reducing the overall carbon footprint. The practical applicability of the model was validated using a mechanical engineering company as a case study, where simulations confirmed the model’s potential in supporting sustainable decision-making and production process optimization. The findings suggest that the implementation of such a tool can significantly contribute to environmentally responsible management and the reduction of industrial emissions. In comparison to existing methods such as SimaPro/OpenLCA (detailed LCA) and the Corporate Calculator (GHG Protocol), SustainSIM achieves the same accuracy in calculating Scopes 1/2, while reducing the analysis time to less than 15% and decreasing the requirements for expertise. Unlike simulation packages like Energy Plus, users can modify parameters without scripting, and they can see the immediate impact in CO₂e. Full article

Flexible pressure sensors face the dual challenges of weak signal extraction and environmental noise suppression in wearable electronics and human-machine interfaces. This research proposes an intelligent pressure sensor utilizing chitosan/carbon nanotube/melamine sponge (CS/CNT/MS) composites, achieving high-performance sensing through a dual-stage noise reduction architecture that combines mechanical pre-filtration and electrical synergistic regulation. An innovative compressed-stitching encapsulation technique creates pressure sensors with equivalent mechanical low-pass filtering characteristics, actively eliminating interference signals below 3 kPa while maintaining linear response within the 3–20 kPa effective loading range (sensitivity: 0.053 kPa⁻¹). The synergistic effects of CS molecular cross-linking and CNTs’ three-dimensional conductive network endow the device with a 72 ms response time, 24 ms recovery speed, and over 3500-cycle compression stability. Successful applications in smart sport monitoring and tactile interactive interfaces demonstrate a material-structure-circuit co-design paradigm for mechanical perception in complex environments. Full article

To address the needs of enhancing adaptive control and reducing emissions at intersections within intelligent traffic signal systems, this study innovatively proposes a deep reinforcement learning signal control model tailored for mixed traffic flows. Addressing shortcomings in existing models that overlook mixed traffic scenarios, neglect optimization of CO₂ emissions, and overly rely on high-performance algorithms, our model utilizes vehicle queue length, average speed, numbers of gasoline and electric vehicles, and signal phases as state information. It employs a fixed-phase strategy to decide between maintaining or switching signal states and incorporates a reward function that balances vehicle CO₂ emissions and waiting times, significantly lowering intersection carbon emissions. Following training with reinforcement learning algorithms, the model consistently demonstrates effective control outcomes. Simulation results using the SUMO platform reveal that our designed reward mechanism facilitates the rapid and stable convergence of intelligent agents. Compared with Fixed Time Control (FTC), Actuated Traffic Signal Control (ATSC), and Fuel-ECO TSC (FECO-TSC) methods, our model achieves superior performance in average waiting times and CO₂ emissions. Even across scenarios with gasoline–electric vehicle ratios of 25–75%, 50–50%, and 75–25%, the model exhibits significant advantages. These simulations validate the model’s rationality and effectiveness in promoting low-carbon travel and efficient signal control. Full article

Gas evolution in lithium-ion batteries represents a pivotal yet underaddressed concern, significantly compromising long-term cyclability and safety through complex interfacial dynamics and material degradation across both normal operation and extreme thermal scenarios. While extensive research has focused on isolated gas generation mechanisms in specific components, critical knowledge gaps persist in understanding cross-component interactions and the cascading failure pathways it induced. This review systematically decouples gas generation mechanisms at cathodes (e.g., lattice oxygen-driven CO₂/CO in high-nickel layered oxides), anodes (e.g., stress-triggered solvent reduction in silicon composites), electrolytes (solvent decomposition), and auxiliary materials (binder/separator degradation), while uniquely establishing their synergistic impacts on battery stability. Distinct from prior modular analyses, we emphasize that: (1) emerging systems exhibit fundamentally different gas evolution thermodynamics compared to conventional materials, exemplified by sulfide solid electrolytes releasing H₂S/SO₂ via unique anionic redox pathways; (2) gas crosstalk between components creates compounding risks—retained gases induce electrolyte dry-out and ion transport barriers during cycling, while combustible gas–O₂ mixtures accelerate thermal runaway through chain reactions. This review proposes three key strategies to suppress gas generation: (1) oxygen lattice stabilization via dopant engineering, (2) solvent decomposition mitigation through tailored interphases engineering, and (3) gas-selective adaptive separator development. Furthermore, it establishes a multiscale design framework spanning atomic defect control to pack-level thermal management, providing actionable guidelines for battery engineering. By correlating early gas detection metrics with degradation patterns, the work enables predictive safety systems and standardized protocols, directly guiding the development of reliable high-energy batteries for electric vehicles and grid storage. Full article