Search Results (191)

Electrohydrodynamic (EHD) printing offers mask-free, high-resolution deposition across a broad range of ink viscosities, yet combining void-free filling of high-aspect-ratio through-glass vias (TGVs) with ultrafine drop-on-demand (DOD) line printing on the same platform requires balancing conflicting requirements: for example, high field strengths to drive ink into deep and narrow vias; sufficiently high ink viscosity to prevent gravity-induced leakage; and stable meniscus dynamics to avoid satellite droplets and charge accumulation on the glass surface. By coupling electrostatic field analysis with transient level-set simulations, we establish a dimensionless regime map that delineates stable cone-jetting regime; these predictions are validated by high-speed imaging and surface profilometry. Operating within this window, the platform achieves complete, void-free filling of 200 µm × 1.52 mm TGVs and continuous 10 µm-wide traces in a single print pass. Demonstrating its capabilities, we fabricate transparent Ku-band substrate-integrated waveguide antennas on borosilicate glass: the printed vias and arc feed elements exhibit a reflection coefficient minimum of –18 dB at 14.2 GHz, a –10 dB bandwidth of 12.8–16.2 GHz, and an 8 dBi peak gain with 37° beam tilt, closely matching full-wave predictions. This physics-driven, all-in-one EHD approach provides a scalable route to high-performance, glass-integrated RF devices and transparent electronics. Full article

Aluminum nitride (AlN) thin films were deposited on SiO₂ substrates by RF-magnetron sputtering at varying powers (110–140 W) and subsequently subjected to thermal annealing at 450 °C under nitrogen atmosphere. A comprehensive multi-technique investigation—including X-ray reflectometry (XRR), X-ray diffraction (XRD), scanning electron microscopy (SEM), atomic force microscopy (AFM), optical profilometry, spectroscopic ellipsometry (SE), and electrical measurements—was performed to explore the physical structure, morphology, and optical and electrical properties of the films. The analysis of the film structure by XRR revealed that increasing sputtering power resulted in thicker, denser AlN layers, while thermal treatment promoted densification by reducing density gradients but also induced surface roughening and the formation of island-like morphologies. Optical studies confirmed excellent transparency (>80% transmittance in the near-infrared region) and demonstrated the tunability of the refractive index with sputtering power, critical for optoelectronic applications. The electrical characterization of Au/AlN/Al sandwich structures revealed a transition from Ohmic to trap-controlled space charge limited current (SCLC) behavior under forward bias—a transport mechanism frequently present in a material with very low mobility, such as AlN—while Schottky conduction dominated under reverse bias. The systematic correlation between deposition parameters, thermal treatment, and the resulting physical properties offers valuable pathways to engineer AlN thin films for next-generation optoelectronic and high-frequency device applications. Full article

This study examines methods to improve the energy efficiency of radiofrequency inductively coupled plasma (RF-ICP) torches for radioactive waste treatment, with a focus on surpassing the typical energy efficiency limit of approximately 70%. To improve energy efficiency and plasma performance, this research investigates the transition from axial gas flow to vortex gas flow patterns using COMSOL Multiphysics software v6.2. Key plasma parameters, including energy efficiency, number of gas vortices, heat transfer, and temperature distribution, were analyzed to evaluate the improvements. The results indicate that adopting a vortex flow pattern increases energy conversion efficiency, increases heat flux, and reduces charge losses. Furthermore, optimizing the torch body design, particularly the nozzle, chamber volume, and gas entry angle, significantly improves plasma properties and energy efficiency by up to 90%. Improvements to RF-ICP torches positively impact waste decomposition by creating better thermal conditions that support resource recovery and potential material recycling. In addition, these improvements contribute to reducing secondary waste, mitigating environmental risks, and fostering long-term public support for nuclear technology, thereby promoting a more sustainable approach to waste management. Simulation results demonstrate the potential of RF-ICP flares as a cost-effective and sustainable solution for the thermal treatment of low- to intermediate-level radioactive waste. Full article

The ability to predict capacity retention is critical for ensuring the long-term reliability of supercapacitors in energy storage systems. This study presents a comprehensive machine learning framework that integrates both electrical cycling data and experimentally derived material and structural features to forecast the degradation behavior of commercial supercapacitors. A total of seven supercapacitor samples were tested under various current and voltage conditions, resulting in over 70,000 charge–discharge cycles across three case studies. In addition to electrical measurements, detailed physical and material characterizations were performed, including electrode dimension analysis, Scanning Electron Microscopy (SEM), Energy Dispersive X-ray Spectroscopy (EDS), and Thermogravimetric Analysis (TGA). Three machine learning models, Linear Regression (LR), Random Forest (RF), and Multi-Layer Perceptron (MLP), were trained using both cycler-only and combined cycler + material features. Results show that incorporating material features consistently improved prediction accuracy across all models. The MLP model exhibited the highest performance, achieving an R² of 0.976 on the training set and 0.941 on unseen data. Feature importance analysis confirmed that material descriptors such as porosity, thermal stability, and electrode thickness significantly contributed to model performance. This study demonstrates that combining electrical and material data offers a more holistic and physically informed approach to supercapacitor health prediction. The framework developed here provides a practical foundation for accurate and robust lifetime forecasting of commercial energy storage devices, highlighting the critical role of material-level insights in enhancing model generalization and reliability. Full article

The rapid growth in population and industrialization have significantly increased global energy demand, placing immense pressure on finite and environmentally harmful conventional fossil fuel-based energy sources. In this context, the development of hybrid electrocatalysts presents a crucial solution for energy conversion and storage, addressing environmental challenges while meeting rising energy needs. In this study, the fabrication of a novel bifunctional catalyst, copper nickel aluminum spinel (Cu_0.5Ni_0.5Al₂O₄) supported on graphitic carbon nitride (GCN), using a solid-state synthesis process is reported. Because of its effective interface design and spinel cubic structure, the Cu_0.5Ni_0.5Al₂O₄/GCN nanocomposite, as synthesized, performs exceptionally well in electrochemical energy conversion, such as the oxygen evolution reaction (OER), the hydrogen evolution reaction (HER), and energy storage. In particular, compared to noble metals, Pt/C- and IrO₂-based water-splitting cells require higher voltages (1.70 V), while for the Cu_0.5Ni_0.5Al₂O₄/GCN nanocomposite, a voltage of 1.49 V is sufficient to generate a current density of 10 mA cm⁻² in an alkaline solution. When used as supercapacitor electrode materials, Cu_0.5Ni_0.5Al₂O₄/GCN nanocomposites show a specific capacitance of 1290 F g⁻¹ at a current density of 1 A g⁻¹ and maintain a specific capacitance of 609 F g⁻¹ even at a higher current density of 5 A g⁻¹, suggesting exceptional rate performance and charge storage capacity. The electrode’s exceptional capacitive properties were further confirmed through the determination of the roughness factor (Rf), which represents surface heterogeneity and active area enhancement, with a value of 345.5. These distinctive characteristics render the Cu_0.5Ni_0.5Al₂O₄/GCN composite a compelling alternative to fossil fuels in the ongoing quest for a viable replacement. Undoubtedly, the creation of the Cu_0.5Ni_0.5Al₂O₄/GCN composite represents a significant breakthrough in addressing the energy crisis and environmental concerns. Owing to its unique composition and electrocatalytic characteristics, it is considered a feasible choice in the pursuit of ecologically sustainable alternatives to fossil fuels. Full article

This work presents a high-performance novel photodetector based on two-dimensional boron nitride (BN) nanosheets functionalized with gold nanoparticles (Au NPs), offering ultra-broadband photoresponse from 0.25 to 5.9 μm. Operating in both photovoltaic and photoconductive modes, the device features rapid response times (<0.5 ms), high responsivity (up to 1015 mA/W at 250 nm and 2.5 V bias), and thermal stability up to 100 °C. The synthesis process involved CO₂ laser exfoliation of hexagonal boron nitride, followed by gold NP deposition via RF sputtering and thermal annealing. Structural and compositional analyses confirmed the formation of a three-dimensional network of atomically thin BN nanosheets decorated with uniformly distributed gold nanoparticles. This architecture facilitates plasmon-enhanced absorption and efficient charge separation via heterojunction interfaces, significantly boosting photocurrent generation across the deep ultraviolet (DUV), visible, near-infrared (NIR), and mid-infrared (MIR) spectral regions. First-principles calculations support the observed broadband response, confirming bandgap narrowing induced by defects in h-BN and functionalization by gold nanoparticles. The device’s self-driven operation, wide spectral response, and durability under elevated temperatures underscore its strong potential for next-generation broadband, self-powered, and wearable sensing and optoelectronic applications. Full article

High-current electron beams can significantly enhance the productivity of variety of applications including medical radioisotope (RI) production and wastewater purification. High-power superconducting radio frequency (SRF) linacs are capable of producing such high-current electron beams due to the key advantage to operate in continuous wave (CW) mode. However, this requires an injector capable of generating electron bunches with high repetition rate and in CW mode, while minimizing beam losses to avoid damage to SRF cavities due to quenching. RF gating to the grid of a thermionic electron gun is a promising solution, as it ensures CW bunch generation at the repetition rate same as the fundamental or sub-harmonics of the accelerating RF frequency, with minimal beam loss. This paper presents detailed beam dynamics simulations demonstrating that an RF-gated gun operating at 1.3 GHz can generate bunches with 148 ps full width with 8.96 pC charge. Full article

This study presents a machine learning-based photovoltaic (PV) model for energy management and planning in a microgrid with a battery system. Microgrids integrating PV face challenges such as solar irradiance variability, temperature fluctuations, and intermittent generation, which impact grid stability and battery storage efficiency. Existing models often lack predictive accuracy, computational efficiency, and adaptability to changing environmental conditions. To address these limitations, the proposed model integrates an Adaptive Neuro-Fuzzy Inference System (ANFIS) with a multi-input multi-output (MIMO) prediction algorithm, utilizing historical temperature and irradiance data for accurate and efficient forecasting. Simulation results demonstrate high prediction accuracies of 95.10% for temperature and 98.06% for irradiance on dataset-1, significantly reducing computational demands and outperforming conventional prediction techniques. The model further uses ANFIS outputs to estimate PV generation and optimize battery state of charge (SoC), achieving a consistent minimal SoC reduction of about 0.88% (from 80% to 79.12%) over four different battery types over a seven-day charge–discharge cycle, providing up to 11 h of battery autonomy under specified load conditions. Further validation with four other distinct datasets confirms the ANFIS network’s robustness and superior ability to handle complex data variations with consistent accuracy, making it a valuable tool for improving microgrid stability, energy storage utilization, and overall system reliability. Overall, ANFIS outperforms other models (like curve fittings, ANN, Stacked-LSTM, RF, XGBoost, GBoostM, Ensemble, LGBoost, CatBoost, CNN-LSTM, and MOSMA-SVM) with an average accuracy of 98.65%, and a 0.45 RMSE value on temperature predictions, while maintaining 98.18% accuracy, and a 31.98 RMSE value on irradiance predictions across all five datasets. The lowest average computational time of 17.99s was achieved with the ANFIS model across all the datasets compared to other models. Full article

This paper proposes a hybrid energy-harvesting chip that utilizes both radio-frequency (RF) energy and solar energy for low-power applications and extended service life. The key contributions include a wide input power range, a compact chip area, and a high maximum power conversion efficiency (PCE). Solar energy is a clean and readily available source. The hybrid energy harvesting system has gained popularity by combining RF and solar energy to improve overall energy availability and efficiency. The proposed chip comprises a matching network, rectifier, charge pump, DC combiner, overvoltage protection circuit, and low-dropout voltage regulator (LDO). The matching network ensures maximum power delivery from the antenna to the rectifier. The rectifier circuit utilizes a cross-coupled differential drive rectifier to convert radio frequency energy into DC voltage, incorporating boosting functionality. In addition, a solar harvester is employed to provide an additional energy source to extend service time and stabilize the output by combining it with the radio-frequency source using a DC combiner. The overvoltage protection circuit safeguards against high voltage passing from the DC combiner to the LDO. Finally, the LDO facilitates the production of a stable output voltage. The entire circuit is simulated using the Taiwan Semiconductor Manufacturing Company 0.18 µm 1P6M complementary metal–oxide–semiconductor standard process developed by the Taiwan Semiconductor Research Institute. The simulation results indicated a rectifier conversion efficiency of approximately 41.6% for the proposed radio-frequency-energy-harvesting system. It can operate with power levels ranging from −1 to 20 dBm, and the rectifier circuit’s output voltage is within the range of 1.7–1.8 V. A 0.2 W monocrystalline silicon solar panel (70 × 30 mm²) was used to generate a supplied voltage of 1 V. The overvoltage protection circuit limited the output voltage to 3.6 V. Finally, the LDO yielded a stable output voltage of 3.3 V. Full article

BiFeO₃ is a fascinating material with a rhombohedral crystal structure (R3c) at room temperature. This unique structure makes it suitable for use in solar cells, as the interaction of light with the polarized octahedral enhances electron movement. Evaluating its properties on different substrates helps to identify the specific characteristics of thin films. The thin films presented in this work were deposited using reactive RF cathodic sputtering with a homemade 1-inch diameter ceramic target. Their morphology, phase composition, optical, piezoelectric, and ferroelectric properties were evaluated. Fluorine Tin Oxide (FTO) and Indium Tin Oxide (ITO) substrates were used for the presented thin films. The thin films deposited on FTO displayed the “butterfly” behavior typically associated with ferroelectric materials. A d₃₃ value of 2.71 nm/V was determined using SSPFM-DART mode. In contrast, the thin films deposited on ITO at 550 °C reached a maximum saturation polarization of 40.89 μC/cm² and a remnant polarization of 44.87 μC/cm², which are the highest values recorded, but did not present the typical “butterfly” behavior. As the grain size increased, the influence of charge defects became more pronounced, leading to an increase in the leakage current. Furthermore, the presence of secondary phases also contributed to this behavior. Full article

The study explores the optical and transport properties of polycrystalline ZnO thin films prepared using reactive pulsed mid-frequency sputtering with RF electron cyclotron wave resonance (ECWR) plasma. This deposition method increases the ionization degree of sputtered particles, the dissociation of reactive gas and the plasma density of pulsed reactive magnetron plasma. Optical absorption spectra reveal a sharp Urbach edge, indicating low valence band disorder. Lattice disorder and deep defect concentration are more likely to occur in samples with higher roughness. PL analysis at low temperature reveals in all samples a relatively slow (μs) red emission band related to deep bulk defects. The fast (sub-ns), surface-related blue PL band was observed in some samples. Blue PL disappeared after annealing in air at 500 °C. Room temperature Hall effect measurements confirm n-type conductivity, though with relatively low mobility, suggesting defect-related scattering. Persistent photoconductivity was observed under UV illumination, indicating deep trap states affecting charge transport. These results highlight the impact of deposition and post-treatment on polycrystalline ZnO thin films, offering insights into optimizing their performance for optoelectronic applications, such as UV detectors and transparent conductive oxides. Full article

Hydrogen production via water splitting is a crucial strategy for addressing the global energy crisis and promoting sustainable energy solutions. This review systematically examines water-splitting mechanisms, with a focus on photocatalytic and electrochemical methods. It provides in-depth discussions on charge transfer, reaction kinetics, and key processes such as the oxygen evolution reaction (OER) and hydrogen evolution reaction (HER). Various electrode synthesis techniques, including hydrothermal methods, chemical vapor deposition (CVD), pulsed laser deposition (PLD), and radio frequency sputtering (RF), are reviewed for their advantages and limitations. The role of carbon-based materials such as graphene, biochar, and graphitic carbon nitride (g-C₃N₄) in photocatalytic and photoelectrochemical (PEC) water splitting is also highlighted. Their exceptional conductivity, tunable band structures, and surface functionalities contribute to efficient charge separation and enhanced light absorption. Further, advancements in heterojunctions, doped systems, and hybrid composites are explored for their ability to improve photocatalytic and PEC performance by minimizing charge recombination, optimizing electronic structures, and increasing active sites for hydrogen and oxygen evolution reactions. Key challenges, including material stability, cost, scalability, and solar spectrum utilization, are critically analyzed, along with emerging strategies such as novel synthesis approaches and sustainable material development. By integrating water splitting mechanisms, electrode synthesis techniques, and advancements in carbon-based materials, this review provides a comprehensive perspective on sustainable hydrogen production, bridging previously isolated research domains. Full article

Radio-frequency (RF) microwave wireless power transfer (WPT) offers an efficient means of delivering energy to a wide array of devices over long distances. Previous RF WPT systems faced significant challenges, including complex hardware and control systems, software deficiencies, insufficient rectification power, lack of high-performance substrate materials, and electromagnetic radiation hazards. Addressing these issues, this paper proposes the world’s first watt-level RF WPT system capable of intelligent continuous tracking and occlusion judgment. Our 5.8 GHz band RF WPT system integrates several advanced technologies, such as millimeter-precision lidar, the multi-object image recognition algorithm, the accurate 6-bit continuous beamforming algorithm, a compact 16-channel 32 W high-power transmitting system, a pair of ultra-low axial ratio circularly polarized antenna arrays, ultra-low-loss high-strength ceramic substrates, and a 2.4 W high-power Schottky diode array rectifier achieving a rectification efficiency of $66.8\%$. Additionally, we construct a platform to demonstrate the application of the proposed RF WPT system in battery-free vehicles, achieving unprecedented ${360}^{\circ }$ uninterrupted power supply to the battery-free vehicle. In summary, this system represents the most functionally complete RF WPT system to date, serving as a milestone for several critical fields such as smart living, transportation electrification, and battery-less/free societies. Full article

The advent of CMOS power amplifiers has enabled compact and cost-effective solutions for RF applications. Among the available options, switching amplifiers are the most competitive due to their superior efficiency. In this paper, we present the design of a fully integrated 130 nm CMOS class-E RF power amplifier optimized for 2.4 GHz ISM band operations that is compliant with the Bluetooth Low Energy (BLE) standard. The amplifier is based on a cascode configuration with charging acceleration capacitance and a combination of standard and high-voltage (HV) MOSFETs, ensuring optimal performance while maintaining device reliability. To identify the best configuration for the proposed circuit, we first provide an overview of basic class-E amplifier operations and critically review optimization techniques proposed in the scientific literature. This review is complemented by a numerical analysis of the potential advantages of using a combined standard-HV MOSFET structure. Post-layout simulations with parasitic parameter extraction demonstrated that the amplifier achieves 40.85% Power Added Efficiency and 20.52 dBm output power. Full article

This research presents an intelligent, environment-aware navigation framework for smart electric vehicles (EVs), focusing on multi-agent mapping, real-time obstacle recognition, and adaptive route optimization. Unlike traditional navigation systems that primarily minimize cost and distance, this research emphasizes how EVs perceive, map, and interact with their surroundings. Using a distributed mapping approach, multiple EVs collaboratively construct a topological representation of their environment, enhancing spatial awareness and adaptive path planning. Neural Radiance Fields (NeRFs) and machine learning models are employed to improve situational awareness, reduce positional tracking errors, and increase mapping accuracy by integrating real-time traffic conditions, battery levels, and environmental constraints. The system intelligently balances delivery speed and energy efficiency by dynamically adjusting routes based on urgency, congestion, and battery constraints. When rapid deliveries are required, the algorithm prioritizes faster routes, whereas, for flexible schedules, it optimizes energy conservation. This dynamic decision making ensures optimal fleet performance by minimizing energy waste and reducing emissions. The framework further enhances sustainability by integrating an adaptive optimization model that continuously refines EV paths in response to real-time changes in traffic flow and charging station availability. By seamlessly combining real-time route adaptation with energy-efficient decision making, the proposed system supports scalable and sustainable EV fleet operations. The ability to dynamically optimize travel paths ensures minimal energy consumption while maintaining high operational efficiency. Experimental validation confirms that this approach not only improves EV navigation and obstacle avoidance but also significantly contributes to reducing emissions and enhancing the long-term viability of smart EV fleets in rapidly changing environments. Full article