Search Results (3,373)

Light alloy wheel rims, despite their widespread use, remain vulnerable to fatigue-related defects and mechanical damage. This study presents a method for assessing their technical condition based on acoustic parameter analysis and classification using a deep neural network. Diagnostic data were collected using a custom-developed ADF (Acoustic Diagnostic Features) system, incorporating the reverberation time (T₆₀), sound absorption coefficient (α), and acoustic energy (E). These parameters were measured during laboratory fatigue testing on a Wheel Resistance Test Rig (WRTR) and from used rims obtained under real-world operating conditions. The neural network was trained on WRTR data and subsequently employed to classify field samples as either “serviceable” or “unserviceable”. Results confirmed the high effectiveness of the proposed method, including its robustness in detecting borderline cases, as demonstrated in a case study involving a mechanically damaged rim. The developed approach offers potential support for diagnostic decision-making in workshop settings and may, in the future, serve as a foundation for sensor-based real-time rim condition monitoring. Full article

Resistive stretching sensors are currently used in healthcare robotics due to their ability to vary electrical resistance when subjected to mechanical strain. However, commercial sensors often lack the softness required for integration into soft structures. This study presents a detailed methodology to characterize fabric-based resistive stretching sensors, focusing on both static and dynamic performance, for application in a smart vascular simulator for surgical training. Five sensors, called #1–#5, were developed using conductive fabrics integrated into soft silicone. Stability and fatigue tests were performed to evaluate their behavior. The surface structure and fiber distribution were analyzed using digital microscopy and scanning electron microscopy, while element analysis was performed via Energy-Dispersive X-ray Spectroscopy. Sensors #1 and #3 are the most stable with a low relative standard deviation and good sensitivity at low strains. Sensor #3 showed the lowest hysteresis, while sensor #1 had the widest operating range (0–30% strain). Although all sensors showed non-monotonic behavior across 0–100% strain, deeper investigation suggested that the sensor response depends on the configuration of conductive paths within and between fabric layers. Soft fabric-based resistive sensors represent a promising technical solution for physical simulators for surgical training. Full article

Ammonia, as a toxic and corrosive gas, is widely present in industrial emissions, agricultural activities, and disease biomarkers. Detecting ammonia is of vital importance to environmental safety and human health. Sensors based on MXene have become an effective means for detecting ammonia gas due to their unique hierarchical structure, adjustable surface chemical properties, and excellent electrical conductivity. This study reviews the latest progress in the use of MXene and its composites for the low-temperature detection of ammonia gas. The strategies for designing MXene composites, including heterojunction engineering, surface functionalization, and active sites, are introduced, and their roles in improving sensing performance are clarified. These methods have significantly improved the ability to detect ammonia, offering high selectivity, rapid responses, and ultra-low detection limits within the low-temperature range. Successful applications in fields such as industrial safety, food quality monitoring, medical diagnosis, and agricultural management have demonstrated the multi-functionality of this technology in complex scenarios. The challenges related to the material’s oxidation resistance, humidity interference, and cross-sensitivity are also discussed. This study aims to briefly describe the reasonable design based on MXene sensors, aiming to achieve real-time and energy-saving environmental and health monitoring networks in the future. Full article

The advancement of Internet of Things (IoT) technology has enabled battery-powered devices to be deployed across a wide range of applications; however, it also introduces challenges such as high energy consumption and security vulnerabilities. To address these issues, adiabatic logic circuits offer a promising solution for achieving energy efficiency and enhancing the security of IoT devices. Adiabatic logic circuits are well suited for energy harvesting systems, especially in applications such as sensor nodes, RFID tags, and other IoT implementations. In these systems, the harvested bipolar sinusoidal RF power is directly used as the power supply for the adiabatic logic circuit. However, adiabatic circuits require a peak detector to provide bulk biasing for pMOS transistors. To meet this requirement, a diode-connected MOS transistor-based voltage doubler circuit is used to convert the sinusoidal input into a usable DC signal. In this paper, we propose a novel adiabatic logic design that maintains low power consumption while optimizing energy and current fluctuations across various input transitions. By ensuring uniform and complementary current flow in each transition within the logic circuit’s functional blocks, the design reduces energy variation and enhances resistance against power analysis attacks. Evaluation under different clock frequencies and load capacitances demonstrates that the proposed adiabatic logic circuit exhibits lower fluctuation and improved security, particularly at load capacitances of 50 fF and 100 fF. The results show that the proposed circuit achieves lower power dissipation compared to conventional designs. As an application example, we implemented an ultrasonic transmitter circuit within a LoRaWAN network at the end-node sensor level, which serves as both a communication protocol and system architecture for long-range communication systems. Full article

Termites pose significant threats to the structural integrity of embankments due to their nesting and tunneling behavior, which leads to internal voids, water leakage, or even dam failure. This review systematically classifies and evaluates current termite detection techniques in the context of embankment maintenance, focusing on physical sensing technologies and biological characteristic-based methods. Physical sensing methods enable non-invasive localization of subsurface anomalies, including ground-penetrating radar, acoustic detection, and electrical resistivity imaging. Biological characteristic-based methods, such as electronic noses, sniffer dogs, visual inspection, intelligent monitoring, and UAV-based image analysis, are capable of detecting volatile compounds and surface activity signs associated with termites. The review summarizes key principles, application scenarios, advantages, and limitations of each technique. It also highlights integrated multi-sensor frameworks and artificial intelligence algorithms as emerging solutions to enhance detection accuracy, adaptability, and automation. The findings suggest that future termite detection in embankments will rely on interdisciplinary integration and intelligent monitoring systems to support early warning, rapid response, and long-term structural resilience. This work provides a scientific foundation and practical reference for advancing termite management and embankment safety strategies. Full article

Nanomotion sensors have emerged as a pivotal technology in microbiology and biology, leveraging advances in nanotechnology, microelectronics, and optics to provide a highly sensitive, label-free detection of biological activity and interactions. These sensors were first limited to nanomechanical oscillators like atomic force microscopy cantilevers, but now they are expanding into new, more intriguing setups. The idea is to convert the inherent nanoscale movements of living organisms—a direct manifestation of their metabolic activity—into measurable signals. This review highlights the evolution and diverse applications of nanomotion sensing. Key methodologies include Atomic Force Microscopy-based sensors, optical nanomotion detection, graphene drum sensors, and optical fiber-based sensors, each offering unique advantages in sensitivity, cost, and applicability. The analysis of complex nanomotion data is increasingly supported by advanced modeling and the integration of artificial intelligence and machine learning, enhancing pattern recognition and automation. The versatility and real-time, label-free nature of nanomotion sensing position it as a transformative tool that could revolutionize diagnostics, therapeutics, and fundamental biological research. Full article

Leveraging the sensitivity of long-period fiber grating (LPFG) to changes in the environmental refractive index, an LPFG-based seawater concentration monitoring sensor is proposed. Considering the highly saltine and alkali characteristics of the sensor’s operating environment, the proposed sensor is packaged by basalt fiber-reinforced polymer (BFRP), and the sensor’s sensitivities were studied by sodium chloride and calcium chloride solution concentration experiments and one real-time sodium chloride solution concentration monitoring experiment. The test results show the wavelength of LPFG, a 3 dB bandwidth and a peak loss of LPFG’s spectrogram change with changes in the concentration of sodium chloride or calcium chloride solutions, but only the wavelength has a good linear relationship with the change in solution concentration, and the sensing coefficient is −0.160 nm/% in the sodium chloride solution and −0.225 nm/% in the calcium chloride solution. The real-time monitoring test further verified the sensor’s sensing performance, with an absolute measurement error of less than 1.8%. The BFRP packaged sensor has good corrosion resistance and a simple structure, and it has a certain application value in the monitoring of salinity in the marine environment and coastal soil. Full article

In resistive polymer humidity sensors, the quality of the resistor chips directly affects the performance. Detecting chip defects remains challenging due to the scarcity of defective samples, which limits traditional supervised-learning methods requiring abundant labeled data. While few-shot learning (FSL) shows promise for industrial defect detection, existing approaches struggle with mixed-scene conditions (e.g., daytime and night-version scenes). In this work, we propose a crossed wavelet convolution network (CWCN), including a dual-pipeline crossed wavelet convolution training framework (DPCWC) and a loss value calculation module named ProSL. Our method innovatively applies wavelet transform convolution and prototype learning to industrial defect detection, which effectively fuses feature information from multiple scenarios and improves the detection performance. Experiments across various few-shot tasks on chip datasets illustrate the better detection quality of CWCN, with an improvement in mAP ranging from 2.76% to 16.43% over other FSL methods. In addition, experiments on an open-source dataset NEU-DET further validate our proposed method. Full article

Highly sensitive real-time detection of hydrogen sulfide (H₂S) is important for human health and environmental protection due to its highly toxic properties. The development of high-performance H₂S sensors remains challenging for poor selectivity, high limit detection and slow recovery from irreversible sulfidation. To solve these problems, we strategically prepared a layered structure of CuO-sensitized WO₃ flower-like hollow spheres with CuO as the sensitizing component. A 15 wt% CuO/WO₃ exhibits an ultra-high response (R_a/R_g = 571) to 10 ppm H₂S (131-times of pure WO₃), excellent selectivity (97-times higher than 100 ppm interference gas), and a low detection limit (100 ppb). Notably, its fast response (4 s) is accompanied by full recovery within 236 s. After 30 days of continuous testing, the response of 15 wt% CuO/WO₃ decreased slightly but maintained the initial response of 90.5%. The improved performance is attributed to (1) the p-n heterojunction formed between CuO and WO₃ optimizes the energy band structure and enriches the chemisorption sites for H₂S; (2) the reaction of H₂S with CuO generates highly conductive CuS, which significantly reduces the interfacial resistance; and (3) the hierarchical flowery hollow microsphere structure, heterojunction, and oxygen vacancy synergistically promote the desorption. This work provides a high-performance H₂S gas sensor that balances response, selectivity, and response/recovery kinetics. Full article

Encouraging discoveries and excellent advances in the fight against cancer have led to innovative therapies such as photothermal therapy (PTT), photodynamic therapy (PDT), drug targeting (DT), gene therapy (GT), immunotherapy (IT), and therapies that combine these treatments with conventional chemotherapy (CT). Furthermore, 2,041,910 new cancer cases and 618,120 cancer deaths have been estimated in the United States for the year 2025. The low survival rate (<50%) and poor prognosis of several cancers, despite aggressive treatments, are due to therapy-induced secondary tumorigenesis and the emergence of drug resistance. Moreover, serious adverse effects and/or great pain usually arise during treatments and/or in survivors, thus lowering the overall effectiveness of these cures. Although prevention is of paramount importance, novel anticancer approaches are urgently needed to address these issues. In the field of anticancer nanomedicine, carbon nanotubes (CNTs) could be of exceptional help due to their intrinsic, unprecedented features, easy functionalization, and large surface area, allowing excellent drug loading. CNTs can serve as drug carriers and as ingredients to engineer multifunctional platforms associated with diverse treatments for both anticancer therapy and diagnosis. The present review debates the most relevant advancements about the adjuvant role that CNTs could have in cancer diagnosis and therapy if associated with PTT, PDT, DT, GT, CT, and IT. Numerous sensing strategies utilising various CNT-based sensors for cancer diagnosis have been discussed in detail, never forgetting the still not fully clarified toxicological aspects that may derive from their extensive use. The unsolved challenges that still hamper the possible translation of CNT-based material in clinics, including regulatory hurdles, have been discussed to push scientists to focus on the development of advanced synthetic and purification work-up procedures, thus achieving more perfect CNTs for their safer real-life clinical use. Full article

Measuring pH is a critical parameter in environmental monitoring, biomedical diagnostics, food safety, and industrial processes. Optical fiber sensors have proven highly effective for pH detection due to their exceptional sensitivity, rapid response, and resistance to electromagnetic interference, making them well suited for real-time monitoring. This review offers a comprehensive analysis of recent advances in optical fiber-based pH sensors, covering key techniques such as fluorescence-based, absorbance-based, evanescent wave, and interferometric methods. Innovations in Fiber Bragg Grating and Surface Plasmon Resonance technologies are also examined. The discussion extends to the impact of pH-sensitive coatings—ranging from nanomaterials and polymeric films to graphene-based compounds—on enhancing sensor performance. Recent advancements have also enabled automation in data analysis and improvements in remote sensing capabilities. The review further compares the economic viability of optical fiber sensors with traditional electrochemical methods, while acknowledging persistent issues such as temperature cross-sensitivity, long-term stability, and fabrication costs. Overall, recent developments have broadened the functionality and application scope of these sensors by improving efficiency, accuracy, and scalability. Future research directions are outlined, including advanced optical interrogation techniques, such as Addressed Fiber Bragg Structures (AFBSs), microwave photonic integration, and optimized material selection. These approaches aim to enhance performance, reduce costs, and enable the broader adoption of optical fiber pH sensors. Full article

Flexible strain sensors, fabricated from high-elongation polymers and conductive filler particles, are proving an essential tool in the study of biomechanics using wearable technology. It has been previously shown that the resistive response of such composites, relative to the amount of conductive filler material, can be reasonably modeled using a standard percolation-type model. Once a certain critical fraction of filler material is reached, a conductive network across the sample is established and resistance rapidly decreases. However, modeling the more subtle resistance changes that occur while deforming the sensors during operation is more nuanced. Conductivity across the network of particles is dominated by tunneling mechanisms at the interfaces between the filler materials. Small changes in strain at these interfaces lead to relatively large, but nevertheless continuous, changes in local resistance. By assigning some arbitrary value of resistance as a dividing line between ‘low’ and ‘high’ resistance, one might model the piezoresistive behavior using a standard percolation model. But such an assumption is likely to lead to low accuracy. Our alternative approach is to divide the range of potential resistance values into several bins (rather than the usual two bins) and apply a relatively novel multi-state percolation theory. The performance of the multi-state percolation model is assessed using a random resistor model that is assumed to provide the ground truth. The model is applied to predict resistance response with both changes in relative amount of conductive filler (i.e., to help design the initial unstrained sensor) and with applied strain (for an operating sensor). We find that a multi-state percolation model captures the behavior of the simulated composite sensor in both cases. The multicomponent percolation theory becomes more accurate with more divisions/bins of the resistance distribution, and we found good agreement with the simulation using between 10 and 20 divisions. Full article

Traditional six-axis force sensors are mostly based on resistance strain, piezoelectricity and capacitors, which have poor resistance to electromagnetic interference. In this paper, a six-axis force sensor based on bending-sensitive optical fibers is proposed. A 3-UPU-(universal joint–prismatic joint–universal joint) compliant parallel mechanism is adopted in the sensor. The bending-sensitive optical fiber is encapsulated to form a fiber encapsulation module (FEM). The configuration of the FEMs within the six-axis force sensor is investigated. Static and stiffness analyses of the sensor are conducted and a force mapping matrix for the sensor is established. Simulation experiments are performed to verify the correctness of the established force mapping matrix. The detection system of the sensor is fabricated and the experiments are carried out to evaluate the performance of the sensor. The experiment results show that the maximum values of type-I errors and type-II errors are 4.52%FS and 3.26%FS, respectively. The maximum hysteresis and repeatability errors are 2.78% and 3.27%. These results verify the effectiveness of the proposed sensor. Full article

We present the numerical proof of a new sensor concept, based on the Resistive AC-Coupled Silicon Detectors (RSDs) paradigm and standard CMOS process, which benefits from having a 100% fill factor and embedded front-end electronics. The compatibility between these two technologies has been investigated, and our encouraging results suggest that this target could be reliably achieved, enabling the possibility to considerably boost the performance of current silicon detectors intended for timing and 4D-tracking. Full article

Orthotics are often used to support the metacarpophalangeal joint (MCPj) in horses recovering from soft tissue injury; however, their effect on the MCPj biomechanics remain largely underexplored. The MCPj moves primarily in the sagittal plane, flexing during the swing phase and extending during the stance phase. The suspensory ligament and flexor tendons act as biological springs resisting MCPj extension. Injuries to these structures are common and, although early mobilization promotes their healing, controlled loading may be beneficial during rehabilitation. This study aims to evaluate the efficacy of a semirigid orthotic in limiting the MCPj extension and the MCPj range of motion, and its influence on the MCPj kinematics. Twelve healthy horses were equipped with portable inertial sensors on the distal limb. The MCPj extension and the MCPj range of motion were assessed during walking and trotting without the orthotic (S0) and with the orthotic using two different support settings (S1 and S4). Data were evaluated for normality and homoscedasticity. A Student t-test was used to compare the MCPj angle pattern of the two forelimbs of each horse at the baseline. Data were analysed using one-way ANOVA to compare the mean values across conditions, followed by paired t-tests for post-hoc comparison (significance set at p < 0.05). The results showed significant reductions in both the MCPj extension and the MCPj range of motion, with the greatest restriction occurring at the highest support setting. These results suggest that the semirigid orthotic limits the MCPj movement in the sagittal plane and consequently the load on the suspensory ligament and flexor tendons. Therefore, this orthotic device is an effective tool during rehabilitation for forelimb tendon and ligament injuries. Full article