Search Results (222)

Inelastic electron tunneling spectroscopy (IETS) has emerged as a powerful vibrational spectroscopy technique for molecular electronic junctions, providing unique insights into molecular vibrations and electron–phonon coupling at the nanoscale. In this review, we present a comprehensive overview of IETS in molecular junctions, tracing its development from foundational principles to the latest advances. We begin with the theoretical background, detailing the mechanisms by which inelastic tunneling processes generate vibrational fingerprints of molecules, and highlighting how IETS complements optical spectroscopies by accessing electrically driven vibrational excitations. We then discuss recent progress in experimental techniques and device architectures that have broadened the applicability of IETS. Central focus is given to emerging applications of IETS over the last decade: molecular sensing (identification of chemical bonds and conformational changes in junctions), thermoelectric energy conversion (probing vibrational contributions to molecular thermopower), molecular switches and functional devices (monitoring bias-driven molecular state changes via vibrational signatures), spintronic molecular junctions (detecting spin excitations and spin–vibration interplay), and advanced data analysis approaches such as machine learning for interpreting complex tunneling spectra. Finally, we discuss current challenges, including sensitivity at room temperature, spectral interpretation, and integration into practical devices. This review aims to serve as a thorough reference for researchers in physics, chemistry, and materials science, consolidating state-of-the-art understanding of IETS in molecular junctions and its growing role in molecular-scale device characterization. Full article

On-chip optical accelerometers can be a promising alternative to capacitive, piezo-resistive, and piezo-electric accelerometers in some applications due to their immunity to electromagnetic interference and high sensitivity, which allow for robust operation in electromagnetically noisy environments. This paper focuses on the characterization of an easy-to-fabricate tri-axial fiber-free optical MEMS accelerometer, which employs a simple assembly consisting of a light emitting diode (LED), a quadrant photodetector (QPD), and a suspended proof mass, measuring acceleration through light power modulation. This configuration enables simple readout circuitry without the need for complex digital signal processing (DSP). Performance modeling was conducted to simulate the LED’s irradiance profile and its interaction with the proof mass and QPD. Additionally, experimental tests were performed to measure the device’s mechanical sensitivity and validate the mechanical model. Lateral mechanical sensitivity is obtained with acceptable discrepancy from that obtained from FEA simulations. This work consolidates the performance of the design adapted and demonstrates the accelerometer’s feasibility for practical applications. Full article

Aircraft and fixed-wing drones, designed to perform vertical take-off and landing (VTOL), often incorporate unconventional configurations that offer unique capabilities but simultaneously pose significant challenges in flight mechanics modeling, whose reliability strongly depends on the correct tuning of the inertial and aerodynamic parameters. Having a good characterization of the aerodynamics represents a critical issue, especially in the design and optimization of unconventional aircraft configurations, when, indeed, one is bound to employ empirical or semi-empirical methods, devised for conventional geometries, that struggle to capture complex aerodynamic interactions. Alternatives such as high-fidelity computational fluid dynamics (CFD) simulations, although more accurate, are typically expensive and impractical for both preliminary design and lofting optimization. This work introduces a procedure that exploits multiple analyses conducted through semi-empirical methodologies implemented in the USAF Digital DATCOM to develop a flight mechanics model for fixed-wing unmanned aerial vehicles (UAVs). The reference UAV chosen to test the proposed procedure is the Dragonfly DS-1, an electric VTOL UAV developed by Overspace Aviation, featuring a three-surface configuration. The accuracy of the polar data, i.e., the lift and drag coefficients, is assessed through comparisons with computational fluid dynamics simulations and flight data. The main discrepancies are found in the drag estimation. The present work represents a preliminary investigation into the possible extension of semi-empirical methods, consolidated for traditional configurations, to unconventional aircraft so as to support early-stage UAV design. Full article

To address the challenge of dynamic monitoring during grouting operations in coal mine fault zones under pressurized mining, this study proposes the Borehole–Tunnel Joint Resistivity Method (BTJRM). By integrating three-dimensional (3D) electrode arrays in both tunnels and boreholes with 3D resistivity inversion technology, this approach enables fully automated underground data acquisition and real-time processing, facilitating comprehensive dynamic monitoring of grout propagation. A case study was conducted on a coal mine fault grouting project, where tunnel and borehole survey lines were deployed to construct a 3D cross-monitoring network, overcoming the limitations of traditional 2D data acquisition. Finite volume method and quasi-Gauss–Newton inversion algorithms were employed to analyze dynamic resistivity variations, enhancing spatial resolution for detailed characterization of grout migration. Key findings include: (1) Grout diffusion reduced resistivity by 10%, aligning with electrical response patterns during fracture-filling stages; (2) 3D inversion reveals that grout propagates along the principal stress axis, forming a “Y”-shaped low-resistivity anomaly zone that penetrates the fault structural block and extends into roadway areas. The maximum planar and vertical displacements of grout reach 100 m and 40 m, respectively. Thirty days post-grouting, resistivity recovers by up to 22%, reflecting the electrical signature of grout consolidation; (3) This method enables 3D reconstruction of grout diffusion pathways, extends the time window for early warning of water-conducting channel development, and enhances pre-warning capabilities for grout migration. It provides a robust framework for real-time sealing control of fault strata, offering a novel dynamic monitoring technology for mine water inrush prevention. The technology can provide reliable grouting evaluation for mine disaster control engineering. Full article

The currently used electrical energy devices for portable applications are in limited life and need of frequent recharging, it is a big bottleneck for wireless and transportation systems. The scientific community is motivated to find innovative and efficient devices to convert environmental energy into useful forms. Nanogenerator can mitigate this issue by harvesting ambient energy of different forms into useful electrical energy. Particularly flexible nanogenerators can efficiently convert ambient mechanical energy into electrical energy which can be fruitfully used for self-powered sensors and electronic appliances. Zinc oxide is an interesting photosensitive and piezoelectric material that is expected to play a vital role in the synergetic harvesting of environmental thermal, sound, mechanical, and solar energies. As ZnO can be synthesized using easy methods and materials at low cost, the conversion efficiencies of solar and other energy forms can increase considerably. ZnO is a versatile material with interesting semiconducting, optical, and piezoelectric properties; it can be used advantageously to harvest more than one type of ambient energy. The coupled semiconducting and piezoelectric properties of ZnO are attractive for fabricating nanogenerators capable of harvesting both ambient optical and mechanical energies simultaneously. These nanolevel conversion devices are much required to power remote and implantable devices without the need for additional power sources. The present review briefly discusses the principles and mechanisms of different energy harvesting abilities of ZnO nanorods and their composites by consolidating available literature. In addition, the developments taking place in nanogenerators of different kinds—such as photovoltaic, piezoelectric, pyroelectric, and triboelectrics for self-powered technology—and their progress in hybrid energy harvesting application is reviewed. Full article

The ability to walk is often lost after neural injury, leading to multiple secondary complications that reduce quality of life and increase healthcare costs. The current rehabilitation interventions primarily focus on restoring leg movements through intensive training on a treadmill or using robotic devices, but ignore engaging the arms. Several groups have recently shown that simultaneous arm and leg (A&L) cycling improves walking function and interlimb connectivity. These findings highlight the importance of neuronal pathways between the arm (cervical) and leg (lumbar) control regions in the spinal cord during locomotion, and emphasize the need for activating these pathways to improve walking after neural injury or disease. While the findings to date provide important evidence about actively including the arms in walking rehabilitation, these strategies have yet to be optimized. Moreover, improvements beyond A&L cycling alone may be possible with conjunctive targeted strategies to enhance spinal interlimb connectivity. The aim of this review is to highlight the current evidence for improvements in walking function and neural interlimb connectivity after neural injury or disease with cycling-based rehabilitation paradigms. Furthermore, strategies to enhance the outcomes of A&L cycling as a rehabilitation strategy are explored. These include the use of functional electrical stimulation-assisted cycling in acute care settings, utilizing non-invasive transcutaneous spinal cord stimulation to activate previously inaccessible circuitry in the spinal cord, and the use of paired arm and leg rehabilitation robotics. This review aims to consolidate the effects of exercise interventions that incorporate the arms on improved outcomes for walking, functional mobility, and neurological integrity, underscoring the importance of integrating the arms into the rehabilitation of walking after neurological conditions affecting sensorimotor function. Full article

Silver nanowires (AgNWs) have garnered significant attention in nanotechnology due to their unique mechanical and electrical properties and versatile applications. This review explores the synthesis of AgNWs, with a specific focus on the utilization of millifluidic flow reactors (MFRs) as a promising platform for controlled and efficient production. It begins by elucidating the exceptional characteristics and relevance of AgNWs in various technological domains and then delves into the principles and advantages of MFRs by showcasing their pivotal role in enhancing the precision and scalability of nanowire synthesis. Within this review, an overview of the diverse synthetic methods employed for AgNW production using MFRs is provided. Special attention is given to the intricate parameters and factors influencing synthesis and how MFRs offer superior control over these critical variables. Recent advances in this field are highlighted, revealing innovative strategies and promising developments that have emerged. As with any burgeoning field, challenges are expected, so future directions are explored, offering insights into the current limitations and opportunities for further exploration. In conclusion, this review consolidates the state-of-the-art knowledge in AgNW synthesis and emphasizes the critical role of MFRs in shaping the future of nanomaterial production and nanomanufacturing. Full article

The growing complexity of distributed energy systems and the rise of peer-to-peer energy markets demand innovative solutions for efficient, resilient, and sustainable energy management. However, existing research often remains fragmented, with limited integration between control strategies, optimization frameworks, and practical implementation. This paper presents a comprehensive systematic review, following the PRISMA methodology, that synthesizes findings from 94 high-quality studies and addresses the lack of consolidated insights across technical, operational, and architectural layers. This review highlights advancements in six key areas: optimization and modeling, multi-agent systems, simulations, blockchain and smart contracts, robust frameworks, and electric machines. Despite progress, several studies reveal challenges related to scalability, data privacy, computational complexity, and system adaptability, particularly in dynamic and decentralized environments. Stochastic–robust optimization and multi-agent systems improve decentralized coordination, while blockchain enhances security and automation in peer-to-peer trading. Simulations validate energy strategies, bridging theory and practice, and electric machines support renewable integration and grid flexibility. The synthesis underscores the need for unified frameworks that combine artificial intelligence, predictive control, and secure communication protocols. This review aims to provide a roadmap for advancing distributed energy systems toward scalable, resilient, and sustainable energy solutions. Full article

The rapid growth of e-commerce has intensified the need for efficient and sustainable last-mile delivery solutions in urban environments. This paper explores the integration of electric vehicles (EVs) and artificial intelligence (AI) into a combined framework to enhance the environmental, operational, and economic performance of urban logistics. Through a comprehensive literature review, we examine current trends, technological developments, and implementation challenges at the intersection of smart mobility, green logistics, and digital transformation. We propose an operational framework that leverages AI for route optimization, fleet coordination, and energy management in EV-based delivery networks. This framework is validated through a real-world case study conducted in Lisbon, Portugal, where a logistics provider implemented a city consolidation center model supported by AI-driven optimization tools. Using key performance indicators—including delivery time, energy consumption, fleet utilization, customer satisfaction, and CO₂ emissions—we measure the pre- and post-AI deployment impacts. The results demonstrate significant improvements across all metrics, including a 15–20% reduction in delivery time, a 10–25% gain in energy efficiency, and up to a 40% decrease in emissions. The findings confirm that the synergy between EVs and AI provides a robust and scalable model for achieving sustainable last-mile logistics, supporting broader urban mobility and climate objectives. Full article

A comprehensive roadmap for advancing Electric Micromobility (EMM) systems addressing the fragmented and scarce information available in the field is defined as a transformative solution for urban transportation, targeting short-distance trips with compact, lightweight vehicles under 350 kg and maximum speeds of 45 km/h, such as bicycles, e-scooters, and skateboards, which offer flexible, eco-friendly alternatives to traditional transportation, easing congestion and promoting sustainable urban mobility ecosystems. This review aims to guide researchers by consolidating key technical insights and offering a foundation for future exploration in this domain. It examines critical components of EMM systems, including electric motors, batteries, power converters, and control strategies. Likewise, a comparative analysis of electric motors, such as PMSM, BLDC, SRM, and IM, highlights their unique advantages for micromobility applications. Battery technologies, including Lithium Iron Phosphate, Nickel Manganese Cobalt, Nickel-Cadmium, Sodium-Sulfur, Lithium-Ion and Sodium-Ion, are evaluated with a focus on energy density, efficiency, and environmental impact. The study delves deeply into power converters, emphasizing their critical role in optimizing energy flow and improving system performance. Furthermore, control techniques like PID, fuzzy logic, sliding mode, and model predictive control (MPC) are analyzed to enhance safety, efficiency, and adaptability in diverse EMM scenarios by using cutting-edge semiconductor devices like Silicon Carbide (SiC) and Gallium Nitride (GaN) in well-known configurations, such as buck, boost, buck–boost, and bidirectional converters to ensure great efficiency, reduce energy losses, and ensure compact and reliable designs. Ultimately, this review not only addresses existing gaps in the literature but also provides a guide for researchers, outlining future research directions to foster innovation and contribute to the development of sustainable, efficient, and environmentally friendly urban transportation systems. Full article

The issue of low resource utilization rate and high treatment cost in the disposal of construction waste and solid waste was a challenging problem. In order to seek a synergistic and efficient treatment method, based on the similarity of microstructural characteristics between clay, solid waste, and lithium slag particles, a dual-layer theory and model was used to conduct adaptive analysis at the electrochemical level, studying the solid–liquid–solid dual-layer theoretical model suitable for silicon–aluminum-phase materials. At the same time, the phenomenon of particle interface contact and the influence mechanism of ion adsorption on the surface of particles in the liquid phase were discussed, analyzing the ion selection mechanism for regulating the dual-layer of silicon–aluminum-phase materials and studying the method of clay-modified stabilization based on solid waste. Further laboratory tests and microscopic analyses were conducted to determine the engineering properties of the soil stabilized by the clay–solid waste synergistic stabilization and verified the effectiveness of the method. The research results showed that the trial soil stabilized by the theoretical model guidance was significantly stronger in unconfined compressive strength (1.44 MPa at 28 days) than the undisturbed clay (0.26 MPa at 28 days), and the scanning electron microscope (SEM) microscopic analysis results showed that the microscopic morphology of the modified stabilized soil specimen was tightly woven with a high-strength network-like structure. The research provided a theoretical basis and experimental reference for the synergistic treatment and resource utilization of waste soft soil and solid waste engineering problems. Full article

Artificial Neural Networks (ANNs) improve battery management in electric vehicles (EVs) by enhancing the safety, durability, and reliability of electrochemical batteries, particularly through improvements in the State of Charge (SOC) estimation. EV batteries operate under demanding conditions, which can affect performance and, in extreme cases, lead to critical failures such as thermal runaway—an exothermic chain reaction that may result in overheating, fires, and even explosions. Addressing these risks requires advanced diagnostic and management strategies, and machine learning presents a powerful solution due to its ability to adapt across multiple facets of battery management. The versatility of ML enables its application to material discovery, model development, quality control, real-time monitoring, charge optimization, and fault detection, positioning it as an essential technology for modern battery management systems. Specifically, ANN models excel at detecting subtle, complex patterns that reflect battery health and performance, crucial for accurate SOC estimation. The effectiveness of ML applications in this domain, however, is highly dependent on the selection of quality datasets, relevant features, and suitable algorithms. Advanced techniques such as active learning are being explored to enhance ANN model performance by improving the models’ responsiveness to diverse and nuanced battery behavior. This compact survey consolidates recent advances in machine learning for SOC estimation, analyzing the current state of the field and highlighting the challenges and opportunities that remain. By structuring insights from the extensive literature, this paper aims to establish ANNs as a foundational tool in next-generation battery management systems, ultimately supporting safer and more efficient EVs through real-time fault detection, accurate SOC estimation, and robust safety protocols. Future research directions include refining dataset quality, optimizing algorithm selection, and enhancing diagnostic precision, thereby broadening ANNs’ role in ensuring reliable battery management in electric vehicles. Full article

This paper provides a consolidated discussion and proposes significant measures in improving and advancing the performance of synchronous machines employed in electric traction applications designed for passenger electric vehicles (EVs). The paper quantifies the discussion on improving the power density (kW/kg) and efficiency (%η) of the machine with the commercially available solutions in terms of new design architectures, advanced emerging materials, and adoption of additive manufacturing (AM) technologies. New challenges and opportunities are identified for the optimized machine designs having the potential to meet the global standards while keeping the cost under control. This paper provides an overview of current trends, an introduction to innovative technologies, and changes in existing manufacturing practices to achieve high-performance electrical machines with improved fault tolerance capabilities and reliability. Thereby meeting the standards for the next generation of electric vehicles. Full article

In the maritime industry, the adoption of hybrid electric propulsion systems aims to enhance energy efficiency and environmental sustainability. However, this study originates from the fundamental question: ‘Are hybrid systems truly environmentally friendly?’ Ensuring optimal system performance requires accurate load analysis and an effective energy management system. Existing studies have limitations in addressing real-time load variability, long-term load patterns, and scalability across different operational conditions. To address these, this study proposes a standard load analyzer based on main engine power output data to conduct performance analysis. Using MATLAB/Simulink simulations and Excel VBA-based methods, the system evaluates key performance factors under various operational load conditions. Cross-validation between MATLAB and Excel ensured high accuracy, with a relative error rate below 0.01%. The results showed consistent performance indicators, offering reliable insights across vessel types and scenarios. The system’s lightweight design and rapid data acquisition enable effective energy management optimization. However, it has limitations in performing detailed analyses for life cycle assessment, operating expenditures, and capital expenditures. Future advancements in data consolidation and analytical methods will help the tool evolve into a comprehensive tool for multi-dimensional performance evaluation, addressing economic, environmental, and technical aspects. Full article

There is a growing demand for sustainable solutions in civil engineering concerning the carbon footprint of cementitious composites. Alkali-Activated Binders (AAB) are materials with great potential to replace ordinary Portland cement (OPC), with similar strength levels and lower environmental impact. Despite their improved environmental performance, their durability remains a gap in the literature, influenced by aspects of mechanical behavior, physical properties, and microstructure. This paper aims to assess the impact of steel slag aggregates and curing temperature of a proposed AAB based concrete formulation by characterizing fresh state, mechanical behavior, and microstructure. The proposed AAB is composed of fly ash (FA) and basic oxygen furnace (BOF) steel slag (SS) as precursors, sodium silicate and sodium hydroxide solution as activators, in total replacement of OPC, using baosteel slag short flow (BSSF) SS as aggregate in comparison with natural aggregate. The concrete formulation was designed to achieve a high-performance concrete (HPC) and a self-compacting concrete (SCC) behavior. Mechanical characterization encompassed hardened (compressive strength and Young’s modulus), fresh state (J-ring, slump flow, and T50), and durability tests (scanning electronic microscopy, water penetration under pressure, and chloride ion penetration). The compressive strength (64.1 ± 3.6 MPa) achieves the requirements of HPC, while the fresh state results fulfill the SCC requirements as well, with a spread diameter from 550 mm to 650 mm (SF-1 class). However, the flow time ranges from 3.5 s to 13.8 s. There was evidence of high chloride penetrability, affected by the lower electrical resistance inherent to the material. Otherwise, there was a low water penetration under pressure (3.5 cm), which indicates a well-consolidated microstructure with low connected porosity. Therefore, the durability assessment demonstrated a divergence in the results. These results indicate that the current durability tests of cementitious materials are not feasible for AAB, requiring adapted procedures for AAB composite characterization. Full article