Search Results (4,078)

Silicene is a two-dimensional silicon monolayer with a band gap caused by relatively strong spin–orbit coupling. This band gap can be steered using a vertical electric field. In turn, the change in this electric field value leads to a transition from a topological insulator to a bulk insulator regime. This study aims to develop a phase-space approach to detecting the topological phase transitions in silicene induced by the presence of parallel magnetic and electric fields with the aid of the concept of topological quantum number based on the Wigner–Rényi entropy. A reinterpreted definition of the Wigner distribution function is employed to determine this indicator. The topological phase transition in silicene as a function of the electric field in the presence of the magnetic field is confirmed through the use of the topological quantum number determined for the one-half, Shannon and collision entropies. Full article

Soil moisture serves as a critical factor in the hydrological cycle, affecting plant growth, ecosystem health, and groundwater reserves. Current methods for monitoring and predicting it fail to account for the complexities introduced by climatic variations and other influencing factors, such as the effects of atmospheric interference and data gaps, leading to reduced prediction accuracy. To address these challenges, this study introduces a novel soil moisture prediction model based on remote sensing and deep learning, utilizing the Adaptive Group Radial Lasso Regularized Basis Function Networks (AGRL-RBFN) optimized by the Hierarchical Correlated Spider Wasp Optimizer (HCSWO) and incremental learning (IL) techniques. The proposed method for monitoring soil moisture utilizes hyperspectral and soil moisture data from a 2017 campaign in Karlsruhe, encompassing variables such as datetime, soil moisture percentage, soil temperature, and remote sensing spectral bands. The proposed methodology begins with comprehensive preprocessing of historical remote sensing data to fill gaps, reduce noise, and correct atmospheric disturbances. It then employs a unique seasonal mapping and grouping technique, enhanced by the AdaK-MCC method, to analyze the impact of climatic changes on soil moisture patterns. The model’s innovative feature selection approach, using HCSWO, identifies the most significant predictors, ensuring optimal data input for the AGRL-RBFN model. The model achieves an impressive accuracy of 98.09%, a precision of 98.17%, a recall of 97.24%, and an F1-score of 98.95%, outperforming existing methods. Furthermore, it attains a mean absolute error (MAE) of 0.047 in gap filling and a Dunn Index of 4.897 for clustering. Although successful in many aspects, the study did not investigate the relationship between soil moisture levels and specific crops, which presents an opportunity for future research aimed at enhancing smart agricultural practices. Furthermore, the model can be refined by integrating a wider range of datasets and improving its resilience to extreme weather conditions, thereby providing a reliable tool for climate-responsive agricultural management and water conservation strategies. Full article

Three composite materials, made by inserting the same amount of BiFeO₃/Bi₂₅FeO₄₀ powders (each powder having a different concentration of the secondary phase, Bi₂₅FeO₄₀: 10%, 20%, and 30%) into a silicone rubber (SR) matrix, were investigated to understand their electrical properties. Electrical conductivity measurements of the composite samples were carried out over a frequency range from 0.5 kHz to 2 MHz. The resulting conductivity spectra revealed two distinct regions: a low-frequency plateau corresponding to DC conductivity and a high-frequency region where AC conductivity increases with frequency. Some key electrical parameters, such as DC conductivity and band gap energy, were calculated using these measurements. An increase in Bi₂₅FeO₄₀ concentration resulted in a rise in DC conductivity from 5.61 × 10⁻⁵ S/m to 7.67 × 10⁻⁵ S/m across the composite samples. To gain further insight into the mechanisms of charge transport, both Jonscher’s universal response and the correlated barrier hopping (CBH) model were applied. The polaron model was also used to calculate the energy barrier for electrical conduction, but for higher temperatures (where the samples exhibit conductor behavior). The last part of the study was an aging analysis that showed a degradation of the investigated sample, as reflected by a decline in their conductive properties over time. Having no endothermic or exothermic events in the DTA curves, it is clear that the observed variation in conductive properties is not related to phase transitions, but it can be attributed to microstructural mechanisms, such as defects, microcracks, or structural disorders. These results can help in designing composite materials with desirable conductive properties by optimizing their filler concentration and processing conditions. Full article

The synthesis of Fe₃O₄ and ε-Fe₂O₃ nanoparticles (hereafter referred to as Fe₃O₄ NPs and ε-Fe₂O₃ NPs, respectively) was conducted in an eco-friendly manner using FeCl₃·6H₂O as the primary reactant. The experiment was conducted by subjecting the sample to an aqueous solution of FeCl₂·4H₂O at a temperature of 80 °C for a duration of 45 min, with the inclusion of apricot kernel shell extract (AKSE) as a natural reducing agent. The synthesized Fe₃O₄ NPs and ε-Fe₂O₃ NPs were characterized using Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), scanning electron microscopy (SEM), and thermogravimetric analysis (TGA). The optical properties of Fe₃O₄ NPs and ε-Fe₂O₃ NPs were examined, with the band gap energy estimated using the Kubelka–Munk formula. The results demonstrated a band gap of E_g (Fe₃O₄ NPs) = 2.59 eV and E_g (ε-Fe₂O₃ NPs) = 2.75 eV, thereby confirming their semiconductor behavior. The photoconductivity of Fe₃O₄ NPs and ε-Fe₂O₃ NPs was analyzed as a function of photon energy. For Fe₃O₄ NPs, photoconductivity exhibited an increase between 1.37 eV and 6.2 eV prior to reaching a state of stability. A comparable trend was observed for ε-Fe₂O₃ NPs, with an increase from 1.35 eV to 6.22 eV, followed by stabilization. Furthermore, the extinction coefficient (k) was determined. For Fe₃O₄ NPs, k ranged from 39 to a maximum of 300, while for ε-Fe₂O₃ NPs, it varied from 37 to a maximum of 280. A higher k value indicates strong light absorption, rendering these nanoparticles highly suitable for photothermal and sensing applications. Full article

Al-based intermetallic compounds possess excellent mechanical and thermal properties, making them promising candidates for high-temperature structural applications. In this study, the structural stability, mechanical properties, and electronic characteristics of Al₅TM (TM = Mo, Nb, Os, Re, Ru, Ta, Tc, Ti) intermetallic compounds were systematically investigated using first-principles calculations based on density functional theory. All alloys exhibit negative formation energy, indicating favorable thermodynamic stability. Elastic constant analysis shows that all compounds satisfy the Born stability criteria, confirming their mechanical stability. Among them, Al₅Mo (205.9 GPa), Al₅Nb (201.1 GPa), and Al₅Ta (204.1 GPa) exhibit relatively high Young’s moduli, while Al₅Os, Al₅Re, and Al₅Ru demonstrate large bulk moduli and good ductility. The high Debye temperatures of Al₅Mo (600.5 K) and Al₅Nb (606.7 K) suggest excellent thermal stability at elevated temperatures. Electronic structure analysis reveals that all alloys exhibit metallic behavior with no band gap near the Fermi level. The hybridization between TM-d and Al-3p orbitals enhances the covalent bonding between Al and TM atoms. This study provides theoretical guidance for the design and application of high-performance Al-based intermetallic compounds. Full article

The objective of the study was to develop and validate a ground-based method using a depth image sensor equipped with depth, visible red, green, blue (RGB), and near-infrared bands to measure the leaf area index (LAI) based on the relative illuminance of foliage only. The method was applied in a Itajii chinkapin (Castanopsis sieboldii (Makino) Hatus. ex T.Yamaz. & Mashiba )forest in Aichi Prefecture, Japan, and validated by comparing estimates with conventional methods (LAI-2200 and fisheye photography). To apply the 5-band sensor to actual forests, a methodology is proposed for matching the color camera and near-infrared camera in units of pixels, along with a method for widening the exposure range through multi-step camera exposure. Based on these advancements, the RGB color band, near-infrared band, and depth band are converted into several physical properties. Employing these properties, each pixel of the canopy image is classified into upper foliage, lower foliage, sky, and non-assimilated parts (stems and branches). Subsequently, the LAI is calculated using the gap-fraction method, which is based on the relative illuminance of the foliage. In comparison with existing indirect LAI estimations, this technique enabled the distinction between upper and lower canopy layers and the exclusion of non-assimilated parts. The findings indicate that the plant area index (PAI) ranged from 2.23 to 3.68 m² m⁻², representing an increase from 33% to 34% compared to the LAI calculated after excluding non-assimilating parts. The findings of this study underscore the necessity of distinguishing non-assimilated components in the estimation of LAI. The PAI estimates derived from the depth image sensor exhibited moderate to strong agreement with the LAI-2200, contingent upon canopy rings (R² = 0.48–0.98), thereby substantiating the reliability of the system’s performance. The developed approaches also permit the evaluation of the distributions of leaves and branches at various heights from the ground surface to the top of the canopy. The novel LAI measurement method developed in this study has the potential to provide precise, reliable foundational data to support research in ecology and hydrology related to complex tree structures. Full article

Machine learning modeling is a valuable tool for gap-filling or prediction, and its performance is typically evaluated using standard metrics. To enable more precise assessments for time-series data, this study emphasizes the importance of considering time-series consistency, which can be evaluated through amplitude—specifically, the interquartile range and the lower bound of the band in gap-filled time series. To test this hypothesis, a gap-filling technique was applied using long-term (~6 years) high-frequency flux and meteorological data collected at four different levels (1.5, 60, 140, and 300 m above sea level) on a ~300 m tall flux tower. This study focused on turbulent kinetic energy among several variables, which is important for estimating sensible and latent heat fluxes and net ecosystem exchange. Five ensemble machine learning algorithms were selected and trained on three different datasets. Among several modeling scenarios, the stacking model with a dataset combined with derivative data produced the best metrics for predicting turbulent kinetic energy. Although the metrics before and after gap-filling reported fewer differences among the scenarios, large distortions were found in the consistency of the time series in terms of amplitude. These findings underscore the importance of evaluating time-series consistency alongside traditional metrics, not only to accurately assess modeling performance but also to ensure reliability in downstream applications such as forecasting, climate modeling, and energy estimation. Full article

This study presents a compact and high-efficiency microstrip antenna integrated with a square electromagnetic band-gap (EBG) structure for radio frequency energy harvesting to power battery-less Internet of Things (IoT) sensors and medical devices in the 5.8 GHz Industrial, Scientific, and Medical (ISM) band. The proposed antenna features a compact design with reduced physical dimensions of 36 × 40 mm² (0.69λ_o × 0.76λ_o) while providing high-performance parameters such as a reflection coefficient of −27.9 dB, a voltage standing wave ratio (VSWR) of 1.08, a gain of 7.91 dBi, directivity of 8.1 dBi, a bandwidth of 188 MHz, and radiation efficiency of 95.5%. Incorporating EBG cells suppresses surface waves, enhances gain, and optimizes impedance matching through 50 Ω inset feeding. The simulated and measured results of the designed antenna show a high correlation. This study demonstrates a robust and promising solution for high-performance wireless systems requiring a compact size and energy-efficient operation. Full article

A facile and versatile strategy to obtain NPs@ZnAlO nanocomposite materials, comprising controlled-size nanoparticles (NPs) within a ZnAlO matrix is reported. The mono-(Au, Ni) and bimetallic (Ni-Au) NPs serving as an active phase were prepared by the polyol-alkaline method, while the ZnAlO support was obtained via the thermal decomposition of its corresponding layered double hydroxide (LDH) precursors. X-ray diffraction (XRD) patterns confirmed the successful fabrication of the nanocomposites, including the synthesis of the metallic NPs, the formation of LDH-like structure, and the subsequent transformation to ZnO phase upon LDH calcination. The obtained nanostructures confirmed the nanoplate-like morphology inherited from the original LDH precursors, which tended to aggregate after the addition of gold NPs. According to the UV-Vis spectroscopy, loading NPs onto the ZnAlO support enhanced the light absorption and reduced the band gap energy. ATR-DRIFT spectroscopy, H₂-TPR measurements, and XPS analysis provided information about the functional groups, surface composition, and reducibility of the materials. The catalytic performance of the developed nanostructures was evaluated by the photodegradation of bisphenol A (BPA), under simulated solar irradiation. The conversion of BPA over the bimetallic Ni-Au@ZnAlO reached up to 95% after 180 min of irradiation, exceeding the monometallic Ni@ZnAlO and Au@ZnAlO catalysts. Its enhanced activity was correlated with good dispersion of the bimetals, narrower band gap, and efficient charge carrier separation of the photo-induced e⁻/h⁺ pairs. Full article

This paper presents the design, implementation, and experimental validation of a Condition Monitoring (CM) circuit for SiC-based Power Electronics Converters (PECs). The paper leverages in situ drain–source resistance (R_ds,on) measurements, interfaced with cloud connectivity for data processing and lifetime assessment, addressing key limitations in current state-of-the-art (SOTA) methods. Traditional approaches rely on expensive data acquisition systems under controlled laboratory conditions, making them unsuitable for real-world applications due to component variability, time delay, and noise sensitivity. Furthermore, these methods lack cloud interfacing for real-time data analysis and fail to provide comprehensive reliability metrics such as Remaining Useful Life (RUL). Additionally, the proposed CM method benefits from noise mitigation during switching transitions by utilizing delay circuits to ensure stable and accurate data capture. Moreover, collected data are transmitted to the cloud for long-term health assessment and damage evaluation. In this paper, experimental validation follows a structured design involving signal acquisition, filtering, cloud transmission, and temperature and thermal degradation tracking. Experimental testing has been conducted at different temperatures and operating conditions, considering coolant temperature variations (40 °C to 80 °C), and an output power of 7 kW. Results have demonstrated a clear correlation between temperature rise and R_ds,on variations, validating the ability of the proposed method to predict device degradation. Finally, by leveraging cloud computing, this work provides a practical solution for real-world Wide Band Gap (WBG)-based PEC reliability and lifetime assessment. Full article

Frequency-hopping multiple access is widely adopted to blunt narrow-band jamming and limit spectral disclosure in cyber–physical systems, yet its practical resilience depends on three sequence-level properties. First, balancedness guarantees that every carrier is occupied equally often, removing spectral peaks that a jammer or energy detector could exploit. Second, a wide gap between successive hops forces any interferer to re-tune after corrupting at most one symbol, thereby containing error bursts. Third, a no-hit zone (NHZ) window with a zero pairwise Hamming correlation eliminates user collisions and self-interference when chip-level timing offsets fall inside the window. This work introduces an algebraic construction that meets the full set of requirements in a single framework. By threading a permutation over an integer ring and partitioning the period into congruent sub-blocks tied to the desired NHZ width, we generate balanced wide gap no-hit zone frequency-hopping (WG-NHZ FH) sequence sets. Analytical proofs show that (i) each sequence achieves the Lempel–Greenberger bound for auto-correlation, (ii) the family and zone sizes satisfy the Ye–Fan bound with equality, (iii) the hop-to-hop distance satisfies a provable WG condition, and (iv) balancedness holds exactly for every carrier frequency. Full article

Intermediate-band photovoltaics promise single-junction efficiencies that exceed the Shockley and Queisser limit, yet viable material platforms and device geometries remain under debate. Here, we perform comprehensive two-dimensional device-scale simulations using Silvaco Atlas TCAD to analyze p-i-n In_0.20Ga_0.80N solar cells in which the intermediate band is supplied by In_0.35Ga_0.65N quantum dots located inside the intrinsic layer. Quantum-dot diameters from 1 nm to 10 nm and areal densities up to 116 dots per period are evaluated under AM 1.5G, one-sun illumination at 300 K. The baseline pn junction achieves a simulated power-conversion efficiency of 33.9%. The incorporation of a single 1 nm quantum-dot layer dramatically increases efficiency to 48.1%, driven by a 35% enhancement in short-circuit current density while maintaining open-circuit voltage stability. Further increases in dot density continue to boost current but with diminishing benefit; the highest efficiency recorded, 49.4% at 116 dots, is only 1.4 percentage points above the 40-dot configuration. The improvements originate from two-step sub-band-gap absorption mediated by the quantum dots and from enhanced carrier collection in a widened depletion region. These results define a practical design window centred on approximately 1 nm dots and about 40 dots per period, balancing substantial efficiency gains with manageable structural complexity and providing concrete targets for epitaxial implementation. Full article

Copper-doped indium zinc sulfides were synthesized by heating and stirring a mixture of zinc chloride, indium chloride tetrahydrate, thioacetamide, and copper chloride at 180 °C for 18 h. Among these, Zn_5.7Cu_0.3In₂S₉ exhibited a hydrogen-producing activity of 1660 μmol/g·h, which was approximately five times higher than that of pristine indium zinc sulfide. Therefore, the catalyst was characterized to investigate the effect of Cu addition. PL results revealed that the incorporation of Cu reduced the fluorescence intensity, indicating suppressed recombination of photogenerated electron–hole pairs. DRS showed that the Cu addition enhanced optical absorption in the visible-light region and narrowed the band gap. These findings suggest that the incorporation of copper into indium zinc sulfide improves its photocatalytic activity. Full article

Hexavalent chromium (Cr(VI)), a common contaminant in industrial wastewater, poses severe health risks due to its carcinogenic and mutagenic properties. Consequently, the development of efficient and environmentally friendly methods to reduce Cr(VI) to the less toxic trivalent chromium (Cr(III)) is of great importance. In this study, we present a cost-effective photocatalytic approach using graphitic carbon nitride (g-C₃N₄) modified with 1,3,5-trihydroxybenzene via one-step thermal condensation. The modified photo-catalyst exhibited improved surface area, porosity, visible-light absorption, and a narrowed band gap, all of which contributed to enhanced charge separation. As a result, nearly complete reduction in Cr(VI) was achieved within 90 min under visible-light irradiation. Further optimization of catalyst dosage and EDTA concentration gave even higher reduction efficiency. This work offers a promising strategy for the design of high-performance photocatalysts for environmental remediation. Full article

Forest structure parameters are critical for understanding and managing forest ecosystems, yet sparse forests have received limited attention in previous studies. To address this research gap, this study systematically evaluates and compares the sensitivity of active Synthetic Aperture Radar (SAR) and passive optical remote sensing to key forest structure parameters in sparse forests, including Diameter at Breast Height (DBH), Tree Height (H), Crown Width (CW), and Leaf Area Index (LAI). Using the novel computer-graphics-based radiosity model applicable to porous individual thin objects, named Radiosity Applicable to Porous Individual Objects (RAPID), we simulated 38 distinct sparse forest scenarios to generate both SAR backscatter coefficients and optical reflectance across various wavelengths, polarization modes, and incidence/observation angles. Sensitivity was assessed using the coefficient of variation (CV). The results reveal that C-band SAR in HH polarization mode demonstrates the highest sensitivity to DBH (CV = −6.73%), H (CV = −52.68%), and LAI (CV = −63.39%), while optical data in the red band show the strongest response to CW (CV = 18.83%) variations. The study further identifies optimal acquisition configurations, with SAR data achieving maximum sensitivity at smaller incidence angles and optical reflectance performing best at forward observation angles. This study addresses a critical gap by presenting the first systematic comparison of the sensitivity of multi-band SAR and VIS/NIR data to key forest structural parameters across sparsity gradients, thereby clarifying their applicability for monitoring young and middle-aged sparse forests with high carbon sequestration potential. Full article