Search Results (5,848)

In industrial combustion processes, high concentrations of propane (C₃H₈) gas are employed. Therefore, developing gas-detecting devices that operate under high concentrations, elevated temperatures, and short response times is crucial. This paper presents the design, simulation, and construction of a novel propane (C₃H₈) gas detector. The design was based on the dynamic electrical response of a gas sensor fabricated with cobalt antimoniate (CoSb₂O₆). The simulation considered the device structure and programming criteria, and the final prototype was constructed according to the sensor response, design parameters, and operating principles. Design, simulation, and fabrication results were in concordance, confirming the correct operation of the detector at high gas concentrations. A mathematical model was derived from the sensor’s electrical response, establishing a resistance value that allowed a two-second response time. This resistance was used to adapt the signal between the gas sensor and the PIC18F2550 microcontroller. Input/output signals, safety criteria, and functionality principles were considered in the programming device. The resulting propane (C₃H₈) gas detector operates at 300 °C, detects high C₃H₈ concentrations, and achieves a 2 s response time, making it ideal for industrial applications where combustion monitoring is essential. Full article

The operational reliability of Insulated Gate Bipolar Transistor (IGBT) modules in demanding transportation applications, such as traction systems, is critically challenged by solder layer and bond wire failures under cyclic thermal stress. To address this, this paper proposes a novel health monitoring framework that innovatively synergizes micro-scale spatial thermal analysis with microsecond electrical dynamics inversion. The method requires only non-invasive temperature measurements on the module baseplate and utilizes standard electrical signals (load current, duty cycle, switching frequency, DC-link voltage) readily available from the converter’s controller, enabling simultaneous diagnosis without dedicated voltage or high-bandwidth current sensors. First, a non-invasive assessment of solder layer fatigue is achieved by correlating the normalized thermal gradient ($\nabla T_{\mathrm{P}}$) on the baseplate with the underlying thermal impedance ($Z_{\mathrm{JC}}$). Second, for bond wire aging, a cost-effective inversion algorithm estimates the on-state voltage ($V_{\mathrm{ce},\mathrm{on}}$) by calculating the total power loss from temperature, isolating the conduction loss ($P_{\mathrm{cond}}$) with the aid of a Foster-model-based junction temperature ($T_{\mathrm{J}}$) estimate, and finally computing $V_{\mathrm{ce},\mathrm{on}}$ at a unique current inflection point ($I_{\mathrm{C},\inf }$) to nullify $T_{\mathrm{J}}$ dependency. Third, the health states from both failure modes are fused for comprehensive condition evaluation. Experimental validation confirms the method’s accuracy in tracking both degradation modes. This work provides a practical and economical solution for online IGBT condition monitoring, enhancing the predictive maintenance and operational safety of transportation electrification systems. Full article

Conductive hydrogels that simultaneously exhibit high mechanical robustness, reliable electrical conductivity, and interfacial adhesion are highly desirable for flexible sensing applications; however, achieving these properties in a single system remains challenging due to intrinsic structure–property trade-offs. Herein, a multifunctional conductive hydrogel (ASP hydrogel) is developed based on a polyacrylamide (PAM)/sodium alginate (SA) double-network architecture using a gallic acid (GA)–Fe³⁺–pyrrole (Py) coupling strategy. In this design, GA provides metal-coordination sites for Fe³⁺, while Fe³⁺ simultaneously serves as an oxidant to trigger the in situ polymerization of pyrrole, enabling the homogeneous integration of polypyrrole (PPy) conductive networks within the hydrogel matrix. The resulting ASP hydrogel exhibits a markedly enhanced fracture strength of 2.95 MPa compared with PAM (0.26 MPa) and PAM–SA (0.22 MPa) hydrogels, together with stable electrical conductivity and reproducible strain-dependent electrical responses. Moreover, the introduction of dynamic metal–phenolic coordination and hydrogen-bonding interactions endows the hydrogel with intrinsic self-healing capability and strong adhesion to diverse substrates. Rather than relying on simple filler incorporation, this work demonstrates an integrated network design that balances mechanical strength, conductivity, and adhesion, providing a versatile material platform for flexible strain sensors and wearable electronics. Full article

This paper analyzes microstructural layout and electrical behavior of silicon nanowire-based Schottky diodes, for use as wide-domain temperature sensors. The employed nanostructured three-dimensional substrates provide larger contact areas and enable higher Schottky barrier heights, ultimately leading to a better operable temperature range. Two metal deposition techniques (Radio Frequency sputtering and Electron-beam evaporation) are used to fabricate experimental Schottky diode samples. Scanning electron microscopy, X-ray diffraction, and diffuse reflectance investigations are carried out in order to determine nanowire distribution and the influence of subsequent metal deposition. The analyses evince the formation of a slightly inhomogeneous contact. The findings are validated by a thorough electrical characterization over a wide temperature domain. Inhomogeneity models are used in order to determine the main device parameters and the bias regions where they can be used as precise temperature sensors. The sputtered sample exhibits the best sensitivity, between 1 and 1.4 mV/K, while excellent linearity (R2 > 99.5%) is obtained for Electron-beam evaporated devices. Both types of silicon nanowire-based Schottky diode sensors have 100–500K operable ranges, much larger than planar counterparts. Full article

The rapid evolution of smart transportation systems necessitates the integration of advanced sensing technologies capable of supporting the real-time, reliable, and cost-effective monitoring of road infrastructure. Fiber-optic sensor (FOS) technologies, given their high sensitivity, immunity to electromagnetic interference, and suitability for harsh environments, have emerged as promising tools for enabling intelligent transportation infrastructure. This review critically examines the current landscape of classical mechanical and electrical sensor realization in monitoring solutions. Focus is also given to fiber-optic-sensor-based solutions for smart road applications, encompassing both well-established techniques such as Fiber Bragg Grating (FBG) sensors and distributed sensing systems, as well as emerging hybrid sensor networks. The article examines the most topical physical parameters that can be measured by FOSs in road infrastructure monitoring to support traffic monitoring, structural health assessment, weigh-in-motion (WIM) system development, pavement condition evaluation, and vehicle classification. In addition, strategies for FOS integration with digital twins, machine learning, artificial intelligence, quantum sensing, and Internet of Things (IoT) platforms are analyzed to highlight their potential for data-driven infrastructure management. Limitations related to deployment, scalability, long-term reliability, and standardization are also discussed. The review concludes by identifying key technological gaps and proposing future research directions to accelerate the adoption of FOS technologies in next-generation road transportation systems. Full article

Field-effect transistor (FET) biosensors enable label-free and real-time electrical transduction; however, their practical deployment is often constrained by the need for bulky benchtop instrumentation to provide stable biasing, low-noise readout, and data processing. Here, we report a portable extended-gate FET (EG-FET) integrated sensing system that consolidates the sensing interface, analog front-end conditioning, embedded acquisition/control, and user-side visualization into an end-to-end prototype suitable for on-site operation. The system couples a screen-printed Au extended-gate electrode to a MOSFET and employs a low-noise signal-conditioning chain with microcontroller-based digitization and real-time data streaming to a host graphical interface. As a proof-of-concept, enterohemorrhagic Escherichia coli O157:H7 was selected as the target. A bacteria-specific immunosensing interface was constructed on the Au extended gate via covalent immobilization of monoclonal antibodies. Measurements in buffered samples produced concentration-dependent current responses, and a linear calibration was experimentally validated over 10⁴–10¹⁰ CFU/mL. In specificity evaluation against three common foodborne pathogens (Staphylococcus aureus, Salmonella typhimurium, and Listeria monocytogenes), the sensor showed a maximum interference response of only 13% relative to the target signal (ΔI/ΔImax) with statistical significance (p < 0.001). Our work establishes a practical hardware–software architecture that mitigates reliance on benchtop instruments and provides a scalable route toward portable EG-FET sensing for rapid, point-of-need detection of foodborne pathogens and other biomarkers. Full article

Graphene oxide (GO) and reduced graphene oxide (rGO) have solidified their role as cornerstone nanomaterials in the pursuit of sustainable technology. This review synthesizes recent advances in harnessing the unique properties of GO and rGO such as their tunable surface chemistry and exceptional electrical conductivity for applications spanning environmental remediation and energy storage. In the environmental domain, they function as superior adsorbents and catalysts for the removal of hazardous pollutants. Concurrently, in the energy sector, their integration into supercapacitors and battery electrodes significantly enhances energy and power density. The adaptability of these materials also facilitates the creation of highly sensitive sensors and biosensors. However, the transition from laboratory research to widespread industrial application is hindered by challenges in scalable production, environmental health and safety concerns, and long-term stability. This review enhances the understanding of GO and rGO’s diverse applications and paves the way for future sustainable technologies in energy and environmental sectors. Full article

Orchard wind-assisted spraying technology relies on auxiliary airflow to disturb the canopy and improve droplet deposition uniformity. However, there are few effective means of quantitatively assessing the dynamic response of fruit tree leaves to airflow or the changes in airflow patterns within the canopy in real time. To address this, this study proposed an online monitoring method for the aerodynamic characteristics of fruit tree leaves using strain gauge sensors. The flexible strain gauge was affixed to the midribs of leaves from peach, pear and apple trees. Leaf deformations were captured with high-speed video recording (100 fps) alongside electrical signals in controlled wind fields. Bartlett low-pass filtering and Fourier transform were used to extract frequency-domain features spanning between 0 and 50 Hz. The AdaBoost decision tree model was used to evaluate classification performance across frequency bands. The results demonstrated high accuracy in identifying wind exposure (98%) for pear leaf and classifying the three leaf types (κ = 0.98) within the 4–6 Hz band. A comparison with the frame analysis of high-speed video recordings revealed a time error of 2 s in model predictions. This study confirms that strain gauge sensors combined with machine learning could efficiently monitor fruit tree leaf responses to external airflow in real time. It provides novel insights for optimizing wind-assisted spray parameters, reconstructing internal canopy wind field distributions and achieving precise pesticide application. Full article

The digitalization of machining processes is increasingly recognized as essential for achieving higher productivity, reliability, and traceability. However, access to reliable in-process sensor data remains limited, particularly in multi-axis CNC machining, where dimensional accuracy and surface integrity strongly depend on stable and optimized process conditions. This study investigates sensor-based monitoring as a practical approach for evaluating process performance in five-axis CNC milling. Electric current and vibration signals were acquired during three machining operations, under distinct cutting parameters, using current clamps and a plug-and-play MEMS accelerometer. The signals were processed using the root mean square method to assess the correlation between sensor data and machining conditions. Dimensional inspection of each workpiece was carried out to verify geometric conformity. The results show that spindle current measurements exhibit a strong linear correlation with material removal rate and cutting power, supporting their use as indicators of cutting forces and energy consumption. Vibration signals revealed pronounced dynamic behaviour for specific tool orientations, particularly in transverse to tool axis direction. The proposed methodology provides a simple and low-cost framework for integrating sensor-based monitoring into five-axis CNC milling, particularly relevant for semi-roughing operations, and offers a basis for future studies on process optimization and real-time condition monitoring. Full article

It has long been desired to achieve mechanical enhancement and structural health monitoring by introducing carbon nanotubes (CNTs) into traditional carbon fiber (CF) composites. Herein, the initiation of micro-damage and crack propagation has been investigated by utilizing in situ electrical resistance changes in interlaminar hybrid CNT network/CF composites during the shear loading process. The results show a clear relationship between the crack propagation and the electrical resistance response particularly when approaching the failure of the single-layer CNT network hybrid composites. Furthermore, the chemically modified CNT network exhibits evident enhancement on main mechanical properties of the CF composites, superior to the thermoplastic toughening method. The characterizations manifest that the multiscale interlayered CNT/CF structure can simultaneously resist the crack propagation along both the in-plane direction and the cross-plane direction, which consequently enhances the flexural and compressive strengths of the composite material. This discovery provides a novel idea for the potential application of CNT network/CF hybrid composites in the integration of mechanical reinforcement and structural health monitoring, namely, that the CNT network acts not only as a reinforcing phase but also as a sensor for the structural health monitoring of the composites. Full article

Ionic polymer–metal composite (IPMC) sensors generate voltages or currents when subjected to deformation. The magnitude and time constant of the electrical response vary significantly with ambient humidity and water content. However, most conventional physical models focus solely on cation dynamics and do not consider water dynamics. In addition to cation dynamics, Zhu’s model explicitly incorporates the dynamics of water. Consequently, Zhu’s model is considered one of the most promising approaches for physical modeling of IPMC sensors. This paper presents exact analytical solutions to Zhu’s model of IPMC sensors for the first time. The derivation method transforms Zhu’s model into the frequency domain using Laplace transform-based analysis together with linear approximation, and subsequently solves it as a boundary value problem of a set of linear ordinary differential equations. The resulting solution is expressed as a transfer function. The input variable is the applied bending deformation, and the output variables include the open-circuit voltage or short-circuit current at the sensor terminals, as well as the distributions of cations, water molecules, and electric potential within the polymer. The obtained transfer functions are represented by irrational functions, which typically arise as solutions to a system of partial differential equations. Furthermore, this paper presents analytical approximations of the step response of the sensor voltage or current by approximating the obtained transfer functions. The steady-state and maximum values of the time response are derived from these analytical approximations. Additionally, the relaxation behavior of the sensor voltage is characterized by a key parameter newly derived from the analytical approximation presented in this paper. Full article

Surface plasmon resonance (SPR) sensors based on photonic crystal fibers (PCFs) hold significant promise for high-precision detection in biochemical and chemical sensing. However, achieving high sensitivity in low-refractive-index (RI) aqueous environments remains a formidable challenge due to weak light-matter interactions. To address this limitation, this paper designs and proposes a novel dual-channel D-shaped PCF-SPR sensor tailored for the refractive index range of 1.34–1.40. The sensor incorporates a dual-layer gold/titanium dioxide film, with gold nanoparticles deposited on the surface to synergistically enhance both propagating and localized surface plasmon resonance effects. Furthermore, a D-shaped polished structure integrated with double-sided microfluidic channels is employed to significantly strengthen the interaction between the guided-mode electric field and the analyte. Finite element method simulations demonstrate that the proposed sensor achieves an average wavelength sensitivity of 5733 nm/RIU and a peak sensitivity of 15,500 nm/RIU at a refractive index of 1.40. Notably, the introduction of gold nanoparticles contributes to an approximately 1.47-fold sensitivity enhancement over conventional structures. This work validates the efficacy of hybrid plasmonic nanostructures and optimized waveguide design in advancing RI sensing performance. Full article

A flexible piezoresistive material based on vulcanized natural rubber (VNR) and multiwalled carbon nanotubes (MWCNTs) was developed and systematically investigated for strain sensing applications. The nanocomposites were prepared by melting and vulcanizing MWCNT, while keeping the rubber composition constant to isolate the effect of the conductive nanofiller. By scanning electron microscopy, morphological analyses indicated that MWCNTs were dispersed throughout the rubber matrix, with localized agglomerations becoming more evident at higher loadings. In mechanical tests, MWCNT incorporation increases the tensile strength of VNR, increasing the stress at break from 8.84 MPa for neat VNR to approximately 10.5 MPa at low MWCNT loadings. According to the electrical characterization, VNR-MWCNT nanocomposite exhibits a strong insulator–conductor transition, with the electrical percolation threshold occurring between 2 and 4 phr. The dc electrical conductivity increased sharply from values on the order of 10⁻¹⁴ S·m⁻¹ for neat VNR to approximately 10⁻³ S·m⁻¹ for nanocomposites containing 7 phr of MWCNT. Impedance spectroscopy revealed frequency-independent conductivity plateaus above the percolation threshold, indicating continuous conductive pathways, while dielectric analysis revealed strong interfacial polarization effects at the MWCNT–VNR interfaces. The piezoresistive response of samples containing MWCNT exhibited a stable, reversible, and nearly linear response under cyclic tensile deformation (10% strain). VNR/MWCNT nanocomposites demonstrate mechanical compliance and tunable electrical sensitivity, making them promising candidates for flexible and low-cost piezoresistive sensors. Full article

Background: Neuromuscular electrical stimulation (NMES) is an effective exogenous neuromuscular activation method widely used in sports training and rehabilitation. However, existing research primarily focuses on land-based sports or single-joint movements, with limited in-depth exploration of its intervention effects and underlying neuromuscular control mechanisms for complex, multi-joint coordinated aquatic activities like breaststroke swimming. This study aimed to investigate the effects of NMES combined with traditional resistance training on neuromuscular function during sport-specific technical movements in breaststroke athletes. Methods: A randomized controlled trial was conducted with 30 national-level or above breaststroke athletes assigned to either an experimental group (NMES combined with traditional squat resistance training) or a control group (traditional squat resistance training only) for an 8-week intervention. A specialized fully waterproof wireless electromyography (EMG) sensor system (Mini Wave Infinity Waterproof) was used to synchronously collect surface EMG signals from 10 lower limb and trunk muscles during actual swimming, combined with high-speed video for movement phase segmentation. Changes in lower limb explosive power were assessed using a force plate. Non-negative matrix factorization (NMF) muscle synergy analysis was employed to compare changes in muscle activation levels (iEMG, RMS) and synergy patterns (spatial structure, temporal activation coefficients) across different phases of the breaststroke kick before and after the intervention. Results: Compared to the control group, the experimental group demonstrated significantly greater improvements in single-leg jump height (Δ = 0.06 m vs. 0.03 m) and double-leg jump height (Δ = 0.07 m vs. 0.03 m). Time-domain EMG analysis revealed that the experimental group showed more significant increases in iEMG values for the adductor longus, adductor magnus, and gastrocnemius lateralis during the leg-retraction and leg-flipping phases (p < 0.05). During the pedal-clamp phase, the experimental group exhibited significantly reduced activation of the tibialis anterior alongside enhanced activation of the gastrocnemius. Muscle synergy analysis indicated that post-intervention, the experimental group showed a significant increase in the weighting of the vastus medialis and biceps femoris within synergy module 4 (SYN4, related to propulsion and posture) (p < 0.05), a significant increase in rectus abdominis weighting within synergy module 3 (SYN3, p = 0.033), and a significant shortening of the activation duration of synergy module 2 (SYN2, p = 0.007). Conclusions: NMES combined with traditional resistance training significantly enhances land-based explosive power in breaststroke athletes and specifically optimizes neuromuscular control strategies during the underwater breaststroke kick. This optimization is characterized by improved activation efficiency of key muscle groups, more economical coordination of antagonist muscles, and adaptive remodeling of inter-muscle synergy patterns in specific movement phases. This study provides novel evidence supporting the application of NMES in swimming-specific strength training, spanning from macroscopic performance to microscopic neural control. Full article

Accurate determination of a vehicle’s centre of gravity (CoG) is fundamental to driving dynamics, safety, and engineering design. However, existing static CoG estimation methods often neglect tyre deflection and detailed wheel geometry, which can introduce significant errors, particularly in electric vehicles, where the low and concentrated mass of the battery pack increases the sensitivity of vertical CoG calculations. This study presents a refined static axle-load-based method for electric vehicles, in which the influence of tyre deformation and lifting height on the accuracy of the vertical centre of gravity coordinate is explicitly considered and quantitatively justified. To minimise human error and accelerate the evaluation process, a custom-developed Python (Python 3.13.2.) software tool automates all calculations, provides an intuitive graphical interface, and generates visual representations of the resulting CoG position. The methodology was validated on a Volkswagen e-Golf, demonstrating that the proposed approach provides reliable and repeatable results. Due to its accuracy, reduced measurement complexity, and minimal equipment requirements, the method is suitable for design, educational, and diagnostic applications. Moreover, it enables faster and more precise preparation of vehicle dynamics tests, such as rollover assessments, by ensuring that sensor placement does not interfere with vehicle behaviour. Full article