Stable Power Supply Research Articles

Fixed/Mobile Collaborative Traffic Flow Detection Study Based on Wireless Charging of UAVs

Accurate traffic flow detection plays a critical role in intelligent traffic control systems. However, conventional fixed video detection devices often face challenges such as occlusion and overlap in high-density traffic scenarios, which leads to distortions in vehicle detection. To address this issue, it is essential to obtain precise vehicle data as a reliable reference for managing traffic flow during peak periods. In this paper, we propose an intelligent detection scheme using an improved YOLOv8n target recognition algorithm combined with a ByteTrack multi-target tracking algorithm. A collaborative unmanned aerial vehicle (UAV) collaborative detection framework is also established, integrating UAVs and fixed detection devices to work in tandem. Such a multi-UAV collaborative data acquiring system is designed for efficient, continuous, and uninterrupted operation, employing a three-drone rotational detection strategy. UAVs offer additional flexibility and coverage in obtaining vehicle data. However, limited power could be an essential challenge to the system’s wireless physical link stability and safety. To overcome power limitations during UAV collaboration, a wireless charging (WC) system is introduced, enabling automatic constant current–constant voltage (CC-CV) switching and preventing damage from accidental data link disabling. This collaborative traffic data acquiring and transmission system ensures a stable power supply for UAVs during high-density traffic periods, supporting their reliable UAV collaborative wireless data link. Experimental results show that the collaborative detection architecture combined with wireless charging can achieve high detection accuracy, with the recognition accuracy remaining between 0.95 and 0.99.

A Novel Power Quality Management Method of MGC Based on Distributed Consensus Algorithm

ABSTRACTA novel power quality management method of microgrid cluster based on distributed consensus algorithm is proposed in this paper. First of all, when power quality problems arise, converters with suitable positions and capacity can be selected for compensation by using the distributed consensus algorithm proposed in this paper, so as to realize power quality management of microgrid cluster. Further, when a converter in microgrid cluster cannot continue to participate in the task, other converters can be flexibly switched through the distributed consensus algorithm to ensure the continuity and stability of power supply and power quality management. Finally, the remaining capacity of each converter can be adjusted in real time through the distributed consensus algorithm proposed in this paper, which reduces the negative influence of the uneven capacity distribution caused by converter switching on the power quality in microgrid cluster. The results show that multiple converters of microgrid cluster can be effectively coordinated by adopting the power quality management method based on distributed consensus algorithm proposed in this paper, so that the comprehensive power quality management of microgrid cluster can be optimized.

Optimal turning of a 2-DOF proportional-integral-derivative controller based on a chess algorithm for load frequency control

Load frequency control is necessary for power system management. The power system must maintain a frequency range to ensure power supply stability. System faults and demand fluctuations may cause frequencies to change quickly. System stability and integrity suffer. We are optimizing the two-degree-of-freedom (2-DOF) proportional-integral-derivative (PID) controllers chess algorithm. This article addresses electrical load frequency regulation. We employ classical control theory and current adjustment. It aims for electrical system efficiency and dependability. It checks for errors using integral absolute error (IAE), integral squared error (ISE), integral of time multiply absolute error (ITAE), and integral time squared error (ITSE). Particle swarm algorithm (PSO) compares performance. The IAE of 0.03364, nearly identical to it, shows that chess trumps other algorithms in many scenarios. The chess algorithm's ISE was 0.00035, like PSO's 0.03363. The ISE was 0.00036, indicating PSO's error-reduction capabilities. For the chess algorithm, PSO is 0.07929, and ITAE is 0.07647. This indicates the PSO responds faster to system breakdowns and load changes. Finally, the chess algorithm's ITSE is 0.00072, below the PSO 0.00076. The chess algorithm is better at managing long-term load frequency.

Application of Active Components such as DC Generators and Operational Amplifiers in Mechanical Design

DC generators and operational amplifiers have become increasingly important as the need for automation and precise control in mechanical engineering continues to grow. Since their invention in the 19th century, DC generators have played an important role in power systems, especially in applications that require a stable DC power supply. Meanwhile, since the 1960s, operational amplifiers have played an important role in analog circuits, and their applications cover a wide range of fields such as audio processing, signal conditioning, data acquisition and control systems. This paper explores the multiple applications of DC motors and operational amplifiers in mechanical systems and their integration through a literature review approach. It is found that these components not only improve the efficiency and performance of mechanical systems, but also provide higher control accuracy in areas such as robotics and industrial automation. In addition, with the continuous advancement of technology, the combination of the two is expected to realize innovations and breakthroughs in more fields. The research in this paper provides an important reference for the future innovation and development of mechanical engineering

"Bridge" interface design modulates high-performance cellulose-based integrated flexible supercapacitors.

Flexible supercapacitors offer significant potential for powering next-generation flexible electronics. However, the mechanical and electrochemical stability of flexible supercapacitors under different flexibility conditions is limited by the weak bonding between neighboring layers, posing a major obstacle to their practical application. In this paper, natural coniferous pulp cellulose was successfully modified with ethylenediamine and NiSe2/Cell-NH2/MoS2 cellulose flexible electrodes (NCMF) were fabricated by phase transfer and hydrothermal methods. The amino-modified cellulose (Cell-NH2) acts as a "bridge" between NiSe2 and MoS2, significantly enhancing the interfacial bonding strength of the flexible electrode. The integrated flexible electrode exhibited a high area capacitance (2475 mF/cm2), strong tensile strength (10.3 MPa), and excellent cycling stability (92.1 % capacitance retention after 2500 cycles). The sandwich-structured monolithic supercapacitor achieved both a high electrode capacitance (56.78 F/cm2) and a high energy density (1971.53 μWh/cm2), while maintaining long cycling life (72.73 % capacitance retention after 2000 cycles) and large deformation capability. This makes it suitable for stable power supplies in electronic products and opens new possibilities for the flexible wearable industry. This technology enables stable power supplies for electronic products and paves the way for advancements in the flexible wearable industry.

Picometre-level surface control of a closed-loop, adaptive X-ray mirror with integrated real-time interferometric feedback.

We provide a technical description and experimental results of the practical development and offline testing of an innovative, closed-loop, adaptive mirror system capable of making rapid, precise and ultra-stable changes in the size and shape of reflected X-ray beams generated at synchrotron light and free-electron laser facilities. The optical surface of a piezoelectric bimorph deformable mirror is continuously monitored at 20 kHz by an array of interferometric sensors. This matrix of height data is autonomously converted into voltage commands that are sent at 1 Hz to the piezo actuators to modify the shape of the mirror optical surface. Hence, users can rapidly switch in closed-loop between pre-calibrated X-ray wavefronts by selecting the corresponding freeform optical profile. This closed-loop monitoring is shown to repeatably bend and stabilize the low- and mid-spatial frequency components of the mirror surface to any given profile with an error <200 pm peak-to-valley, regardless of the recent history of bending and hysteresis. Without closed-loop stabilization after bending, the mirror height profile is shown to drift by hundreds of nanometres, which will slowly distort the X-ray wavefront. The metrology frame that holds the interferometric sensors is designed to be largely insensitive to temperature changes, providing an ultra-stable reference datum to enhance repeatability. We demonstrate an unprecedented level of fast and precise optical control in the X-ray domain: the profile of a macroscopic X-ray mirror of over 0.5 m in length was freely adjusted and stabilized to atomic level height resolution. Aside from demonstrating the extreme sensitivity of the interferometer sensors, this study also highlights the voltage repeatability and stability of the programmable high-voltage power supply, the accuracy of the correction-calculation algorithms and the almost instantaneous response of the bimorph mirror to command voltage pulses. Finally, we demonstrate the robustness of the system by showing that the bimorph mirror's optical surface was not damaged by more than 1 million voltage cycles, including no occurrence of the `junction effect' or weakening of piezoelectric actuator strength. Hence, this hardware combination provides a real time, hyper-precise, temperature-insensitive, closed-loop system which could benefit many optical communities, including EUV lithography, who require sub-nanometre bending control of the mirror form.

Intelligent active and reactive power control using multi-layer neural network based MPPT controller for grid tied solar PV system under fault conditions

The integration of renewable energy sources, particularly grid-tied solar photovoltaic (PV) systems, into the modern power grid has become increasingly prevalent. However, ensuring the reliable and efficient operation of grid-tied PV systems under various grid conditions, including fault scenarios, poses a significant challenge. In the event of grid faults or disturbances, traditional control methods often fall short in maintaining stable and reliable operation. This paper introduces a multi-layer neural network (MLNN) based MPPT controller that adapts intelligently to grid fault conditions, mitigating the impact on the grid-tied PV system's performance and providing low voltage ride through (LVRT). The research employs a detailed simulation framework on MATLAB to validate the effectiveness of the proposed controller under fault conditions. The LVRT capability of the designed system was analyzed and validated according to Indian grid code. The proposed controller leverages its capacity to learn and make real-time decisions to optimize the active and reactive power outputs of the PV system as per the grid code. Simulation results demonstrate that the proposed controller not only improves the fault tolerance of grid-tied PV systems but also enhances their performance, ensuring a stable and continuous power supply in the face of grid disturbances.

Research for SARIMA and PatchTSMixer Models on the IEA Monthly Statistics Dataset

The rapid evolution of intelligent algorithms has led to their extensive application in time-series forecasting, particularly in predicting electricity consumption. Accurate forecasting is crucial for energy management, policy-making, and ensuring a stable power supply. However, a significant gap exists in comparing the predictive performance of traditional machine learning methods with advanced deep learning models using real-world datasets. This study aims to address this gap by evaluating and comparing the prediction accuracy of machine learning and deep learning techniques using the Monthly Electricity Statistics dataset from the International Energy Agency (IEA). The research employs a rigorous experimental design, leveraging models such as ARIMA and PatchTSMixer, with an emphasis on model tuning and performance metrics like MAE, MAPE, and RMSE. The findings reveal that deep learning models, particularly PatchTSMixer, outperform traditional machine learning methods in terms of prediction accuracy, demonstrating their superior capability in capturing complex temporal dependencies in electricity consumption data. These results highlight the potential of deep learning models for enhancing predictive accuracy in time-series forecasting, providing valuable insights for future research and practical applications in energy management.

Comparative Analysis of Load Forecasting Algorithms Using Machine Learning

Abstract—Load forecasting is a critical aspect of energy management, enabling utilities to optimize resource allocation and ensure grid stability and reliable power supply. This study proposes a comprehensive approach to enhancing load forecasting accuracy by combining Autoregressive Integrated Moving Average (ARIMA) with Seasonal and Exogenous factors known as Regressor Integrated Moving Average with exogenous factors (SARIMAX) which is categorized under statistical machine learning modeling techniques. The integration of exogenous variables, such as weather conditions and socio- economic factors, enhances the forecasting accuracy by capturing external influences on load demand. The ARIMA/SARIMAX hybrid model is trained on historical load data, exogenous variables, and seasonality patterns, providing a robust framework for capturing complex temporal dependencies. The study evaluates the proposed approach using real-world load datasets, comparing its performance against traditional ARIMA and machine learning models. The results demonstrate superior forecasting accuracy, illustrating the effectiveness of integrating exogenous factors into the ARIMA/SARIMAX model for load forecasting applications. This hybrid approach contributes to the advancement of reliable and precise load forecasting techniques, crucial for effective energy planning and management in modern power systems. Keywords—Load Forecasting, Statistical modeling techiques, Exogenous Regressors, Autoregressive Integrated Moving Average (ARIMA), Seasonal and Exogenous Regressors Integrated Moving Average (SARIMAX)

Justification of the scheme of melting ice on the wires of overhead power lines

The article discusses an urgent problem related to the formation of ice and frost deposits on the wires of overhead power lines, which causes significant technical difficulties in ensuring the reliability of power supply, especially in winter. The accumulation of ice and frost on the wires leads to an increase in the weight of the wires, which can cause them to sag, damage insulators, destroy poles, and, as a result, serious accidents on power lines. The causes of accidents in power grids due to ice and frost deposits are analyzed and it is found that their elimination will reduce the probability of damage to overhead power lines from the action of ice loads. However, existing methods of dealing with this problem have a number of disadvantages, such as high cost, low efficiency in certain conditions, and negative environmental impact. The article presents a scheme for melting ice on the wires of overhead power lines by the method of a three-phase short circuit when powered by a power source with a solidly grounded and isolated neutral. This scheme makes it possible to locally increase the temperature of wires to a level sufficient to melt ice and frost, which reduces the risk of emergencies and ensures the uninterrupted operation of the electrical grid. The article provides theoretical justifications for the scheme for melting ice with three-phase short-circuit currents, which confirm its effectiveness. The analysis has shown that the proposed scheme is economically feasible, as it reduces the cost of maintenance and repair of overhead power lines. In addition, it is environmentally friendly, as it does not involve the use of chemicals. Thus, the proposed scheme for melting ice on the wires of overhead power lines is a promising solution for improving the reliability and safety of power supply in winter weather conditions. Its application can significantly increase the efficiency of power grid operation, reduce the number of accidents, and improve the stability of power supply to consumers.

Assessing the factors militating against the effective implementation of electronic health records (EHR) in Nigeria

This study assessed the factors militating against the effective implementation of electronic health records (EHR) in Nigeria, the computerization of patients’ health records with a lot of benefits including improved patients’ satisfaction, improved care processes, reduction of patients’ waiting time, and medication errors. Despite these benefits, healthcare organizations are slow to adopt the EHR system. Therefore, the study assessed the factors militating against the effective implementation of the EHR system, the level of awareness of EHR, and the utilization of electronic health records; it also investigated the factors militating against the effective implementation of EHR. This is a descriptive cross-sectional study conducted among members of staff of the University of Medical Sciences Teaching Hospital (UNIMEDTH). Purposive sampling was adopted to select the study participants, and a structured questionnaire was used for data collection. Statistical Product and Service Solutions (SPSS) version 27 was used for data analysis, R and Microsoft Excel were used for data visualization. Findings revealed that the respondents had a high level of awareness of EHR with an average mean of 1.29. The study revealed a low utilization rate for EHR (mean 3.01, SD 0.115) and data storage (mean 3.00, SD 0.231), indicating major problems with EHR implementation. Financial limitations (mean 2.63, SD 0.486) and insufficient ICT resources (mean 2.62, SD 0.516) are the main challenges. The regression analysis revealed that EHR utilization is highly impacted by awareness (F = 4.26, p < 0.008), accounting for 15.1% of the variance. Governmental assistance and financial constraints showed a statistically significant negative correlation (r=−0.04, p < 0.05), while inadequate internet connectivity and epileptic power supply showed a statistically significant positive association (r = 0.1, p < 0.05). The results show that, more government support is needed to alleviate financial constraints, and a stable power supply may enhance internet accessibility. The study concluded that epileptic power supply, poor internet connectivity, financial constraints, and other factors mentioned in the study militate against the effective implementation of electronic health records. It was recommended that the management should implement electronic health records effectively for efficient and effective health care delivery.

THE PUBLIC ADMINISTRATION OF INFORMATION TECHNOLOGIES IN HEALTH CARE FIELD UNDER THE CONDITIONS OF MARTIAL STATE

In the conditions of martial state, the need for fast and accurate processing of large volumes of information, especially in the field of health care, is increasing. This is due to a lack of resources, an increase in the number of victims of military operations, displaced persons, the complication of access to medical services in connection with hostilities, the loss of individual specialists and information arrays, as well as increased risks for information security and the entire process of public management of information technologies in the health care field. The public administration of information technologies in health care is becoming critical to ensure the stability of healthcare services, support communications between healthcare facilities and government agencies, and effectively process and protect healthcare data. The analysis of scientific works on the issue of the public administration of information technologies in health care under the conditions of martial state is insufficiently researched. The article highlights the main challenges of the industry related to the war: ensuring the continuity of medical services, the appropriateness of public administration decision-making in conditions of limited access to open information, problems with logistics, and stable power supply, information protection and cyber security, shortage of qualified personnel; adopted management decisions, and information technology tools, which were used by authorities at the state and regional levels to respond to new challenges, were investigated. In the article, we are offered some recommendations for the further development and management of information technologies in the healthcare field. The main ones are further measures to strengthen cybersecurity, namely the widespread implementation of the Digital Competency Framework for Healthcare Workers in various areas, the implementation of modern information protection systems, the implementation of an information security policy, measures to legalize and update software, deepening the integration of mHealth into the existing eHealth system to enable rapid and remote collection of medical information and the introduction of artificial intelligence elements into healthcare management processes.

ENERGY MANAGEMENT IN HYBRID PV-WIND-BATTERY STORAGE-BASED MICROGRID USING MONTE CARLO OPTIMIZATION TECHNIQUE

The paper presents an efficient energy management system designed for a small-scale hybrid microgrid incorporating wind, solar, and battery-based energy generation systems using three types of Monte Carlo simulation techniques. The heart of the proposed system is the energy management system, which is responsible for maintaining power balance within the microgrid. The EMS continuously monitors variations in renewable energy generation and load demand and adjusts the operation of the energy conversion systems and battery storage to ensure optimal performance and reliability. The primary objective of the energy management system is to maintain power balance within the microgrid, even in the face of fluctuations in renewable energy generation and load demand. This involves dynamically adjusting the operation of the renewable energy sources and battery storage system to match the instantaneous power requirements of the microgrid. Overall, the paper presents a comprehensive approach to designing and implementing the Monte Carlo technique to extract maximum energy profit using the hybrid microgrid. By integrating renewable energy sources with energy storage and advanced control algorithms, the proposed system aims to enhance the reliability, stability, and sustainability of the microgrid's power supply.

Pemeliharaan Kubikel 20kV dan Keandalan Distribusi Listrik Pelanggan ULP Prima Krakatau

The 20 kV cubicle plays a vital role in the electrical distribution system, particularly in maintaining the reliability and stability of power supply for industrial customers. This study aims to evaluate the impact of routine maintenance on the performance of 20 kV cubicles at the Prima Krakatau Customer Service Unit (ULP). Maintenance is carried out periodically using time-based and condition-based approaches and includes measuring technical parameters such as grounding resistance, Current Transformer (CT), and Circuit Breaker (PMT). The results indicate that all components are operating optimally, with grounding resistance at 0.6 ohms and resistance values for CT and PMT exceeding 50 GΩ. The study concludes that scheduled cubicle maintenance contributes to improved power distribution reliability, reduced risk of system interruptions, and enhanced customer satisfaction.

Fault diagnosis of cells in PEM electrolyzer stack under fluctuating power source

The scale of hydrogen production using renewable energy is expanding with the global energy transition accelerating. Safety in hydrogen production is gaining increased attention. Compared to stable power supplies, the voltage of the electrolyzer stack fluctuates dynamically with input power in the wind and photovoltaic systems. For this reason, a fault diagnosis method for electrolyzers under fluctuating power sources is proposed. Firstly, the faults of the sensor and electrolyzer are distinguished by interleaved voltage detection to avoid the misdiagnosis caused by the sensor failure. Secondly, an improved variable mode decomposition method is introduced to process the raw voltage signals to weaken the influence of inter-cell inconsistency on fault diagnosis. Furthermore, the fault characteristics are highlighted with a shortened fault identification time. Then, the fault characteristics are extracted by the degree of cell voltage fluctuation with the occurrence of faults is judged by the difference in the degree of fluctuation between neighboring electrolyzers. Finally, faults such as short circuits and water shortages in the electrolyzer are diagnosed using the correlation differences in cell voltage between different faults. The feasibility of this fault diagnosis method is validated through case studies and comparative analyses.

Monitoring and Control System for Temperature, Humidity, and Air Quality in LVMDP Panel Rooms to Improve the Reliability of LVMDP Panel Components Using Fuzzy Logic

Power Distribution System plays a crucial role in modern life. Low Voltage Distribution Panel (LVMDP) is a critical component within this system, responsible for distributing low-voltage electricity to various loads. Optimal reliability and performance of LVMDP are essential to ensure continuity and stability of power supply. Environmental factors such as temperature, humidity, and air quality can significantly impact the reliability and lifespan of electrical equipment in LVMDP. An Internet of Things (IoT) and Fuzzy Logic-based Monitoring and Control System offers an innovative solution to address these issues. The IoT Monitoring System enables real-time monitoring of temperature, humidity, and air quality, allowing for early detection of environmental changes that could potentially damage equipment. The Fuzzy Control System then responds to these changes automatically and adaptively, regulating cooling, heating, circulation, dehumidifier, or humidifier devices to maintain optimal environmental conditions. Implementing this Monitoring and Control System not only enhances equipment reliability and lifespan but also reduces the risk of power supply disruptions. Therefore, the development and implementation of this system serve as an innovative and essential solution in maintaining the reliability of the power system. Advancements in IoT and Fuzzy Logic technologies open up opportunities to improve the overall reliability and efficiency of the Power Distribution System. This Monitoring and Control System presents a strategic step that can yield significant positive impacts on the reliability and performance of the Power Distribution System as a whole.

Numerical simulation of impact response for pantograph slide plate

As a key component, the performance of pantograph slide plate is directly related to the normal operation of train and the stability of power supply. The pantograph slide plate is exposed to the complex environment and thus it would face mechanical impacts. In this paper, two types of slide plate models (wide and narrow) are constructed to explore the mechanical behavior of pantograph slide plate under impact using the finite element method. The results show that the slide plates rapidly deform at the beginning of the impact, and then the stress variation speed gradually slows down and tends towards a stable and low amplitude of self vibration. Under strong impact, the vibration frequency and amplitude of the slide plate significantly increase. The stress level of wide slide plate is lower than that of narrow slide plate. Narrow slide plate exhibits higher sensitivity and instability. This indicates that when the train is running at a high speed, factors such as track irregularities and airflow disturbances have a more significant impact on the narrow slide plate. This work provides valuable references for subsequent optimization design.

Study on the Production Mechanism of Fluorescent Species and Its Application to White LED of NH4-Form Y-Type Zeolite

Introduction Zeolites are aluminosilicates in which all vertices of SiO4 and AlO4 tetrahedra (TO4) are shared to build a three-dimensional framework and are classified on the basis of the framework structure. Assuming that SiO4 4− in neutral SiO2 is partially replaced with AlO4 5−, the zeolite framework becomes negatively charged by the same amount of Al3+. Various cations can be introduced into the framework cages to compensate for this negative charge. The charge-compensating cation is primarily an alkali metal ion such as Na+ or K+. This cation can be easily ion-exchanged for other monovalent and polyvalent cations. Zeolites are ideal host materials because they can easily give fluorescent light by introducing luminescent centers as charge-compensating cations and are composed only of ubiquitous elements. Hayashi et al., reported that NH4-form Y-type zeolite exhibited intense fluorescence upon calcination in air atmosphere without any addition of dopants. In their study, the following fluorescence mechanism was proposed: On the halfway conversion to the H-form by heating, NH4-form zeolite was decomposed to produce oxygen vacancies. Then new excited levels appeared leading to fluorescence. This zeolite phosphor does not contain any main transition-metal nor rare-earth elements and produces bright white emission, so it will become an ecological and economical fluorescent material. In this study, the dependence of the fluorescence properties of NH4-form Y-type zeolites on calcination atmosphere and temperature was investigated and the production mechanism of fluorescent species was elucidated from detailed analyses of the calcination samples. White LEDs were fabricated from the most fluorescent samples. It is demonstrated that they can be applied practical white phosphors without rare-earth nor main transition-metal elements.Experimental The thermal properties of a commercially available NH4-form Y-type zeolite (Sigma-Aldrich, ammonium Y zeolite) underwent thermogravimetric/differential thermal analysis under air, oxygen, nitrogen, and 4% hydrogen/nitrogen mixture atmospheres at a flow rate of 150 mL/min or in vacuo at approximately 27 Pa using a rotary pump. From the TG curves, 130, 230, 280, 350, 460, 500, and 600 °C were selected as the calcination temperatures. The zeolite was calcined for 2 h at these temperatures under the above-mentioned atmospheres in a tubular furnace. The following measurements were performed on the calcined zeolite samples; X-ray diffraction (XRD), fluorescence, ultraviolet-visual (UV-Vis) absorption, electron spin resonance (ESR), Fourier transform infrared (FT-IR) absorption, and 29Si and 27Al magic-angle spin nuclear magnetic resonance (MAS-NMR) by solid state. The phosphors with the most fluorescence intensities were mixed with silicone rubber sealant in 2:1 mass ratio and were applied to a near-ultraviolet (405 nm) LED. The coated LED was then heated at 80 ºC overnight for curing. This process was repeated three times to fabricate the white LED. These LED were driven at 3.0 V, 16 mA by a stabilized power supply and illuminance and emission spectrum were measured.Results and discussion The fluorescence intensity was higher when the zeolite was calcined at 350 °C for 2 h in nitrogen atmosphere than in other atmospheres. In atmospheres containing oxygen, quenching was observed at the higher temperatures. These results indicate that calcination caused oxygen vacancies under every atmosphere which brought fluorescence properties. The spectra of ESR showed two signals corresponding to the E'-center and the non-bridging oxygen hole center (NBOHC) which were generated by the collapse of the SiO4 tetrahedron. In FT-IR spectra, the absorptions assigned to Si–O vibration decreased in the fluorescent samples. Figure shows the production mechanism of the fluorescent species. The oxygen vacancy on the SiO4 tetrahedron created SiO3 and/or SiO2 radicals and brought the zeolites to generate fluorescence. The white LED constructed with the zeolite phosphors. Driving this LED with a regulated power source could obtain illumination with color temperature of 7500 K and the tristimulus values determined xy chromaticity coordinates parameters to x = 0.272 and y = 0.341. It will be a guidelines to be able to produce a practical white phosphor that does not contain rare-earth nor transition-metal elements. Figure 1

Sputtering Deposition of Lipon Solid Electrolyte on Slurry-Cast Composite Cathode

As the miniaturization of wearable and implantable devices advances, the demand for ultra-small power supply systems continues to grow. These miniature devices can operate by harvesting energy from the environment or by incorporating micro-sized energy storage devices. However, since these devices require continuous and precise operation for therapeutic or diagnostic purposes, integrating micro-sized batteries is essential for a stable power supply. Consequently, one of the major challenges in microbattery development is enhancing areal energy density, as the size must be comparable to that of the miniature device. Thin-film-based microbatteries have attempted to increase surface area by adopting 3D structures, but their effectiveness is limited due to the low ionic and electrical conductivity of cathode materials, which restricts the deposition thickness to just a few tens of nanometers. In all-solid-state batteries, a strategy to address these inherent issues of the cathode involves using composite cathodes that incorporate materials with high ionic conductivity. In this study, we fabricated a composite cathode by slurry-casting a mixture of LiCoO₂ and PEO/PVDF-HFP, followed by the sputter deposition of a LiPON solid electrolyte. The half-cell and full-cell configurations were evaluated for improvements in areal capacity under various C-rates. This method offers flexibility in designing the thickness and composition of the cathode material, highlighting its potential applicability for various future biodevices.

Advanced Aerodynamics‐Driven Energy Harvesting Leveraging Galloping‐Flutter Synergy

AbstractFlow‐induced vibrations (FIVs) serve as the fundamental principle of non‐rotary wind energy harvesting. However, nanogenerators relying on a single FIV effect remain constrained by insufficient breeze energy conversion efficiency. In this paper, we propose a novel galloping‐flutter coupled nanogenerator (GFNG) that leverages the synergistic interaction between these two aerodynamic phenomena, to achieve high performance across broad wind speed bandwidth. A galloping‐flutter coupled mechanism (GFM) is implemented using a multifunctional flexible beam that integrates a galloping piezoelectric energy harvester (GPEH) and a fluttering triboelectric nanogenerator (FTENG). Through meticulous optimization, it significantly enhances the average electrical output of the FTENG by up to six times at low wind speeds below 6 m s−1, by intensifying the triboelectric contact behavior through galloping‐induced beam oscillations. The GFNG demonstrates a maximum average power of 6.3 mW across wind speeds from 1.4 to 10 m s−1, along with a remarkable power density of 7.1 W m−2 of the enhanced FTENG at 10 m s−1, enabling the lighting of 508 LEDs and stable power supply for wireless sensor nodes (WSNs). This study offers new insights into designing high‐performance aerodynamics‐driven nanogenerators by harnessing multiple FIV synergistic effects, broadening the potential for intelligent wind energy applications.