Search Results (3,999)

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

This paper presents the preparation of Z-cut near-stoichiometric lithium niobate (NSLN) wafers using a combined process of the lithium-rich Czochralski growth and diffusion methods. The fabricated Z-cut NSLN wafers exhibited outstanding comprehensive performance, including a high Curie temperature of up to 1200 °C, a refractive index gradient in the diameter direction below 1.5 × 10⁻⁴ cm⁻¹, and a UV absorption edge shifted 14 nm toward the ultraviolet region compared to congruent lithium niobate crystals, with a coercive field of 1268 V/mm. Additionally, the wafers demonstrated excellent processing characteristics, with the bow of 4-inch wafers controlled within 55 μm, surpassing the machining standards of traditional lithium niobate wafers of the same size. These results indicated the highly uniform chemical stoichiometry and crystallization quality of the wafers. Leveraging the high uniformity and low coercive field of the wafers, periodic triangular domain structure arrays were successfully fabricated, laying the foundation for domain engineering design in electro-optic deflectors and switching devices. This study not only achieves the scalable preparation of NSLN wafers but also provides a reliable technical solution for their practical applications in high-performance electro-optic devices. Full article

Aiming at the ice coating problem on medium-voltage overhead lines, this paper proposes a resonance de-icing device based on an improved YOLOv7 algorithm to achieve efficient and intelligent ice detection and removal. First, by introducing the SimAM three-dimensional attention mechanism to optimize feature extraction capability, combining the MPDIoU loss function to enhance bounding box regression accuracy, and designing a task-specific context decoupling head to separate classification and regression tasks, the ice detection accuracy and real-time performance are significantly improved. Second, an integrated ice observation/de-icing device is developed, which incorporates the improved YOLOv7 visual detection algorithm and a resonance vibration module. Through a dynamic frequency optimization strategy, precise matching between the excitation frequency and the inherent frequency of the conductor is achieved. The findings from the engineering experiments demonstrate that the de-icing apparatus conceptualized in this study is capable of effectively identifying the condition of ice-covered conductors and de-icing them. This research presents a novel technical solution for the intelligent de-icing of overhead lines, which holds significant value for engineering applications. Full article

Conductive composites are a flexible class of engineered materials that combine conductive fillers with an insulating matrix—usually made of ceramic, polymeric, or a hybrid material—to customize a system’s electrical performance. By providing tunable electrical properties in addition to benefits like low density, mechanical flexibility, and processability, these materials are intended to fill the gap between conventional insulators and conductors. The increasing need for advanced technologies, such as energy storage devices, sensors, flexible electronics, and biomedical interfaces, has significantly accelerated their development. The electrical characteristics of composite materials, including metallic, ceramic, polymeric, and nanostructured systems, are thoroughly examined in this review. The impact of various reinforcement phases—such as ceramic fillers, carbon-based nanomaterials, and metallic nanoparticles—on the electrical conductivity and dielectric behavior of composites is highlighted. In addition to conduction models like correlated barrier hopping and Debye relaxation, the study investigates mechanisms like percolation thresholds, interfacial polarization, and electron/hole mobility. Because of the creation of conductive pathways and improved charge transport, developments in nanocomposite engineering, especially with regard to graphene derivatives and silver nanoparticles, have shown notable improvements in electrical performance. This work covers the theoretical underpinnings and physical principles of conductivity and permittivity in composites, as well as experimental approaches, characterization methods (such as SEM, AFM, and impedance spectroscopy), and real-world applications in fields like biomedical devices, sensors, energy storage, and electronics. This review provides important insights for researchers who want to create and modify multifunctional composite materials with improved electrical properties by bridging basic theory with technological applications. Full article

Perovskite/Si tandem solar cells (PSTSCs) have emerged as a leading candidate for surpassing the Shockley–Queisser (SQ) efficiency limit inherent to single-junction silicon solar cells. Following their inaugural demonstration in 2015, perovskite/Si tandem solar cells have experienced remarkable technological progression, reaching a certified power conversion efficiency of 34.9% by 2025. To elucidate pathways for realizing the full potential of perovskite/Si tandem solar cells, this review commences with an examination of fundamental operational mechanisms in multi-junction photovoltaic architectures. Subsequent sections systematically analyze technological breakthroughs across three critical PSTSC components organized by an optical path sequence: (1) innovations in perovskite photoactive layers through component engineering, additive optimization, and interfacial modification strategies; (2) developments in charge transport and recombination management via advanced interconnecting layers; and (3) silicon subcell architectures. The review concludes with a critical analysis of persistent challenges in device stability, scalability, structural optimization and fabrication method, proposing strategic research directions to accelerate the transition from laboratory-scale achievements to commercially viable photovoltaic solutions. Full article

Carbohydrate-based hydrogels represent a new advancement in the development of multifunctional biomedical systems, thanks to their intrinsic biocompatibility, structural versatility, and capacity for functional modification. This review examines the latest progress made in employing these materials as intelligent theranostic platforms, with a particular focus on their role as biosensors and therapeutic drug delivery devices. Engineered to interact dynamically with the biological environment, carbohydrate hydrogels enable the site-specific release of therapeutic agents while simultaneously supporting the monitoring of key physiological markers. Their dual functionality offers significant advantages in managing complex pathologies such as cancer, metabolic disorders, and chronic inflammation, where personalized treatment and real-time feedback are essential. By exploring their biological application, this review underscores the pivotal role played by carbohydrate hydrogels in advanced therapeutic technologies. Full article

Nanophotonics, the study of light–matter interactions at the nanometer scale, has emerged as a transformative field that bridges photonics and nanotechnology. Using engineered nanomaterials—including plasmonic metals, high-index dielectrics, two-dimensional (2D) materials, and hybrid systems—nanophotonics enables light manipulation beyond the diffraction limit, unlocking novel applications in sensing, imaging, and quantum technologies. This review provides a comprehensive overview of recent advances (post-2020) in nanophotonic materials, fabrication methods, and their cutting-edge applications. We first discuss the fundamental principles governing nanophotonic phenomena, such as localized surface plasmon resonances (LSPRs), Mie resonances, and exciton–polariton coupling, highlighting their roles in enhancing light–matter interactions. Next, we examine state-of-the-art fabrication techniques, including top-down (e.g., electron beam lithography and nanoimprinting) and bottom-up (e.g., chemical vapor deposition and colloidal synthesis) approaches, as well as hybrid strategies that combine scalability with nanoscale precision. We then explore emerging applications across diverse domains: quantum photonics (single-photon sources, entangled light generation), biosensing (ultrasensitive detection of viruses and biomarkers), nonlinear optics (high-harmonic generation and wave mixing), and integrated photonic circuits. Special attention is given to active and tunable nanophotonic systems, such as reconfigurable metasurfaces and hybrid graphene–dielectric devices. Despite rapid progress, challenges remain, including optical losses, thermal management, and scalable integration. We conclude by outlining future directions, such as machine learning-assisted design, programmable photonics, and quantum-enhanced sensing, and offering insights into the next generation of nanophotonic technologies. This review serves as a timely resource for researchers in photonics, materials science, and nanotechnology. Full article

Organic photodetectors (OPDs) offer considerable promise for low-power, solution-processable biosensing and imaging applications; however, their performance remains limited by spectral mismatch and interfacial trap states. In this study, a highly sensitive polymer photodiode was developed via trace incorporation (0.8 wt%) of InP/ZnSe/ZnS quantum dots (QDs) into a PTB7-Th:PC₇₁BM bulk heterojunction (BHJ) matrix. This QD doping approach enhanced the external quantum efficiency (EQE) across the 540–660 nm range and suppressed the dark current density at −2 V by passivating interface trap states. Despite a slight decrease in optical absorption at the optimized composition, the internal quantum efficiency (IQE) increased significantly from ~80% to nearly 95% resulting in a net EQE improvement. This suggests that QD incorporation improved charge transport without compromising charge separation efficiency. As a result, the device achieved a specific detectivity (D*) of 1.8 × 10¹³ Jones, representing a 93% improvement over binary BHJs, along with an ultra-low dark current density of 7.76 × 10⁻¹⁰ A/cm². Excessive QD loading, however, led to optical losses and increased dark current, underscoring the need for precise compositional control. Furthermore, the enhanced detectivity led to a 4 dB improvement in the signal-to-noise ratio (SNR) of photoplethysmography (PPG) signals in the target wavelength range, enabling more reliable biophotonic sensing without increased power consumption. This work demonstrates that QD-based spectral and interfacial engineering offers an effective and scalable route for advancing the performance of OPDs, with broad applicability to low-power biosensors and high-resolution polymer–QD imaging systems. Full article

Seismic isolation and energy dissipation systems are essential technologies for enhancing the resilience and sustainability of buildings and infrastructure exposed to earthquake-induced ground motions. By reducing structural damage, protecting non-structural components, and ensuring post-earthquake functionality, these systems contribute to minimizing economic loss, preserving human life, and supporting long-term community resilience. This review focuses exclusively on passive control systems, such as base isolators and damping devices, commonly codified and implemented in current engineering practice. A comprehensive analysis of international design codes and performance-based practices is presented, highlighting the role of these systems in promoting sustainable infrastructure through risk mitigation and extended service life. The study identifies critical gaps in global standards and testing protocols, advocating for harmonized and forward-looking approaches. The findings aim to inform seismic design strategies that align with the principles of environmental, economic, and social sustainability. Full article

Anodised metal matrices represent a versatile and multifunctional platform for the development of advanced materials with tunable physicochemical properties. Through electrochemical oxidation processes—commonly referred to as anodisation—metals such as aluminium, titanium, niobium, zinc and tantalum can be transformed into structured oxide layers with defined porosity, thickness and surface morphology. These methods enable the fabrication of ordered nanoporous arrays, nanotubes and nanowires, depending on the process parameters and the type of metal. The review introduces and outlines the various anodisation techniques and parameters. This is crucial, since each individual metal requires specified optimal conditions to obtain a stable anodised oxide layer. This review provides an overview of recent advances in the design and application of anodised metal substrates, with the focus on their role as functional platforms in catalysis, sensing, energy storage and biomedical engineering. Special attention is given to post-anodisation surface modification strategies, such as chemical functionalisation, thin-film deposition and molecular-level integration, which significantly expand the utility of these materials. The review also highlights the challenges, limitations and future perspectives of anodising technologies, aiming to guide the rational design of next-generation devices based on engineered oxide architectures. Full article

As micro–nano power devices have evolved towards high frequency, high voltage, and a high level of integration, the issue of thermal resistance at heterointerfaces has become increasingly prominent, posing a key bottleneck that limits device performance and reliability. This paper presents a systematic review of the current state of research and future challenges related to interface thermal resistance in heterostructures within micro and nano power devices. First, based on phonon transport theory, we conducted an in-depth analysis of the heat transfer mechanisms at typical heterointerfaces, such as metal–semiconductor and semiconductor–semiconductor, and novel low-dimensional materials. Secondly, a comprehensive review of current interface thermal resistance characterization techniques is provided, including the application and limitations of advanced methods such as time domain thermal reflection and Raman thermal measurement in micro- and nano-scale thermal characterization. Finally, in response to the application requirements of semiconductor power devices, future research directions such as atomic-level interface engineering, machine learning-assisted material design, and multi-physics field collaborative optimization are proposed to provide new insights for overcoming the thermal management bottlenecks of micro–nano power devices. Full article

In the age of digital transformation, where students increasingly rely on technology for learning and communication, concerns arise regarding its potential association with academic outcomes. This study quantifies the relationship between Digital Behaviour (DB) and Academic Behaviour (AB) among engineering undergraduates at Misurata, Al-Asmarya Islamic, and Al-Marqab universities in Libya. DB is conceptualised as a spectrum ranging from excessive, compulsive device use (addictive behaviour) to purposeful academic technology use (digital immersion). Using a descriptive-analytical design, a convenience sample of 300 undergraduate engineering students completed a validated 20-item questionnaire (Cronbach’s α = 0.711–0.899 for subscales). Data were analysed using descriptive statistics, Pearson correlation, simple regression, and analysis of variance (ANOVA) in the Statistical Package for the Social Sciences (SPSS) v23. The analysis identified a weak but statistically significant positive correlation between students’ DB and their AB (r = +0.19, p = 0.002). Notably, AB scores increased in the senior study years, while digital engagement remained consistently high across all years, suggesting an evolving capacity among students to regulate their digital habits. ANOVA results revealed significant differences in AB by year of study, while gender showed no significant overall association. These findings contradict the conventional assumption that heavy digital use uniformly diminishes academic outcomes; instead, in digitally immersed learning environments, strategic DB may coexist with or support academic performance. The study concludes that DB is not inherently detrimental to AB and may provide benefits when managed effectively, especially among more advanced engineering students. It recommends early educational interventions that promote digital self-regulation and the strategic use of technology for academic purposes. Full article

This review outlines the technological principles of neural–computer interface (NCI) construction, classifying them according to: (1) the degree of intervention (invasive, semi-invasive, and non-invasive); (2) the direction of signal communication, including BCI (brain–computer interface) for converting neural activity into commands for external devices, CBI (computer–brain interface) for translating artificial signals into stimuli for the CNS, and BBI (brain–brain interface) for direct brain-to-brain interaction systems that account for agency; and (3) the mode of user interaction with technology (active, reactive, passive). For each NCI type, we detail the fundamental data processing principles, covering signal registration, digitization, preprocessing, classification, encoding, command execution, and stimulation, alongside engineering implementations ranging from EEG/MEG to intracortical implants and from transcranial magnetic stimulation (TMS) to intracortical microstimulation (ICMS). We also review mathematical modeling methods for NCIs, focusing on optimizing the extraction of informative features from neural signals—decoding for BCI and encoding for CBI—followed by a discussion of quasi-real-time operation and the use of DSP and neuromorphic chips. Quantitative metrics and rehabilitation measures for evaluating NCI system effectiveness are considered. Finally, we highlight promising future research directions, such as the development of electrochemical interfaces, biomimetic hierarchical systems, and energy-efficient technologies capable of expanding brain functionality. Full article

Magnetic gears are becoming promising devices that can replace conventional mechanical gears in several applications, where reduced maintenance, absence of lubrication and intrinsic overload protection are especially relevant. This paper focuses on the experimental analysis of a coaxial magnetic gear prototype recently developed at the Department of Industrial Engineering of the University of Padova. It is found that its efficiency is high and aligned with prototypes in the literature, its stationary response confirms the velocity ratio of the corresponding mechanical planetary gear, the overload protection is aligned with numerical prediction, while the dynamic response highlights that the intrinsic compliance of the magnetic coupling prevents the use of such device in high-frequency transients. It is concluded that the proposed architecture can be effectively employed for speed reducers applications where low-frequency modulation is sufficient, which includes many industrial applications. Nevertheless, high rotational speeds are allowed. The performance characteristics, although specific for the prototype considered, experimentally highlights the key features of coaxial magnetic gear devices. The experimental performance are also compared with estimations from the literature, when available. Full article

This study addresses the shortcomings of traditional exercise volume assessment methods in dynamic modeling and individual adaptation by proposing a multi-modal data fusion framework based on a spatio-temporal attention-enhanced LSTM neural network for personalized exercise volume optimization in college basketball courses. By integrating physiological signals (heart rate), kinematic parameters (triaxial acceleration, step count), and environmental data collected from smart wearable devices, we constructed a dynamic weighted fusion mechanism and a personalized correction engine, establishing an evaluation model incorporating BMI correction factors and fitness-level compensation. Experimental data from 100 collegiate basketball trainees (60 males, 40 females; BMI 17.5–28.7) wearing Polar H10 and Xsens MVN devices were analyzed through an 8-week longitudinal study design. The framework integrates physiological monitoring (HR, HRV), kinematic analysis (3D acceleration at 100 Hz), and environmental sensing (SHT35 sensor). Experimental results demonstrate the following: (1) the LSTM-attention model achieves 85.3% accuracy in exercise intensity classification, outperforming traditional methods by 13.2%, with its spatio-temporal attention mechanism effectively capturing high-dynamic movement features such as basketball sudden stops and directional changes; (2) multi-modal data fusion reduces assessment errors by 15.2%, confirming the complementary value of heart rate and acceleration data; (3) the personalized correction mechanism significantly improves evaluation precision for overweight students (error reduction of 13.6%) and beginners (recognition rate increase of 18.5%). System implementation enhances exercise goal completion rates by 10.3% and increases moderate-to-vigorous training duration by 14.7%, providing a closed-loop “assessment-correction-feedback” solution for intelligent sports education. The research contributes methodological innovations in personalized modeling for exercise science and multi-modal time-series data processing. Full article