Search Results (7,528)

Perovskite materials have garnered significant attention in both fundamental research and practical applications owing to their exceptional light absorption coefficients, low fabrication costs, and inherent advantages for thin-film and flexible device fabrication. Nevertheless, interface defects within perovskite films induce detrimental non-radiative carrier recombination and pronounced hysteresis effects, which collectively impose substantial limitations on the photovoltaic performance and long-term operational stability of perovskite solar cells (PSCs). Conventional passivation strategies, despite their demonstrated efficacy in mitigating interface defects, often inadvertently introduce secondary defects in originally defect-free regions, thereby restricting the extent of device performance improvement. To overcome this critical limitation, we have developed a precision defect passivation methodology that employs a targeted two-step immersion–cleaning process, achieving selective defect passivation while concomitantly eliminating residual passivating agents. This approach effectively prevents the formation of new defects in unaffected regions of the perovskite films, and the resultant PSC possesses a power conversion efficiency (PCE) of 21.08%, accompanied by a substantial mitigation of hysteresis behavior. Furthermore, unencapsulated devices demonstrate remarkable stability, retaining over 81% of their initial efficiency after 20 days of atmospheric storage under 50% relative humidity, which underscores the effectiveness of our passivation strategy in simultaneously enhancing both device performance and operational stability. Full article

:With the proliferation of image-capturing and display-enabled IoT devices, ensuring the authenticity and integrity of visual data has become increasingly critical, especially in light of emerging cybersecurity threats and powerful generative AI tools. One of the major challenges in such sensor-based systems is the ability to protect privacy while maintaining data usability. Reversible data hiding has attracted growing attention due to its reversibility and ease of implementation, making it a viable solution for secure image communication in IoT environments. In this paper, we propose reversible data hiding techniques tailored to the content characteristics of images. Our approach leverages subsampling and quadtree partitioning, combined with multi-stage prediction schemes, to generate a predicted image aligned with the original. Secret information is embedded by analyzing the difference histogram between the original and predicted images, and enhanced through multi-round rotation techniques and a multi-level embedding strategy to boost capacity. By employing both subsampling and quadtree decomposition, the embedding strategy dynamically adapts to the inherent characteristics of the input image. Furthermore, we investigate the trade-off between embedding capacity and marked image quality. Experimental results demonstrate improved embedding performance, high visual fidelity, and low implementation complexity, highlighting the method’s suitability for resource-constrained IoT applications. Full article

Vigilance refers to a sustained attentional state enabling the detection of specific but unpredictable changes in the external environment. This state typically declines rapidly over time. A deterioration in vigilance can lead to serious errors or accidents in both occupational and special scenarios, rendering vigilance intervention a critical area of interest for researchers. Transcranial direct current stimulation (tDCS) has shown promise in mitigating vigilance decrement. However, the timing of such interventions may yield differential effects, a question that remains unresolved in the literature. The present study examines the possibility of using the average power in the low alpha frequency band (alpha-1) as an Electroencephalography-based index of vigilance to identify a candidate entry point for tDCS application that may enhance efficacy, and further explores how the timing of tDCS influences vigilance outcomes. In the pilot experiment, we determined the timing for guiding tDCS based on the average power of the low alpha frequency band (alpha-1) from five participants, which was identified as the third stage of the experiment. The validity of this timing has been verified in subsequent independent samples with a larger size. In the formal experiment, ninety-nine participants were randomly assigned to three groups, receiving early intervention, late intervention, or a no-stimulus control, and completed a 20 min visual modification of the Bakan Task. The early-stimulated group (n = 33) received anodal stimulation (2 mA) on the right posterior parietal cortex during the first 8 min of the test (0–8 min), the late-stimulated group (n = 33) received stimulation on the same location during the middle 8 min of the test (8–16 min), while the blank control group (n = 33) received no stimulation. Results indicated that the late-stimulated group (8–16 min of stimulation), for which alpha-1 power guided the tDCS onset timing, was associated with a greater attenuation of vigilance decrement compared to the early-stimulated group (0–8 min of stimulation). Both groups demonstrated significant differences in vigilance during the first stage following stimulation. Full article

Brushless DC (BLDC) motors are increasingly used in industrial applications due to their high efficiency, reliability, and low weight. However, their performance strongly depends on the electromagnetic properties of stator and rotor core materials. This study evaluates six BLDC motor configurations, employing materials such as M19 electrical steel, 1010 low-carbon steel, magnetic PLA, and ABS, and analyzes their impact using FEMM 4.2 finite element simulations. Key electromagnetic characteristics—including flux linkage, Back-EMF, torque, and torque ripple—were compared across configurations. The reference motor with M19 steel stator and 1010 steel rotor achieved ~7 mWb flux linkage, ~39 V pk–pk Back-EMF, and 1.44 Nm torque with ~49% ripple, confirming the suitability of laminated steels for high-power-density designs. Substituting M19 with 1010 steel in the stator reduced torque by less than 10%, indicating material interchangeability with minimal performance loss. By contrast, polymer-based designs exhibited drastic degradation: magnetic PLA yielded only 3.5% of the baseline torque with sixfold ripple increase, while ABS delivered nearly zero torque and >700% ripple. Hybrid configurations improved PLA-based results by 15–20%, though they remained far below ferromagnetic cores. Overall, results demonstrate a nearly linear relationship between material permeability and both flux linkage and Back-EMF, alongside a sharp rise in torque ripple at low permeability. The findings highlight the advantages of ferromagnetic and laminated steel cores for efficiency and stability, while polymer and hybrid cores are limited to lightweight demonstrator applications. Full article

Accurate and real-time monitoring of low-frequency electromagnetic field (EMF) is essential in power and industrial environments, yet most conventional approaches still suffer from limited spatial coverage, manual operation, and insufficient digitization. To address these challenges, this paper proposes an intelligent EMF monitoring system that integrates six-axis magnetic field sensing, temperature compensation, vector synthesis, Sub-1 GHz wireless communication, and real-time data visualization. The system supports simultaneous measurement of both AC and DC magnetic fields across the 30 Hz–100 kHz range, with specific optimization for power-frequency conditions (50/60 Hz). Designed with modular integration and low power consumption, it is suitable for portable deployment in field scenarios. Comprehensive laboratory and substation tests demonstrate high accuracy, with maximum measurement errors of 1.17% under zero-field and 1.42% under applied-field conditions—well below the ±5% tolerance defined by international standards. Wireless performance tests further confirm stable long-distance communication, achieving ranges of up to 5 km without significant transmission errors, while overall system measurement error reached as low as 0.015%. These results verify the system’s robustness, fidelity, and compliance with international safety standards. Overall, the proposed platform provides a practical and scalable solution for intelligent EMF monitoring, offering strong potential for deployment in industrial environments and infrastructure-critical applications. Full article

The power system’s dynamic frequency stability is affected by common-mode ultra-low-frequency oscillation and differential-mode low-frequency oscillation. Traditional frequency control based on generators is facing the problem of capacity reduction. It is urgent to explore new regulation resources such as photovoltaics. To address this issue, this paper proposes a distributed active support method based on photovoltaic systems via state–disturbance observation and dynamic surface consensus control. A three-layer distributed control framework is constructed to suppress low-frequency oscillations and ultra-low-frequency oscillations. To solve the high-order problem of the regional grid model and to obtain its unmeasurable variables, a regional observer estimating both system states and external disturbances is designed. Furthermore, a distributed dynamic frequency stability control method is proposed for wide-area photovoltaic clusters based on the dynamic surface control theory. In addition, the stability of the proposed distributed active support method has been proven. Moreover, a parameter tuning algorithm is proposed based on improved chaos game theory. Finally, simulation results demonstrate that, even under a 0–2.5 s time-varying communication delay, the proposed method can restrict the frequency deviation and the inter-area frequency difference index to 0.17 Hz and 0.014, respectively. Moreover, under weak communication conditions, the controller can also maintain dynamic frequency stability. Compared with centralized control and decentralized control, the proposed method reduces the frequency deviation by 26.1% and 17.1%, respectively, and shortens the settling time by 76.3% and 42.9%, respectively. The proposed method can effectively maintain dynamic frequency stability using photovoltaics, demonstrating excellent application potential in renewable-rich power systems. Full article

Due to their unique advantages—such as small size, easy integration, flexible wearability, low power consumption, high sensitivity, and material designability—organic field-effect transistor (OFET) gas sensors have significant application potential in fields such as environmental detection, smart healthcare, robotics, and artificial intelligence. Benzothiadiazole fused tetrathiafulvalenes (TTF) are promising organic semiconductor candidates due to their abundant S atoms and planar π-π conjugation skeletons. We designed a series of derivatives by side-chain modification, and conducted systematic computations on TTF derivatives, including reported and newly designed materials, to analyze how geometric factors affect the charge transport properties of materials at the PBE0/6-311G(d,p) level. The frontier molecular orbitals (FMOs) and reorganization energy indicate that the designed derivatives are promising candidates for organic semiconductor sensing materials. Furthermore, theoretical calculations reveal that the designed TTF derivatives are sensitive to gases like NH₃, H₂S, and SO₂, indicating organic field-effect transistors (OFETs) with gas-sensing functions. Full article

Particle accelerators are powerful tools in fundamental research, medicine, and industry that provide high-energy beams that can be used to study matter and to enable advanced applications. The state-of-the-art particle accelerators are fundamentally constructed from superconducting radio-frequency (SRF) cavities, which act as resonant structures for the acceleration of charged particles. The performance of such cavities is governed by inherent superconducting material properties such as the transition temperature, critical fields, penetration depth, and other related parameters and material quality. For the last few decades, bulk niobium has been the preferred material for SRF cavities, enabling accelerating gradients on the order of ~50 MV/m; however, its intrinsic limitations, high cost, and complicated manufacturing have motivated the search for alternative strategies. Among these, sputter-deposited superconducting thin films offer a promising route to address these challenges by reducing costs, improving thermal stability, and providing access to numerous high-T_c superconductors. This review focuses on progress in sputtered superconducting materials for SRF applications, in particular Nb, NbN, NbTiN, Nb₃Sn, Nb₃Al, V₃Si, Mo–Re, and MgB₂. We review how deposition process parameters such as deposition pressure, substrate temperature, substrate bias, duty cycle, and reactive gas flow influence film microstructure, stoichiometry, and superconducting properties, and link these to RF performance. High-energy deposition techniques, such as HiPIMS, have enabled the deposition of dense Nb and nitride films with high transition temperatures and low surface resistance. In contrast, sputtering of Nb₃Sn offers tunable stoichiometry when compared to vapour diffusion. Relatively new material systems, such as Nb₃Al, V₃Si, Mo-Re, and MgB₂, are just a few of the possibilities offered, but challenges with impurity control, interface engineering, and cavity-scale uniformity will remain. We believe that future progress will depend upon energetic sputtering, multilayer architectures, and systematic demonstrations at the cavity scale. Full article

Radon exposure is the second leading cause of lung cancer worldwide, yet monitoring strategies remain limited, expensive, and unevenly applied. Recent advances in the Internet of Things (IoT) offer the potential to change radon surveillance through low-cost, real-time, distributed sensing networks. This review consolidates emerging research on IoT-based radon monitoring, drawing from both primary radon studies and analogous applications in environmental IoT. A search across six major databases and relevant grey literature yielded only five radon-specific IoT studies, underscoring how new this research field is rather than reflecting a shortcoming of the review. To enhance the analysis, we delve into sensor physics, embedded system design, wireless protocols, and calibration techniques, incorporating lessons from established IoT sectors like indoor air quality, industrial safety, and volcanic gas monitoring. This interdisciplinary approach reveals that many technical and logistical challenges, such as calibration drift, power autonomy, connectivity, and scalability, have been addressed in related fields and can be adapted for radon monitoring. By uniting pioneering efforts within the broader context of IoT-enabled environmental sensing, this review provides a reference point and a future roadmap. It outlines key research priorities, including large-scale validation, standardized calibration methods, AI-driven analytics integration, and equitable deployment strategies. Although radon-focused IoT research is still at an early stage, current progress suggests it could make continuous exposure assessment more reliable, affordable, and widely accessible with clear public health benefits. Full article

This paper explores the development and implementation of a brain–computer interface (BCI) system that utilizes electroencephalogram (EEG) signals for real-time monitoring of attention levels to control unmanned aerial vehicles (UAVs). We propose an innovative approach that combines spectral power analysis and machine learning classification techniques to translate cognitive states into precise UAV command signals. This method overcomes the limitations of traditional threshold-based approaches by adapting to individual differences and improving classification accuracy. Through comprehensive testing with 20 participants in both controlled laboratory environments and real-world scenarios, our system achieved an 85% accuracy rate in distinguishing between high and low attention states and successfully mapped these cognitive states to vertical UAV movements. Experimental results demonstrate that our machine learning-based classification method significantly enhances system robustness and adaptability in noisy environments. This research not only advances UAV operability through neural interfaces but also broadens the practical applications of BCI technology in aviation. Our findings contribute to the expanding field of neurotechnology and underscore the potential for neural signal processing and machine learning integration to revolutionize human–machine interaction in industries where dynamic relationships between cognitive states and automated systems are beneficial. Full article

Micro-electromechanical system (MEMS) sensors are widely used in various navigation applications because of their cost-effectiveness, low power consumption, and compact size. However, their performance is often degraded by temperature hysteresis, which arises from internal temperature gradients. This paper presents a calibration method that corrects temperature hysteresis without requiring any additional hardware or modifications to the existing MEMS sensor design. By analyzing the correlation between the external temperature change rate and hysteresis errors, a mathematical calibration model is derived. The method is experimentally validated on MEMS accelerometers, with results showing an up to 63% reduction in hysteresis errors. We further evaluate bias repeatability, scale factor repeatability, nonlinearity, and Allan variance to assess the broader impacts of the calibration. Although minor trade-offs in noise characteristics are observed, the overall hysteresis performance is substantially improved. The proposed approach offers a practical and efficient solution for enhancing MEMS sensor accuracy in dynamic thermal environments. Full article

A compact electrochemical sensor module for pH detection was developed for potential integration into specialized devices used for live cell or tissue incubation, for applications in highly parallelized cell culture analysis, by incorporating Organ-on-Chip devices. This research focuses on the deposition, structural and chemical analysis, and functional characterization of different titanium-oxide layers with various compositions as potentially sensitive materials for pH sensing applications. The titanium-oxide layers were deposited using vacuum sputtering and atomic layer deposition at 100 °C and 300 °C, respectively. Transmission electron microscopy and X-ray photoelectron spectroscopy were utilized to determine the specific composition and structure of different titanium-oxide layers. These TiO_x-functionalized electrodes were connected to the application-specific analog front-end chip of the low-power readout circuit for precise evaluation. The pH sensitivity of the differently modified electrodes, employing various TiO_x materials, was evaluated using pH calibration solutions ranging from pH 6 to 8. Among the various deposition solutions, such as sputtering or high-temperature atomic layer deposition, the TiO_x layer deposited using low-temperature atomic layer deposition proved more suitable for pH sensing applications, with a sensitivity of 54.8–56.7 mV/pH, which closely approximates the Nernstian response. Full article

The complex environment along railway lines, characterized by low imaging quality, strong background interference, and densely distributed small objects, causes existing detection models to suffer from low accuracy in practical applications. To tackle these challenges, this study aims to develop a robust and lightweight insulator detection model specifically optimized for these challenging railway scenarios. To this end, we release a dedicated comprehensive dataset named complexRailway that covers typical railway scenarios to address the limitations of existing insulator datasets, such as the lack of small-scale objects in high-interference backgrounds. On this basis, we present CutP5-LargeKernelAttention-SIoU (C5LS), an improved YOLOv8 variant with three key improvements: (1) optimized YOLOv8’s detection head by removing the P5 branch to improve feature extraction for small- and medium-sized targets while reducing computational redundancy, (2) integrating a lightweight Large Separable Kernel Attention (LSKA) module to expand the receptive field and improve contextual modeling, (3) and replacing CIoU with SIoU loss to refine localization accuracy and accelerate convergence. Experimental results demonstrate that it reaches 94.7% in mAP@0.5 and 65.5% in mAP@0.5–0.95, outperforming the baseline model by 1.9% and 3.5%, respectively. With an inference speed of 104 FPS and a model size of 13.9 MB, the model balances high precision and lightweight deployment. By providing stable and accurate insulator detection, C5LS not only offers reliable spatial positioning basis for subsequent defect identification but also builds an efficient and feasible intelligent monitoring solution for these failure-prone insulators, thereby effectively enhancing the operational safety and maintenance efficiency of the railway power system. Full article

Water walls in power plant boilers are prone to failure under extreme conditions involving high temperature, corrosion, and wear, which severely threaten unit reliability and operational economy. In this work, a NiCr-Cr₃C₂ protective coating was deposited on SA213-T12 steel substrates using high-velocity oxy-fuel (HVOF) spraying, with arc-sprayed PS45 coating as a reference. The NiCr-Cr₃C₂ coating exhibited a dense, low-porosity structure with homogeneous dispersion of Cr₃C₂ hard phases in the NiCr matrix, forming a typical cauliflower-like composite morphology. During high-temperature sulfate corrosion tests at 750 °C, the NiCr-Cr₃C₂ coating demonstrated superior corrosion resistance, with a weight gain of only 2.7 mg/cm², significantly lower than that of the PS45 coating and the SA213-T12 substrate. The higher microhardness and lower friction coefficient also indicate excellent high-temperature wear resistance. The enhanced performance of the NiCr-Cr₃C₂ coating is attributed to the high Cr content, which promotes the formation of a continuous and protective scale composed of Cr₂O₃ and NiCr₂O₄, effectively inhibiting corrosive diffusion and penetration. This work demonstrates the application prospects of NiCr-Cr₃C₂ coatings on water walls of power plant boilers and guides the development of advanced HVOF coatings. Full article

This study investigates the solubility correlation of oil extracted from Sabah green Robusta coffee (Coffea canephora) beans through supercritical carbon dioxide (SC-CO₂) extraction. Sabah, recognized as the largest coffee-producing region in Malaysia, serves as a significant source of Robusta beans for this research. The solubility of coffee bean oil was evaluated under varying pressures (10–30 MPa) and temperatures (40–80 °C). The maximum solubility, 2.681 mg/g CO₂, was recorded at 30 MPa and 40 °C, whereas the lowest solubility, approximately 0.440 mg/g CO₂, occurred at 20 MPa and 80 °C. A clear inverse relationship between solubility and temperature was observed, with solubility decreasing as temperature increased to 80 °C. Conversely, elevated pressure, particularly at 30 MPa, enhanced solubility due to the increased density and solvent power of SC-CO₂. Experimental data exhibited strong agreement with Chrastil’s equation, yielding a relatively low percentage error of 3.37%, compared with 14.57% for the del Valle-Aguilera model. These findings demonstrate the reliability of Chrastil’s model in predicting the solubility of Sabah green coffee bean oil in SC-CO₂. Overall, the research highlights the potential of SC-CO₂ extraction as a sustainable, solvent-free approach for obtaining high-quality coffee oil extracts, with promising applications in the food industry and possible extension to the recovery of other bioactive compounds in food processing. Full article