Search Results (1,780)

A triboelectricity-enhanced piezoelectric nanogenerator (PENG) based on pressure-processed multi-electrospun polymeric layers is herein developed for efficient vibrational energy harvesting. The hybridization of piezoelectric and triboelectric mechanisms through electrospinning has been utilized to enhance electrical output by increasing contact areas and promoting alignment within piezoelectric materials. A multi-layer structure comprising alternating poly (vinylidene fluoride) (PVDF) and poly (hexamethylene adipamide) (PA 6/6) exhibits superior electrical performance. A lateral Janus configuration, providing distinct positive and negative triboelectric polarities, has further optimized device efficiency. This approach introduces a novel operational mechanism, enabling superior performance compared to conventional methods. The fiber-based architecture ensures exceptional flexibility, low weight, and a high surface-to-volume ratio, enabling enhanced energy harvesting. Experimentally, the PENG achieved an open-circuit voltage of 14.59 V, a short-circuit current of 205.7 nA, and a power density of 7.5 mW m⁻² at a resistance of 30 MΩ with a five-layer structure subjected to post-processing under pressure. A theoretical model has mathematically elucidated the output results. Long-term durability (over 345,600 cycles) has confirmed its robustness. Demonstrations of practical applications include monitoring human joint motion and respiratory activity. These results highlight the potential of the proposed triboelectricity-enhanced PENG for vibrational energy harvesting in flexible and wearable electronic systems. Full article

This paper introduces the overall design plan, development timeline, and preliminary progress of the Autonomous Pit Exploration System project. This project aims to develop an advanced multi-robot system for the efficient inspection of nuclear waste-storage tank pits. The project is structured into three phases: Phase 1 involves data collection and interface definition in collaboration with Hanford Site experts and university partners, focusing on tank riser geometry and hardware solutions. Phase 2 includes the selection of sensors and robot components, detailed mechanical design, and prototyping. Phase 3 integrates all components into a cohesive system managed by a master control package which also incorporates digital twin and surrogate models, and culminates in comprehensive testing and validation at a simulated tank pit at the Idaho National Laboratory. Additionally, the system’s communication design ensures coordinated operation through shared data, power, and control signals. For transportation and deployment, an electric vehicle (EV) is chosen to support the system for a full 10 h shift with better regulatory compliance for field deployment. A telescopic arm design is selected for its simple configuration and superior reach capability and controllability. Preliminary testing utilizes an educational robot to demonstrate the feasibility of splitting computational tasks between edge and cloud computers. Successful simultaneous localization and mapping (SLAM) tasks validate our distributed computing approach. More design considerations are also discussed, including radiation hardness assurance, SLAM performance, software transferability, and digital twinning strategies. Full article

Heart failure (HF) remains a major global health challenge defined by the inability of the heart to adequately meet systemic metabolic requirements. While ventricular dysfunction has traditionally been the primary focus in both conceptual and clinical frameworks of HF, emerging evidence highlights atrial myopathy—covering structural, functional, electrical, metabolic, and neurohormonal remodeling—as a central yet often overlooked contributor to disease progression across the HF spectrum. This review offers a comprehensive overview of the molecular and cellular mechanisms underlying atrial remodeling, with a focus on inflammation and innate immune activation as key pathogenic mediators. Among pattern recognition receptors, Toll-like receptors (TLRs) and NOD-like receptors (NLRs) play crucial roles in translating myocardial stress into pro-inflammatory, profibrotic, and pro-arrhythmic signals that exacerbate HF. By combining experimental and clinical evidence, we emphasize atrial myopathy as both a biomarker and an active driver of HF deterioration, advocating for the inclusion of atrial-targeted diagnostics and immunomodulatory therapies in future HF treatment approaches. Such a paradigm shift holds significant potential for improved risk stratification, arrhythmia prevention, attenuation of structural remodeling, and ultimately, better prognosis and clinical outcomes in this increasingly common syndrome. Full article

Chronic pain is a dynamic, brain-wide condition that eludes effective management by conventional, static treatment approaches. Transcutaneous Electrical Nerve Stimulation (TENS), traditionally perceived as a simple and generic modality, is on the verge of a significant transformation. Guided by advances in brain-state decoding and adaptive algorithms, TENS can evolve into a precision neuromodulation system tailored to individual needs. By integrating multimodal neuroimaging—including the spatial resolution of functional magnetic resonance imaging (fMRI), the temporal sensitivity of an Electroencephalogram (EEG), and the ecological validity of functional near-infrared spectroscopy (fNIRS)—with real-time machine learning, we envision a paradigm shift from fixed stimulation protocols to personalized, closed-loop modulation. This comprehensive review outlines a translational framework to reengineer TENS from an open-loop device into a responsive, intelligent therapeutic platform. We examine the underlying neurophysiological mechanisms, artificial intelligence (AI)-driven infrastructures, and ethical considerations essential for implementing this vision in clinical practice—not only for chronic pain management but also for broader neuroadaptive healthcare applications. Full article

The integration of organoids with biosensors serves as a miniaturized model of human physiology and diseases, significantly transforming the research frameworks surrounding drug development, toxicity testing, and personalized medicine. This review aims to provide a comprehensive framework for researchers to identify suitable technical approaches and to promote the advancement of organoid sensing towards enhanced biomimicry and intelligence. To this end, several primary methods for technology integration are systematically outlined and compared, which include microfluidic integrated systems, microelectrode array (MEA)-based electrophysiological recording systems, optical sensing systems, mechanical force sensing technologies, field-effect transistor (FET)-based sensing techniques, biohybrid systems based on synthetic biology tools, and label-free technologies, including impedance, surface plasmon resonance (SPR), and mass spectrometry imaging. Through multimodal collaboration such as the combination of MEA for recording electrical signals from cardiac organoids with micropillar arrays for monitoring contractile force, these technologies can overcome the limitations inherent in singular sensing modalities and enable a comprehensive analysis of the dynamic responses of organoids. Furthermore, this review discusses strategies for integrating strategies of multimodal sensing approaches (e.g., the combination of microfluidics with MEA and optical methods) and highlights future challenges related to sensor implantation in vascularized organoids, signal stability during long-term culture, and the standardization of clinical translation. Full article

Three-dimensional integrated circuit (3D IC) technology is an innovative approach in the semiconductor industry aimed at enhancing performance and reducing power consumption. However, thermal management issues arising from high-density stacking pose significant challenges. Carbon nanotubes (CNTs) have gained attention as a promising material for addressing the thermal management problems of through-silicon vias (TSVs) owing to their unique properties, such as high thermal conductivity, electrical conductivity, excellent mechanical strength, and low coefficient of thermal expansion (CTE). This paper reviews various applications and the latest research results on CNT-based TSVs. Furthermore, it proposes a novel TSV design using CNT–copper–tin composites to optimize the performance and assess the feasibility of CNT-based TSVs. Full article

Electricity demand forecasting is essential for smart grid management, yet it presents challenges due to the dynamic nature of consumption trends and regional variability in usage patterns. While federated learning (FL) offers a privacy-preserving solution for handling sensitive, region-specific data, traditional FL approaches struggle when local datasets are limited, often leading models to overfit noisy peak fluctuations. Additionally, many regions exhibit stable, periodic consumption behaviors, further complicating the need for a global model that can effectively capture diverse patterns without overfitting. To address these issues, we propose Federated Hybrid Graph Attention Network with Two-step Optimization for Electricity Consumption Forecasting (FedHMGAT), a hybrid modeling framework designed to balance periodic trends and numerical variations. Specifically, FedHMGAT leverages a numerical structure graph with a Gaussian encoder to model peak fluctuations as dynamic covariance features, mitigating noise-driven overfitting, while a multi-scale attention mechanism captures periodic consumption patterns through hybrid feature representation. These feature components are then fused to produce robust predictions. To enhance global model aggregation, FedHMGAT employs a two-step parameter aggregation strategy: first, a regularization term ensures parameter similarity across local models during training, and second, adaptive dynamic fusion at the server tailors aggregation weights to regional data characteristics, preventing feature dilution. Experimental results verify that FedHMGAT outperforms conventional FL methods, offering a scalable and privacy-aware solution for electricity demand forecasting. Full article

Graphene, a two-dimensional material with remarkable electrical, thermal, and mechanical properties, has revolutionized the fields of electronics, energy storage, and nanotechnology. This review presents a comprehensive analysis of graphene synthesis techniques, which can be classified into two primary approaches: top-down and bottom-up. Top-down methods, such as mechanical exfoliation, oxidation-reduction, unzipping carbon nanotubes, and liquid-phase exfoliation, are highlighted for their scalability and cost-effectiveness, albeit with challenges in controlling defects and uniformity. In contrast, bottom-up methods, including chemical vapor deposition (CVD), arc discharge, and epitaxial growth on silicon carbide, offer superior structural control and quality but are often constrained by high costs and limited scalability. The interplay between synthesis parameters, material properties, and application requirements is critically examined to provide insights into optimizing graphene production. This review also emphasizes the growing demand for sustainable and environmentally friendly approaches, aligning with the global push for green nanotechnology. By synthesizing current advancements and identifying critical research gaps, this work offers a roadmap for selecting the most suitable synthesis techniques and fostering innovations in scalable and high-quality graphene production. The findings serve as a valuable resource for researchers and industries aiming to harness graphene’s full potential in diverse technological applications. Full article

Current-carrying friction and wear in contact components are key issues in modern electromechanical systems such as slip rings, electrical connectors, motors, and pantographs, directly influencing their efficiency, reliability, and lifespan. Due to the limitations of experimental methods under some extreme conditions, computational simulations have become essential for studying current-carrying friction and wear in such scenarios. This paper presents a comprehensive review of theoretical modeling and numerical simulation methods for current-carrying friction and wear. It begins with discussions of approaches to solve the electrical contact resistance (ECR), a critical parameter that governs current-carrying friction and wear behaviors. Then, it delves into various modeling strategies for current-carrying friction, with an emphasis on the coupled effects of thermal, mechanical, electrical, and magnetic fields. Finally, the review addresses modeling techniques for current-carrying wear, encompassing mechanical wear and arc erosion. By summarizing existing research, this paper identifies key advancements, highlights existing challenges, and outlines future directions, advocating for the development of efficient, universal, and industry-oriented tools that can seamlessly bridge the gap between theoretical modeling and practical applications. Full article

Blue hydrogen is a promising low-carbon alternative to conventional fossil fuels. This technology has been garnering increasing attention with many technological advances in recent years, with a particular focus on the deployed materials and process configurations aimed at minimising the cost and CO₂ emissions intensity of the process as well as maximising efficiency. However, less attention is given to the practical aspects of large-scale deployment, with the cooling requirements often being overlooked, especially across multiple locations. In particular, the literature tends to focus on CO₂ emissions intensity of blue hydrogen production processes, with other environmental impacts such as water and electrical consumption mostly considered an afterthought. Notably, there is a gap to understand the impact of cooling methods on such environmental metrics, especially with technologies at a lower technology readiness level. Herein, two cooling methods (namely, air-cooling versus water-cooling) have been assessed and cross-compared in terms of their energy impact alongside techno-economics, considering deployment across two specific locations (United Kingdom and Saudi Arabia). A sorption-enhanced steam-methane reforming (SE-SMR) coupled with chemical-looping combustion (CLC) was used as the base process. Deployment of this process in the UK yielded a levelised cost of hydrogen (LCOH) of GBP 2.94/kg H₂ with no significant difference between the prices when using air-cooling and water-cooling, despite the air-cooling approach having a higher electricity consumption. In Saudi Arabia, this process achieved a LCOH of GBP 0.70 and GBP 0.72 /kg H₂ when using air- and water-cooling, respectively, highlighting that in particularly arid regions, air-cooling is a viable approach despite its increased electrical consumption. Furthermore, based on the economic and process performance of the SE-SMR-CLC process, the policy mechanisms and financial incentives that can be implemented have been discussed to further highlight what is required from key stakeholders to ensure effective deployment of blue hydrogen production. Full article

The field of electric vehicles and electric mobility, like other modern engineering practice, not only requires deep analytical skills but increasingly demands the ability to synthesise and integrate knowledge across multiple disciplines (e.g., electrical engineering, mechanical engineering, sustainability engineering, design engineering) to create innovative systems. Education today, however, still has a strong analysis focus: learning, exploring, and understanding theories and concepts is the main drive. Design and synthesis build on those and aim at bringing together theories and concepts into creative and innovative systems. Teaching design and synthesis is notoriously hard. The design of electric vehicles exemplifies the complexity of contemporary engineering problems, requiring the integration of multiple domains to experience the challenges connected to design and synthesis. This paper presents the need for, rationale behind, setup of, and experiences with a 5 European Credit (140 h) Master’s-level (postgraduate) course named “Electric Vehicle System Design” that we developed as a joint effort for the University of Twente and the University of South-Eastern Norway. The course is specifically designed to immerse students in the multidisciplinary design and synthesis processes central to electric mobility. In the paper, the course framework, project-based approach, and lessons learned are discussed. This highlights how engineering students can be equipped for the challenges inherent to designing electric vehicles. Full article

To address the increasing need for clean energy and efficient resource utilization, this paper aims to provide a cooperative framework and a fair profit allocation mechanism for integrated photovoltaic (PV) and energy storage systems that are shared among different types of users within a regional alliance, including industrial, commercial, and residential users. A cooperative game model is proposed and formulated by a two-level optimization problem: the upper level determines the optimal PV and storage capacities to maximize the alliance’s net profit, while the lower level allocates profits using an improved Nash bargaining approach based on Shapley value. The model simultaneously incorporates different real-world factors such as time-of-use electricity pricing, system life cycle cost, and load diversity. The results demonstrate that coordination between energy storage systems and PV systems can avoid 18% of solar curtailment losses. Compared to independent deployment by individual users, the cooperative sharing model increases the net present value by 8.41%, highlighting improvements in cost-effectiveness, renewable resource utilization, and operational flexibility. Users with higher demand or better load–generation matching gain greater economic returns, which can provide decision-making guidance for the government in formulating differentiated subsidy policies. Full article

This study conducts a detailed analysis of cybersecurity threats, including artificial intelligence (AI)-driven cyber-attacks targeting vehicle-to-vehicle (V2V) and electric vehicle (EV) communications within the rapidly evolving field of connected and autonomous vehicles (CAVs). As autonomous and electric vehicles become increasingly integrated into daily life, their susceptibility to cyber threats such as replay, jamming, spoofing, and denial-of-service (DoS) attacks necessitates the development of robust cybersecurity measures. Additionally, EV-specific threats, including battery management system (BMS) exploitation and compromised charging interfaces, introduce distinct vulnerabilities requiring specialized attention. This research proposes a comprehensive and integrated cybersecurity framework that rigorously examines current V2V, vehicle-to-everything (V2X), and EV-specific systems through systematic threat assessments, vulnerability analyses, and the deployment of advanced security controls. Unlike previous state-of-the-art approaches, which primarily focus on isolated threats or specific components such as V2V protocols, the proposed framework provides a holistic cybersecurity strategy addressing the entire communication stack, EV subsystems, and incorporates AI-driven threat detection mechanisms. This comprehensive and integrated approach addresses critical gaps found in the existing literature, making it significantly more adaptable and resilient against evolving cyber-attacks. Our framework aligns with industry standards and regulatory requirements, significantly enhancing the security, safety, and reliability of modern transportation systems. By incorporating specialized cryptographic techniques, secure protocols, and continuous monitoring mechanisms, the proposed approach ensures robust protection against sophisticated cyber threats, thereby safeguarding vehicle operations and user privacy. Full article

The development of wave energy converters (WECs) faces several technical challenges, particularly enhancing the capturing efficiency, improving the conversion of mechanical to electric energy, and reducing energy losses in the transmission of electricity to land-based facilities. The present study is an assessment of the interaction between an oscillating buoy-type wave energy converter (WEC) and waves using experimental and numerical methods. A small-scale model was tested in a wave tank to evaluate its energy capturing efficiency, taking wave heights and periods as independent variables. The recorded data were used to validate OpenFOAM (version 9.0) simulations, which provided insights into system response characteristics. The findings highlight the critical role of resonance in optimizing energy capture, with maximum efficiency observed for medium wave periods, and with specific buoy configurations. The study also identified an inverse relationship between the capture width ratio and wave height, suggesting the need for customized buoy designs, tailored to specific sea states. The integrated approach used in this research provides a comprehensive understanding of WEC behaviour and offers valuable insights for advancing wave energy technologies and improving their sustainability and efficiency in diverse marine environments. Full article

Halide perovskite-based memristors are promising neuromorphic devices due to their unique ion migration and interface tunability, yet their conduction mechanisms remain unclear, causing stability and performance issues. Here, we engineer interstitial Ag⁺ ions within a quasi-two-dimensional (quasi-2D) halide perovskite ((C₆H₅C₂H₄NH₃)₂Cs_n−1Pb_nI_3n+1) to enhance device stability and controllability. The introduced Ag⁺ ions occupy organic interlayers, forming thermodynamically stable structures and introducing deep-level energy states without structural distortion, which do not act as non-radiative recombination centers, but instead serve as efficient charge trapping centers that stabilize intermediate resistance states and facilitate controlled filament evolution during resistive switching. This modification also leads to enhanced electron transparency near the Fermi level, contributing to improved charge transport dynamics and device performance. Under external electric fields, these Ag⁺ ions act as mobile ionic species, facilitating controlled filament formation and stable resistive switching. The resulting devices demonstrate exceptional performance, featuring an ultrahigh on/off ratio (∼10⁸) and low operating voltages (∼0.31 V), surpassing existing benchmarks. Our findings highlight the dual role of Ag⁺ ions in structural stabilization and conduction modulation, providing a robust approach for high-performance perovskite memristor engineering. Full article