Search Results (457)

In this study, we report the synthesis of Te and Ag₂Te micron-sized rods (MRs) via a controlled hot-injection-based quenching process, enabling the control of rod morphology and enhanced crystallinity. Structural analysis confirmed that the synthesized Te MRs exhibit a trigonal phase, growing along the (110) direction, while Ag₂Te MRs undergo a phase transformation into a monoclinic structure upon Ag doping. A simple and scalable photodetector (PD) was fabricated by drop-casting Te and Ag₂Te MRs onto PET plastic films, followed by the application of Ag paste electrodes. The PD demonstrated room-light-induced photocurrent responses, which increased significantly upon mechanical bending due to the formation of additional conductive pathways between MRs. The Ag₂Te-based bending sensor exhibited a fivefold enhancement in photocurrent compared to its Te counterpart and maintained high stability over 1000 bending cycles. These results highlight the potential of Te and Ag₂Te MRs for use in flexible and wearable motion-sensing technologies, offering a simple yet effective approach for integrating 1D telluride nanostructures into scalable optoelectronic applications. Full article

This study explores virtual sensing techniques for the Eolink floating offshore wind turbine (FOWT), which features a pyramidal platform and a single-point mooring system that enables weathervaning to maximize power production and reduce structural loads. To address the challenges and costs associated with monitoring submerged components, virtual sensors are investigated as an alternative to physical instrumentation. The main objective is to design a virtual sensor of mooring hawser loads using a reduced set of input features from GPS, anemometer, and inertial measurement unit (IMU) data. A virtual sensor is also proposed to estimate the bending moment at the joint of the pyramid masts. The FOWT is modeled in OrcaFlex, and a range of load cases is simulated for training and testing. Under defined sensor sampling conditions, both supervised and physics-informed machine learning algorithms are evaluated. The models are tested under aligned and misaligned environmental conditions, as well as across operating regimes below- and above-rated conditions. Results show that mooring tensions can be estimated with high accuracy, while bending moment predictions also perform well, though with lower precision. These findings support the use of virtual sensing to reduce instrumentation requirements in critical areas of the floating wind platform. Full article

Shape-sensing optical fibers have become increasingly important in applications requiring flexible navigation, spatial awareness, and deformation monitoring. Fiber Bragg Grating (FBG) sensors inscribed in multi-core optical fibers have been democratized over the years and nowadays offer a compact and robust platform for shape reconstruction. In this work, we propose a novel, computationally efficient method for determining the 3D tip position of a bent multi-core FBG-based optical fiber using a second-order polynomial approximation of the fiber’s shape. The method begins with a calibration procedure, where polynomial coefficients are fitted for known bend configurations and subsequently modeled as a function of curvature using exponential decay functions. This allows for real-time estimation of the fiber tip position from curvature measurements alone, with no need for iterative numerical solutions or high processing power. The method was validated using miniaturized test structures and achieved sub-millimeter accuracy (<0.1 mm) over a 4.5 mm displacement range. Its simplicity and accuracy make it suitable for embedded or edge-computing applications in confined navigation, structural inspection, and medical robotics. Full article

Point-of-care (POC) diagnostic technologies have become essential for the real-time monitoring and management of chronic wounds, where maintaining a moist environment and controlling pH levels are critical for effective healing. In this study, a flexible pH sensor based on a graphene/molybdenum disulfide (graphene/MoS₂) composite interdigitated electrode (IDE) structure was fabricated using pulsed laser ablation. The pH sensor, with an active area of 30 mm × 30 mm, exhibited good adhesion to the polyethylene terephthalate (PET) substrate and maintained structural integrity under repeated bending cycles. Precise ablation was achieved under optimized conditions of 4.35 J/cm² laser fluence, a repetition rate of 300 kHz, and a scanning speed of 500 mm/s, enabling the formation of defect-free IDE arrays without substrate damage. The influence of laser processing parameters on the surface morphology, electrical conductivity, and wettability of the composite thin films was systematically characterized. The fabricated pH sensor exhibited high sensitivity (~4.7% change in current per pH unit) across the pH 2–10 range, rapid response within ~5.2 s, and excellent mechanical stability under 100 bending cycles with negligible performance degradation. Moreover, the sensor retained > 95% of its stable sensitivity after 7 days of ambient storage. Furthermore, the pH response behavior was evaluated for electrode structures with different pitches, demonstrating that structural design parameters critically impact sensing performance. These results offer valuable insights into the scalable fabrication of flexible, wearable pH sensors, with promising applications in wound monitoring and personalized healthcare systems. Full article

This study proposes a Fabry–Perot interferometric system and an associated evaluation method for measuring the weight of moving trains. An optical fiber sensor, comprising a sensing fiber and a supporting structure, is securely bonded to the rail foot. As a train traverses the track, the resulting localized bending induces a change in the sensing fiber’s length, which manifests as a quantifiable phase shift in the interference signal. We developed a physical–mathematical model, based on three Gaussian functions, to describe the temporal change in sensing fiber length caused by the passage of a single bogie. This model enables the determination of a proportionality constant to accurately convert the measured phase change into train weight. Model validation was performed using a train set, including a locomotive and four variably loaded wagons, traveling at 15.47 km/h. This system offers a novel and effective approach for real-time train weight monitoring. Full article

Stimulus-responsive hydrogels have broad applications in the biomedical, sensing, and actuation fields. However, conventional fabrication methods are often limited to 2D structures, hindering the creation of complex, personalized 3D hydrogel architectures. Furthermore, hydrogels responding to only a single stimulus and delays in fabrication techniques restrict their practical utility in biomedicine. In this study, we developed two novel multi-stimuli-responsive hydrogels (based on Gelatin/Sodium Alginate and Tannic Acid/EDTA-FeNa complexes) specifically designed for direct ink writing (DIW) 3D printing. We systematically characterized the printed properties and optimized component ratio, revealing sufficient mechanical strength (e.g., tensile modulus: Gel/SA-TA ≥ 0.22854 ± 0.021 MPa and Gel/SA-TA@Fe³⁺ ≥ 0.35881 ± 0.021 MPa), high water content (e.g., water absorption rate Gel/SA-TA ≥ 70.21% ± 1.5% and Gel/SA-TA@Fe³⁺ ≥ 64.86% ± 1.28%), excellent biocompatibility (e.g., cell viability: Gel/SA-TA and Gel/SA-TA@Fe³⁺ ≥ 90%) and good shape memory performance (e.g., the highest shape recovery rate of Gel/SA-TA reaches 74.85% ± 4.776%). Furthermore, we explored electrical characteristics, showing that the impedance value of Gel/SA-TA@Fe³⁺ hydrogel changes significantly under finger bending and NIR irradiation. This investigation demonstrates the potential of these 3D-printed multi-stimuli hydrogels for applications such as wearable flexible strain sensors. Full article

Wearable sensors have rapidly advanced, enabling applications such as human activity monitoring, electronic skin, and biomimetic robotics. To meet the growing demands of these applications, multifunctional sensing has become essential for wearable devices. However, most existing studies predominantly focus on enhancing single-function sensing capabilities. This study introduces a multifunctional sensor that combines high stretchability for strain and pressure detection with ultraviolet (UV) sensing capability. To achieve simultaneous detection of strain, pressure, and UV light, a multi-sensing approach was employed: a capacitive method for strain and pressure detections and a resistive method utilizing a pn-heterojunction diode for UV detection. In the capacitive method, polyaniline (PANI) served as parallel-plate electrodes, while silicon-based elastomer acted as the dielectric layer. This configuration enabled up to 100% elongation and enhanced operational stability through encapsulation. The sensor demonstrated a strong linear relationship between capacitance value changes reasonably based on the area of PANI, and showed a good linearity with an R-squared value of 0.9918. It also detected pressure across a wide range, from low (0.4 kPa) to high (9.4 kPa). Furthermore, for wearable applications, the sensor reliably captured capacitance variations during finger bending at different angles. For UV detection, a pn-heterojunction diode composed of p-type silicon and n-type zinc oxide nanorods exhibited a rapid response time of 6.1 s and an on/off ratio of 13.8 at −10 V. Durability under 100% tensile strain was confirmed through Von Mises stress calculations using finite element modeling. Overall, this multifunctional sensor offers significant potential for a variety of applications, including human motion detection, wearable technology, and robotics. Full article

Hand motion monitoring plays a vital role in medical rehabilitation, sports training, and human–computer interaction. High-sensitivity wearable biosensors are essential for accurate gesture recognition and precise motion analysis. In this work, we propose a high-sensitivity wearable glove based on a push–pull optical fiber sensor, designed to enhance the sensitivity and accuracy of hand motion biosensing. The sensor employs diagonal core reflectors fabricated at the tip of a four-core fiber, which interconnect symmetric fiber channels to form a push–pull sensing mechanism. This mechanism induces opposite wavelength shifts in fiber Bragg gratings positioned symmetrically under bending, effectively decoupling temperature and strain effects while significantly enhancing bending sensitivity. Experimental results demonstrate superior bending-sensing performance, establishing a solid foundation for high-precision gesture recognition. The integrated wearable glove offers a compact, flexible structure and straightforward fabrication process, with promising applications in precision medicine, intelligent human–machine interaction, virtual reality, and continuous health monitoring. Full article

Hysteresis is a nonlinear phenomenon found in many physical systems, including soft viscoelastic actuators, where it poses significant challenges to their application and performance. Consequently, developing accurate hysteresis models is essential for the effective design and optimization of soft actuators. Moreover, a reliable model can be used to design compensators that mitigate the negative effects of hysteresis, improving closed-loop control accuracy and expanding the applicability of soft actuators in robotics. Physics-based approaches for modeling hysteresis in soft actuators offer valuable insights into the underlying material behavior. Nevertheless, they are often highly complex, making them impractical for real-world applications. Instead, phenomenological models provide a more feasible solution by representing hysteresis through input–output mappings based on experimental data. To effectively fit these phenomenological models, it is essential to rely on sensing data collected from real actuators. In this context, the primary objective of this work is a comprehensive comparative evaluation of the efficiency and performance of representative phenomenological hysteresis models (e.g., Bouc–Wen and Prandtl-Ishlinskii) using experimental data obtained from a pneumatic bending actuator made of a viscoelastic material. This evaluation suggests that the Generalized Prandtl–Ishlinskii model achieves the highest modeling accuracy, while the Preisach model with a probabilistic density function formulation excels in terms of parameter compactness. Full article

Active or semi-active vibration control systems require real-time vibration information from controlled structures as feedback. However, integrating vibration sensors into some controlled structures remains a challenge due to factors such as mass and signal lines. This issue is particularly prominent in attachment structures located far from the spacecraft, such as robotic arms and solar panels. This paper presents a miniaturized autonomous inertial sensor that can be easily attached to the controlled structure to acquire vibration data and wirelessly transmit the data. We also establish the relationship between cantilevered structural vibration and the inertial acceleration or angular velocity directly measured by the sensor. Consequently, the feedback information for the control system can be calculated by the processor in real-time. This autonomous inertial sensor consists of an inertial measurement unit (IMU) named BMI088 and a common wireless communication unit. An improved Extended Kalman Filter (EKF) algorithm is employed to enhance the quality of the sensing data in practical environments. The experimental results validated the theoretical model, indicating that the miniaturized inertial sensor effectively captures the bending vibration characteristics of the controlled structure. Full article

Chemiresistive gas sensors have gained significant attention and have been widely applied in various fields. However, the gap between experimental observations and fundamental sensing mechanisms hinders systematic optimization. Despite the critical role of modeling in explaining atomic-scale interactions and offering predictive insights beyond experiments, existing reviews on chemiresistive gas sensors remain predominantly experimental-centric, with a limited systematic exploration of the modeling approaches. Herein, we present a comprehensive overview of the modeling approaches for chemiresistive gas sensors, focusing on two critical processes: the reception and transduction stages. For the reception process, density functional theory (DFT), molecular dynamics (MD), ab initio molecular dynamics (AIMD), and Monte Carlo (MC) methods were analyzed. DFT quantifies atomic-scale charge transfer, and orbital hybridization, MD/AIMD captures dynamic adsorption kinetics, and MC simulates equilibrium/non-equilibrium behaviors based on statistical mechanics principles. For the transduction process, band-bending-based theoretical models and power-law models elucidate the resistance modulation mechanisms, although their generalizability remains limited. Notably, the finite element method (FEM) has emerged as a powerful tool for full-process modeling by integrating gas diffusion, adsorption, and electronic responses into a unified framework. Future directions highlight the use of multiscale models to bridge microscopic interactions with macroscopic behaviors and the integration of machine learning, accelerating the iterative design of next-generation sensors with superior performance. Full article

This study advances the development of self-sensing concrete through functionalization with carbon nanotubes (CNTs) for structural health monitoring. Through experimental analyses, it relies on its dual responsiveness to mechanical and thermal stimuli. Three-point bending and thermal tests were systematically conducted on concrete samples with CNT concentrations ranging from 0 to 0.05 wt.% of cement, evaluated at 7- and 28-day curing periods. Mechanical testing demonstrated curing-dependent behavior: At 7 days, mechanical strength and electrical current response exhibited pronounced variability across CNTs loadings, with optimal balance achieved at 0.01% CNTs. At 28 days, the tests show that the mechanical properties are relatively stabilized, reaching the highest value at 0.006 wt.% CNTs and achieving the best electrical sensitivity at 0.01 wt.% CNTs. The thermal experiments revealed faster current modulation in the 7-day samples than in the 28-day counterparts, with intermediate CNT concentrations (e.g., 0.01 wt.%) showing a more sensitive response. The sensitivity was analyzed for both mechanical and thermal changes to further evaluate the feasibility of using CNT-reinforced concrete as a sensor material. Conductivity measurements on fully cured samples indicated that all samples exhibited electrical conductivities in the 10⁻⁴ S/m range, suggesting semiconductive behavior, while 0.006 wt.% CNTs yielded the highest conductivity. Higher CNT content did not further improve conductivity, likely due to agglomeration disrupting the network. These findings confirm CNT-modified concrete’s dual electromechanical and thermal responsiveness and support its potential as a multifunctional sensing material. Full article

The development of ultraflexible and sensitive gas sensors is critical for advancing next-generation environmental monitoring and healthcare diagnostics. In this work, we demonstrate an ultraflexible chemiresistive nitrogen dioxide (NO₂) sensor integrated with a photopatterned porous poly(3-hexylthiophene) (P3HT)/SU-8 blend film as an active sensing layer. The porous microarchitecture was fabricated via high-resolution photolithography, utilizing SU-8 as a photoactive porogen to template a uniform, interconnected pore network within the P3HT matrix. The engineered porosity level ranged from 0% to 36%, substantially improving gas diffusion kinetics to enlarge the accessible surface area for analyte adsorption. Our sensor exhibited a marked enhancement in sensitivity at an optimized porosity of 36%, with the current response at 30 ppm NO₂ increasing from 354% to 3201%, along with a detection limit of 0.7 ppb. The device further exhibited a high selectivity against common interfering gases, including NH₃, H₂S, and SO₂. Moreover, the porous structure imparted excellent mechanical durability, maintaining over 90% of its initial sensing performance after 500 bending cycles at a 1 mm radius, underscoring its potential for integration into next-generation wearable environmental monitoring platforms. Full article

Under low-temperature conditions, the load-bearing capacity of piezoelectric stacks arises from coupled thermo-electro-mechanical interactions, with temperature fluctuations, compressive prestress, and excitation voltage critically modulating performance. This study introduces an integrated measurement platform to systematically quantify these interdependencies, employing a cantilever-based sensing mechanism where bending strain serves as a direct metric of load-bearing capacity. A particle swarm-optimized theoretical framework guides the spatial configuration of actuators and sensors, maximizing strain signal fidelity while suppressing noise interference. Experimental characterization reveals three key findings: 1. Voltage-dependent linear enhancement of load-bearing capacity across all operational regimes, unaffected by thermal or mechanical variations; 2. Prestress-induced amplification (79–90% increase from 0 to 6 MPa) and thermally driven attenuation (15–30% reduction from 20 to −70 °C) of static performance; 3. Frequency-dependent degradation (1–6 Hz) in dynamic load-bearing capacity. The methodology establishes a robust foundation for designing multiphysics-compatible instrumentation systems, enabling precise evaluation of smart material behavior under extreme coupled-field conditions. Full article

Two different magnetoelastic Metglas materials with distinct shapes were compared as sensing elements for the structural health monitoring of concrete beams. One had a ribbon shape, while the other had a microwire shape. The sensing elements were attached to different concrete beams, and a crack was introduced into each beam. The beams were subjected to flexural vibrations, and their deformations were recorded wirelessly by coils, detecting the magnetic signals emitted due to the magnetoelastic nature of the sensors. Fast Fourier Analysis of the received signal revealed the bending mode frequencies of the beams, which serve as a “signature” of their structural health. In these spectra, the ribbon-shaped sensor exhibited a 1.4-times stronger signal than the microwire sensor. However, the extracted mode frequencies were nearly identical, with differences of less than 1% both before and after damage. This indicates that both sensors can be used equivalently to monitor structural damage in concrete beams. The damage-related relative frequency shifts ranged from −0.01 to −0.03, with similar results for both sensors. Thermal annealing was also studied and appeared to significantly enhance the signal by 10–30%, likely due to the relaxation of internal stresses induced during the rapid solidification synthesis of these materials. This enhancement was more pronounced in the ribbon-shaped sensor. This study is the first to utilize a magnetoelastic microwire sensor for damage detection in concrete beams. Full article