Search Results (526)

Highly integrable silicon carbide (SiC) has emerged as a promising platform for photonic integrated circuits (PICs), offering a comprehensive set of material and optical properties that are ideal for the integration of nonlinear devices and solid-state quantum defects. However, despite significant progress in nanofabrication technology, the development of SiC on an insulator (SiCOI)-based photonics faces challenges due to fabrication-induced material optical losses and complex processing steps. An alternative approach to mitigate these fabrication challenges is the direct deposition of amorphous SiC on an insulator (a-SiCOI). However, there is a lack of systematic studies aimed at producing high optical quality a-SiC thin films, and correspondingly, on evaluating and determining their optical properties in the telecom range. To this end, we have studied a single-source precursor, 1,3,5-trisilacyclohexane (TSCH, C₃H₁₂Si₃), and chemical vapor deposition (CVD) processes for the deposition of SiC thin films in a low-temperature range (650–800 °C) on a multitude of different substrates. We have successfully demonstrated the fabrication of smooth, uniform, and stoichiometric a-SiCOI thin films of 20 nm to 600 nm with a highly controlled growth rate of ~0.5 Å/s and minimal surface roughness of ~5 Å. Spectroscopic ellipsometry and resonant micro-photoluminescence excitation spectroscopy and mapping reveal a high index of refraction (~2.7) and a minimal absorption coefficient (<200 cm⁻¹) in the telecom C-band, demonstrating the high optical quality of the films. These findings establish a strong foundation for scalable production of high-quality a-SiCOI thin films, enabling their application in advanced chip-scale telecom PIC technologies. Full article

The converter transformer is subjected to AC/DC composite voltage during operation, and the sealed and time-varying internal state makes its electric field distribution and charge accumulation unable to be monitored in real-time experiments. In this paper, aiming at the influence of bubbles in the oil passage of the converter transformer on charge accumulation before discharge, a simulation model in a laminar flow environment is established, and four different calculation conditions are set to simulate the charge accumulation in 1 s. It is found that under laminar flow conditions, the trapped bubbles on the insulation paper wall play an obvious role in intensifying the charge accumulation in transformer oil, and the extreme range of charge density will increase by about 10⁴ times. Bubbles aggravate the electric field distortion, and the insulation strength of bubbles is lower, which becomes the weak link of insulation. In the laminar flow environment, the oil flow will take away part of the accumulated charge in the oil, but in the case of trapped bubbles, the charge accumulation in the insulating paper will increase from the order of 10⁻² to 10⁻¹. In the case of no bubbles, the transformer oil layer flow will increase the charge accumulation in the insulation paper by 4–5 orders of magnitude. Therefore, it can be seen that the flow of transformer oil will increase the deterioration level of insulation paper. And when the transformer oil is already in the laminar flow state, the influence of laminar flow velocity on charge accumulation is not obvious. The research results in this paper provide a time-varying simulation reference state for the charge accumulation problem that cannot be measured experimentally under normal charged operation conditions, and we obtain quantitative numerical results, which can provide a valuable reference for the study of transformer operation and insulation discharge characteristics. Full article

Building-integrated photovoltaic (BIPV) windows present a viable path towards carbon neutrality in the building sector. However, conventional BIPV windows, such as semi-transparent photovoltaic (STPV) glazings, still suffer from inadequate thermal insulation, which limits their effectiveness across different climate conditions. To address this issue, the cadmium telluride-based vacuum PV glazing has been developed to enhance the thermal performance of BIPV applications. To fully understand the complex thermal behaviour under real-world operational scenarios, this study introduces a one-dimensional transient heat transfer model that can efficiently capture the time-dependent thermal dynamics of this novel glazing system. Based on the numerical solutions using the explicit finite difference method (FDM), the temperature profile of the vacuum PV glazing can be obtained dynamically. Consequently, the heat gain of the semi-transparent vacuum PV glazing can be calculated under time-varying outdoor and indoor conditions. The validated heat transfer model was applied under four different scenarios, viz. summer daytime, summer nighttime, winter daytime, and winter nighttime, to provide a detailed analysis of the dynamic thermal behaviour, including the temperature variation and the energy flow. The dynamic thermal characteristics of the vacuum PV glazing calculated by the transient heat transfer model demonstrate its excellent thermal insulation and solar control capabilities. Moreover, the thermal performance of vacuum PV glazing was compared with a standard double-pane window under various weather conditions of a typical summer day and a typical winter day. The results indicate that the vacuum PV glazing can effectively minimise both heat gain and heat loss. The fluctuation of the inner surface temperature can be controlled within a limited range away from the set point of the indoor room temperature. Therefore, the vacuum PV glazing contributes to stabilising the temperature of the indoor environment despite the fluctuating solar radiation and periodic outdoor temperature. It is suggested that the vacuum PV glazing has the potential to enhance the climate adaptability of BIPV windows under different climate backgrounds. Full article

Despite notable advancements in smart home technologies, residential energy management continues to face critical challenges. These include the complex integration of intermittent renewable energy sources, issues related to data latency, interoperability, and standardization across diverse systems, the inflexibility of centralized control architectures in dynamic environments, and the difficulty of accurately modeling and influencing occupant behavior. To address these challenges, this study proposes an intelligent multi-agent system designed to accurately estimate and control energy consumption in residential buildings, with the overarching objective of optimizing energy usage while maintaining occupant comfort and satisfaction. The methodological approach employed is a hybrid framework, integrating multi-agent system architecture with system dynamics modeling and agent-based modeling. This integration enables decentralized and intelligent control while simultaneously simulating physical processes such as heat exchange, insulation performance, and energy consumption, alongside behavioral interactions and real-time adaptive responses. The system is tested under varying conditions, including changes in building insulation quality and external temperature profiles, to assess its capability for accurate control and estimation of energy use. The proposed tool offers significant added value by supporting real-time responsiveness, behavioral adaptability, and decentralized coordination. It serves as a risk-free simulation platform to test energy-saving strategies, evaluate cost-effective insulation configurations, and fine-tune thermostat settings without incurring additional cost or real-world disruption. The high fidelity and predictive accuracy of the system have important implications for policymakers, building designers, and homeowners, offering a practical foundation for informed decision making and the promotion of sustainable residential energy practices. Full article

This article presents a hybrid fault diagnosis framework for DC–DC converters in photovoltaic (PV) systems, combining digital twin (DT) modelling and detection with machine learning anomaly classification. The proposed method addresses both hardware faults such as open and short circuits in insulated-gate bipolar transistors (IGBTs) and diodes and sensor-level false data injection attacks (FDIAs). A five-dimensional DT architecture is employed, where a virtual entity implemented using FMI-compliant FMUs interacts with a real-time emulated physical plant. Fault detection is performed by comparing the real-time system behaviour with DT predictions, using dynamic thresholds based on power, voltage, and current sensors errors. Once a discrepancy is flagged, a second step classifier processes normalized time-series windows to identify the specific fault type. Synthetic training data are generated using emulation models under normal and faulty conditions, and feature vectors are constructed using a compact, interpretable set of statistical and spectral descriptors. The model was validated using OPAL-RT Hardware in the Loop emulations. The results show high classification accuracy, robustness to environmental fluctuations, and transferability across system configurations. The framework also demonstrates compatibility with low-cost deployment hardware, confirming its practical applicability for fault diagnosis in real-world PV systems. Full article

In view of the limitations of traditional offline detection and external excitation online detection of 10 kV cables, this paper proposes a method to evaluate the insulation aging condition of power cables by online measuring of the phase angle of the cable’s ground current, and explores the impact of load fluctuations on cable insulation. By setting the relative permittivity of the cable to characterize the phase variation of the ground current under different aging degrees, and analyzing the phase variation of the cable’s ground current under different load changes at the same aging degree, a load correction-based dynamic dielectric loss evaluation method for cables is proposed. Through the construction of cable simulation models and the processing of field data, the following conclusions have been reached: Under a 1 MW load, the phase angle of the sheath grounding current in the aged phase increases as the dielectric constant of the insulation increases. At the same aging degree, with an increase in load, the phase differences of the aging phase sheath ground current and the steel armor ground current both show a decreasing trend. To eliminate the impact of load, a dynamic dielectric loss load correction method is proposed, and combined with field data analysis, the dynamic dielectric loss of cables under different loads is corrected to a 1 MW load. Specifically: Under 0.3 MW, the correction coefficients k for the sheath and steel armor are 0.609 and 0.778, respectively. Under 3.5 MW, the correction coefficients k for the sheath and steel armor are 1.435 and 1.089, respectively. This study provides a theoretical basis and experimental verification for online cable monitoring methods. Full article

This paper presents a comprehensive investigation to evaluate the impacts of air cavities between existing walls and insulated panels on the overall R-values of the retrofitted building envelope systems, addressing a key challenge in exterior envelope retrofitting. The effects of several factors are considered including the air cavity thickness (ranging from 0.1 cm to 5 cm), airflow velocity (varying between 0.1 m/s and 1 m/s), and surface emissivity (set between 0.1 and 0.9), in addition to the thickness of the insulated panels (ranging from 1 cm to 7 cm). It is found that any increase in the air cavity thickness increases the overall R-values of the building envelope assemblies when air is trapped within the sealed cavity. However, when air convection is prevalent, the overall R-value of the retrofitted walls decreases with any increase in air velocity and air cavity thickness. For sealed air cavities, the analysis results show a 9% improvement in R-value of the retrofitted walls. However, the R-value of retrofitted walls with unsealed air cavities can degrade by 76% and 81% for natural and forced air flows, respectively. Emissivity adjustment is found to be the most effective in improving the thermal performance of building envelopes with sealed air cavities, increasing the R-value of retrofitted walls by 13.6% when reduced from 0.9 to 0.1. Full article

The traditional detection method of transformer oil acid value has limitations, such as long detection period and toxicity of reagents; while, with the traditional spectral analysis, it is difficult to realize the efficient extraction of key features related to the acid value content. Early detection of rising acid levels is critical to prevent transformer insulation degradation, corrosion, and failure. Conversely, delayed detection accelerates aging and can cause costly repairs or unplanned outages. To address this need, this paper proposes a new method for predicting the acid value content of the transformer oil based on the infrared spectra in the transformer oil and a deep neural network (DNN). The infrared spectral data of the transformer oil is acquired by ALPHA II FT-IR spectrometer, the high frequency noise effect of the spectrum is reduced by wavelet packet decomposition (WPD), and the bootstrapping soft shrinkage (BOSS) algorithm is used to extract the spectra with the highest correlation with the acid value content. The BOSS algorithm is used to extract the feature parameters with the highest correlation with the acid value content in the spectrum, and the DNN prediction model is established to realize the fast prediction of the acid value content of the transformer oil. In comparison with the traditional infrared spectral preprocessing method and regression model, the proposed prediction model has a coefficient of determination (R²) of 97.12% and 95.99% for the prediction set and validation set, respectively, which is 4.96% higher than that of the traditional model. In addition, the accuracy is 5.45% higher than the traditional model, and the R² of the proposed prediction model is 95.04% after complete external data validation, indicating that it has good accuracy. The results show that the infrared spectral analysis method combining WPD noise reduction, BOSS feature extraction, and DNN modeling can realize the rapid prediction of the acid value content of the transformer oil based on infrared spectroscopy technology, and the prediction model can be used to realize the analytical study of transformer oils. The model can be further applied to the monitoring field of the transformer oil characteristic parameter to realize the rapid monitoring of the transformer oil parameters based on a portable infrared spectrometer. Full article

This study presents a comprehensive assessment of energy efficiency and conservation strategies in institutional buildings, using the Riyadh Reformatory Building (RRB) in Saudi Arabia as a case study. The analysis focuses on meeting the operational and safety requirements of the facility while aligning with the regulatory standards of the Saudi Arabian Standards Organization (SASO) and the Saudi Building Code (SBC Part 401), particularly in relation to electrical installations and the Energy Efficiency Ratio (EER) labeling system. Through simulation and evaluation, the study demonstrates that replacing traditional lighting with LED systems results in a 74% reduction in energy consumption. The application of programmable temperature regulators further reduces annual cooling energy use by 5.1%, with associated cost savings reaching 6.2%. Additionally, the research highlights the significant influence of window properties, thermal insulation, and water heater controls on energy performance. Notably, adopting high-EER air conditioning units leads to a 28.4% decrease in annual cooling energy consumption. Collectively, the findings underscore the effectiveness of integrated energy management practices, including optimized building layout, high-efficiency systems, and smart control technologies, in achieving substantial energy savings and operational cost reductions in institutional settings. Full article

Novel diluted magnetic semiconductors derived from BaZn₂As₂ are of considerable importance owing to their elevated Curie temperature of 260 K, the diversity of magnetic states they exhibit, and their prospective applications in multilayer heterojunctions. However, the transition from the intrinsic semiconductor BaZn₂As₂ (BZA) to its doped compounds has not been extensively explored, especially in relation to the significant intermediate compound Ba(Zn,Mn)₂As₂ (BZMA). This study aims to address this gap by performing susceptibility and magnetization measurements, in addition to electronic transport analyses, on these compounds in their single crystal form. Key findings include the following: (1) carriers can significantly modulate the magnetism, transitioning from a non-magnetic BZA to a weak magnetic BZMA, and subsequently to a hard ferromagnet (Ba,K)(Zn,Mn)₂As₂ with potassium (K) doping to BZMA; (2) two distinct sets of metal-insulator transitions were identified, which can be elucidated by the involvement of carriers and the emergence of various magnetic states, respectively; and (3) BZMA exhibits colossal negative magnetoresistance, and by lanthanum (La) doping, a potential n-type (Ba,La)(Zn,Mn)₂As₂ single crystal was synthesized, demonstrating promising prospects for p-n junction applications. This study enhances our understanding of the magnetic interactions and evolutions among these compounds, particularly in the low-doping regime, thereby providing a comprehensive physical framework that complements previous findings related to the high-doping region. Full article

The awareness of global warming has boosted research on methods to reduce energy consumption and greenhouse gas (GHG) emissions. Livestock buildings, although essential for food production, represent a sustainability challenge due to their high maintenance energy costs, GHG emissions, and impact on the environment and rural landscapes. Since the environment, cultural heritage, and community identity deserve protection, research trends and current knowledge on livestock buildings, building sustainability, energy efficiency strategies, and landscape management were investigated using the Web of Science and Scopus search tools (2005–2025). Research on these topics was found to be uneven, with limited focus on livestock buildings compared to food production and animal welfare, and significant interest in eco-sustainable building materials. A total of 96 articles were selected after evaluating over 5400 records. The analysis revealed a lack of universally accepted definitions for building design strategies and their rare application to livestock facilities, where passive solutions and insulation prevailed. The application of renewable energy was rare and limited to rural buildings, as was the application of sustainable building materials to livestock, agriculture, and vernacular buildings. Conversely, increased attention was paid to the definition and classification of vernacular architecture features aimed at enhancing existing buildings and mitigating or facilitating the landscape integration of those that diverge most from them. Although not exhaustive, this review identified some knowledge gaps. More efforts are needed to reduce environmental impacts and meet the milestones set by international agreements. Research on building materials could benefit from collaboration with experts in cultural heritage conservation because of their command of traditional materials, durability-enhancing methods, and biodeterioration. Full article

Flat panel displays for large applications (monitors and TVs) have structural weaknesses in improving the yield of mass-produced products due to large panels: the yield is defined by ratio of output quantity to input into panel fabrication process. From a panel manufacturing point of view, low-cost production should be achieved through improved yield of mass production (Samsung Display’s quantum dot display backplane panel). So, we set the target yield at an extreme value, over the golden yield (90%) at the beginning of new mass products. The main factors contributing to the yield loss were “lifted insulator and etched active pattern defects”. To reach the target yield, we focused on these two main defects. The root causes of these defects (delta coefficient of thermal expansion and galvanic corrosion) are explained, and a defect generation mechanism is proposed (the size of the separated large power line in relation to the defect rate). The power lines are defined based on an Electroluminescent Voltage at the Drain (ELVDD) and Electroluminescent Voltage at the Source (ELVSS). We developed a separated large power line design to reduce defect rates. This design plays a role in preventing these two defects during the mass production of medium to large display panels for use in TVs by ensuring that the large power line area is less than the optimum value (<0.44 cm²). Full article

Residents in low-income multifamily housing often struggle to afford energy for essential needs such as heating, cooking, and electronics. Climate change may increase these energy demands, and high energy bills can reflect inefficiencies in a home’s systems or envelope. Improving the energy efficiency in low-income housing benefits both social justice and sustainability. However, there is limited information on the impact of energy upgrades in multifamily settings. This study examined the energy-related experiences of low-income families in public housing in Detroit, Michigan, who received energy-conserving measures (ECMs) such as efficient light bulbs, faucets, thermostats, and refrigerators in 2022. Thirty-nine residents completed surveys and provided energy usage data before and after the upgrades; twelve residents provided their hourly energy usage. Over 90% of residents reporting income information had an energy burden exceeding 6%, with higher energy expenses during colder months. While many residents appreciated the upgrades, quantitative evidence of reduced energy burdens was insufficient. Existing utility programs for multifamily residents typically offer minor upgrades but do not include larger appliance replacements or improvements to home insulation. To maximize energy efficiency for low-income families, thus promoting sustainability, more comprehensive programs and retrofits are necessary. Full article

Advancements in thermoregulating textiles have been propelled by innovations in nanotechnology, composite materials, and smart fiber engineering. This article reviews recent scholarly papers on experimental passive and active thermoregulating textiles to present the latest advancements in these fabrics, their mechanisms of thermoregulation, and their feasibility for use. The review underscores that phase-change materials enhanced with graphene, boron nitride, and carbon nanofibers offer superior thermal conductivity, phase stability, and flexibility, making them ideal for wearable applications. Shape-stabilized phase-change materials and aerogel-infused fibers have shown promising results in outdoor, industrial, and emergency settings due to their durability and high insulation efficiency. Radiative cooling textiles, engineered with hierarchical nanostructures and Janus wettability, demonstrate passive temperature regulation through selective solar reflection and infrared emission, achieving substantial cooling effects without external energy input. Thermo-responsive, shape-memory materials, and moisture-sensitive polymers enable dynamic insulation and actuation. Liquid-cooling garments and thermoelectric hybrids deliver precise temperature control but face challenges in portability and power consumption. While thermoregulating textiles show promise, the main challenges include achieving scalable manufacturing, ensuring material flexibility, and integrating multiple functions without sacrificing comfort. Future research should focus on hybrid systems combining passive and active mechanisms, user-centric wearability studies, and cost-effective fabrication methods. These innovations hold significant potential for applications in extreme environments, athletic wear, military uniforms, and smart clothing, contributing to energy efficiency, health, and comfort in a warming climate. Full article

This academic paper proposes a terahertz (THz) device featuring dynamic adjustability. This device relies on composite metamaterials made of graphene and vanadium dioxide (VO₂). By integrating the electrically adjustable traits of graphene with the phase transition attributes of VO₂, the suggested metamaterial device can achieve both broadband absorption and dual-band linear-to-circular polarization conversion (LCPC) in the terahertz frequency range. When VO₂ is in its metallic state and the Fermi level of graphene is set to zero electron volts (eV), the device shows remarkable broadband absorption. Specifically, it attains an absorption rate exceeding 90% within the frequency span of 2.28–3.73 terahertz (THz). Moreover, the device displays notable polarization insensitivity and high resistance to changes in the incident angle. Conversely, when VO₂ shifts to its insulating state and the Fermi level of graphene stays at 0 eV, the device operates as a highly effective polarization converter. It attains the best dual-band linear-to-circular polarization conversion within the frequency ranges of 4.31–5.82 THz and 6.77–7.93 THz. Following the alteration of the Fermi level of graphene, the device demonstrated outstanding adjustability. The designed multi-functional device features a simple structure and holds significant application potential in terahertz technologies, including cloaking technology, reflectors, and spatial modulators. Full article