Search Results (4,548)

Lycium barbarum L. is a widely used medicinal and edible Chinese medicinal material. However, with consumers’ heightened concern for health and food safety, pesticide residues have become one of the major challenges affecting its quality and safety. Cyhalothrin is a pyrethroid insecticide and a typical type of pesticide with excessive pesticide residues in Lycium barbarum L. Rapid detection of pesticide residues is an effective way to ensure the quality and safety of traditional Chinese medicinal materials. In this work, a molecularly imprinted polymer electrochemiluminescence (ECL) sensor based on gold nanoparticles (AuNPs)@Ru-ZIF-8 was constructed for rapid detection of cyhalothrin residues. The prepared cyhalothrin molecularly imprinted polymers (MIPs) were used as a recognition element and modified on the surface of a glassy carbon electrode (GCE) by an electrochemical polymerization method. AuNPs were utilized to promote the excitation of Ru(bpy)₃²⁺ and TPrA in the ECL system, which improved the observability of the light signal. The GCE modified with the metal–organic frameworks (MOFs) ZIF-8 was employed to increase the specific surface area and enhance the electron transfer capacity on the electrode, thereby improving the sensing sensitivity of the sensor. In addition, the luminescent reagent of Ru(bpy)₃²⁺ was introduced into the synthesis process of ZIF-8, which caused Ru(bpy)₃²⁺ to be tightly bound around it and enhanced the stability of the sensor. Under optimal conditions, the linear detection range of the sensor is 1 × 10⁻¹~1 × 10⁴ nM, with a limit of detection (LOD) of 10 pM. The accuracy of the ECL-MIP sensor has been verified through spiked recovery experiments and actual sample detection. This study has opened up a new approach to rapid detection of pesticide residues in traditional Chinese medicinal materials used for both food and medicine. Full article

Accurate and rapid prediction of sensor responses is critical for real-time measurement systems, digital twin implementations, and sensor design optimization. Traditional numerical methods such as Finite Element Method (FEM) and Computational Fluid Dynamics (CFD) provide high-fidelity solutions but suffer from prohibitive computational costs, limiting their applicability in time-sensitive applications. This paper presents a novel framework utilizing Fourier Neural Operators (FNO) as surrogate models for fast multi-physics sensor response prediction across thermal, acoustic, and flow measurement domains. Unlike conventional neural networks that learn finite-dimensional mappings, FNO learns operators between infinite-dimensional function spaces by parameterizing the integral kernel in Fourier space, enabling resolution-invariant predictions with remarkable computational efficiency. We demonstrate the framework’s efficacy through three comprehensive case studies: (1) thermal sensor response prediction achieving $R^2>0.98$ with 8300× speedup over FEM, (2) acoustic sensor array modeling with mean absolute error below 0.5 dB and 4000× speedup over BEM, and (3) flow sensor characterization with velocity field prediction accuracy exceeding 97% and 31,000× speedup over CFD. The proposed FNO-based surrogate models are trained on simulation datasets generated from high-fidelity numerical solvers and validated against simulation holdout data for all three case studies, with additional experimental validation conducted for the thermal sensor case. Results indicate that FNO architectures effectively capture the underlying physics governing sensor behavior while reducing inference time from minutes to milliseconds. The framework enables real-time sensor calibration, uncertainty quantification, and design optimization, opening new possibilities for intelligent measurement systems and Industry 4.0 applications. We also investigate the spectral characteristics of FNO predictions, addressing the inherent low-frequency bias through a hybrid architecture combining FNO with local convolutional layers. The primary contributions of this work include: (1) the first systematic application of FNO-based surrogate modeling specifically tailored for sensor response prediction across multiple physics domains, (2) a novel H-FNO architecture that combines spectral operators with local convolutions to mitigate spectral bias in sensor applications, and (3) comprehensive validation including both simulation and experimental data for practical deployment. This work establishes FNO as a powerful tool for accelerating sensor simulation and advancing the field of AI-enhanced instrumentation and measurement. Full article

We present an optical deformation sensor additively manufactured via an embedded printing process that enables the direct integration of colloidal quantum dots into multimode silicone (PDMS) waveguides. The sensor consists of two parallel waveguide strands, one of which is locally functionalized with CdSe/CdS quantum dots serving as fluorescent emitters. When narrow-band UV light at 405 nm is coupled into the non-functionalized strand, structural deformation alters the conditions of total internal reflection, thereby changing the optical interaction between both strands. This leads to a deformation-dependent variation in the fluorescence shift-affected intensity ratio, which serves as a self-referenced signal for angle determination. Using ratiometric evaluation, angular deflections of up to 9.5° are detected with a resolution below 1° ($2\sigma$ confidence), representing the performance of an initial functional prototype. The embedded printing process allows the voxel-wise adjustment of the material composition within a viscoplastic support medium and thus the spatially resolved integration of quantum dot-functionalized silicone. Attenuation losses of $0.81\pm 0.02\mathrm{dB}/\mathrm{cm}$ at 625 nm confirm the optical suitability of the printed waveguides. This approach combines optical sensing and structural flexibility within a single manufacturing step and establishes a pathway toward fully integratable deformation-sensing elements for soft robotic and wearable systems. Full article

Chain breakage in dual-chain scraper conveyors poses significant risks to the safe and efficient operation of coal mines. To address the challenges of harsh underground environments and the lack of effective synchronization monitoring, this paper presents the design and implementation of an intelligent monitoring system for conveyor integrity. The system integrates non-contact Hall-effect sensors with a custom-designed intrinsically safe data acquisition unit. A systematic algorithmic framework is designed, comprising an adaptive threshold and plateau seeking (ATPS) module and an adaptive clustering-based identification (ACCI) module, to enable high-accuracy automatic identification of chain elements. Furthermore, a novel synchronization evaluation design based on event correlation and statistical features is introduced to quantify inter-chain timing deviations. This leads to the construction of a Chain Synchronization Index (CSI) for desynchronization anomaly detection. Field experiments conducted under representative operating conditions, including normal operation and controlled single-chain disconnection scenarios, demonstrate that the proposed design achieves a chain element recognition accuracy of 98.2%. Under normal conditions, the CSI remains consistently high, while breakage faults are sensitively detected. The proposed system provides a practical engineering solution for synchronization-aware condition monitoring and anomaly warning of scraper conveyor chains in underground coal mines. Full article

Electrochemical biosensors have emerged as indispensable detection tools with rapid advancements in recent years, offering high sensitivity, specificity, and cost-effectiveness for quantifying diverse analytes, including amino acids, proteins, pathogens, cells, antigens, and organic/inorganic compounds, thereby advancing analytical detection technologies across multiple fields. Aptamers, synthetic in vitro-evolved ligands with exceptional binding affinity and stability, serve as superior biorecognition elements for electrochemical sensing interfaces. Compared with other bioreceptors such as antibodies, they are generally easier and faster to produce, more uniform between batches, and easier to modify chemically; they also maintain greater stability than protein antibodies or enzymes across varying pH, temperature, and ionic conditions, enabling targeted recognition and measurable signal transduction. This review systematically summarizes recent advances in electrochemical aptasensors across three core domains: biomedical diagnostics (covering tumor markers, infectious disease pathogens, cardiovascular and metabolic biomarkers), food safety monitoring (targeting antibiotics, mycotoxins, foodborne pathogens, and pesticide residues), and environmental hazard detection (including heavy metals, toxic compounds, and biotoxins). Key technological innovations such as nanomaterial modification, signal amplification strategies, and novel sensor architectures are highlighted. Additionally, it critically discusses prominent challenges, including complex matrix interference, limited aptamer repertoires, poor reproducibility, and lack of standardization, along with future prospects. This work aims to provide a comprehensive reference for the rational design, optimization, and clinical/field application of next-generation electrochemical aptasensing technologies. Full article

Two-dimensional graphitic carbon nitride 2D g-C₃N₄ has the potential for gas sensing as a metal-free semiconductor with a layered structure, high surface area, and tunability of electronic properties. In this context, 2D g-C₃N₄ nanosheets were prepared by the thermal polycondensation of urea followed by ultrasonic exfoliation. X-ray diffraction revealed diffraction peaks corresponding to the (110) and (002) crystallographic planes of g-C₃N₄. Scanning electron microscopy showed a nanosheet structure with a 10-nm crystallite size, while energy-dispersive X-ray spectroscopy demonstrated a uniform distribution of carbon and nitrogen. Ultraviolet–visible absorption spectroscopy revealed a band gap of 2.8 eV. Gas sensing measurements exhibited an increase in response to isopropanol and ethanol as the operating temperature and gas concentration increased. Impedance spectroscopy provided additional insight into the sensing mechanism. Observed depressed semicircles in Nyquist plots were fitted with a charge transfer resistance R_ct in parallel with a constant phase element model. The charge transfer resistance R_ct fell systematically with isopropanol exposure, confirming the crucial role of adsorption-induced electron transfer in the gas sensing response. Full article

Deafness poses significant challenges to effective communication, particularly in contexts where access to sign language interpreters is limited. Hand configuration recognition represents a fundamental component of sign language understanding, as configurations constitute a core cheremic element in many sign languages, including Italian Sign Language (LIS). In this work, we address configuration-level recognition as an independent classification task and propose a machine vision framework based on RGB-D sensing. The proposed approach combines MediaPipe-based hand landmark extraction with normalized three-dimensional geometric features and a Support Vector Machine classifier. The first contribution of this study is the formulation of LIS hand configuration recognition as a standalone, configuration-level problem, decoupled from temporal gesture modeling. The second contribution is the integration of sensor-acquired RGB-D depth measurements into the landmark-based feature representation, enabling a direct comparison with estimated depth obtained from monocular data. The third contribution consists of a systematic experimental evaluation on two LIS configuration sets (6 and 16 classes), demonstrating that the use of real depth significantly improves classification performance and class separability, particularly for geometrically similar configurations. The results highlight the critical role of depth quality in configuration-level recognition and provide insights into the design of robust vision-based systems for LIS analysis. Full article

A vibration energy harvester (VEH) based on the principle of variable magnetic reluctance has been developed to enable wireless and maintenance-free power supply for condition monitoring sensors in vibrating machinery. Conventional battery or wired solutions are often impractical due to limited lifetime and high installation costs, motivating the use of vibration-based energy harvesting. The proposed VEH converts mechanical vibrations into electrical energy through the relative motion of a movable ferromagnetic core within a magnetic circuit. Unlike conventional VEH designs, where the magnet is the moving element, this concept utilizes a movable ferromagnetic core in combination with a stationary pole piece for voltage induction. This configuration enables a compact and easily adjustable proof mass, as neither the coil nor the magnet needs to be moved. The VEH is designed to operate effectively under excitation frequencies between $16 \mathrm{H}\mathrm{z}$ and $50 \mathrm{H}\mathrm{z}$ and acceleration levels from $9.81 {\displaystyle \frac{\mathrm{m}}{{\mathrm{s}}^2}}$ (equivalent to $1 \mathrm{g}$) up to $98.1 {\displaystyle \frac{\mathrm{m}}{{\mathrm{s}}^2}}$ (equivalent to $10 \mathrm{g}$). To ensure a reliable power supply, the VEH must deliver a minimum electrical output of $0.1 \mathrm{m}\mathrm{W}$ at the lowest excitation ($1 \mathrm{g}$) while maintaining structural integrity. Additionally, the maximum permissible displacement amplitude of the movable core is limited to $1.15 \mathrm{m}\mathrm{m}$ to avoid mechanical damage and ensure durability over long-term operation. Coupled magnetic-transient and mechanical finite element method (FEM) simulations were conducted to analyze the system’s dynamic behavior and electrical power output across varying excitation frequencies and accelerations. A laboratory prototype was developed and tested under controlled vibration conditions to validate the simulation results. The experimental measurements confirm that the VEH achieves an electrical output of $0.166 \mathrm{m}\mathrm{W}$ at $9.81 {\displaystyle \frac{\mathrm{m}}{{\mathrm{s}}^2}}$ and $16 \mathrm{H}\mathrm{z}$, while maintaining the maximum allowable displacement amplitude of $1.15 \mathrm{m}\mathrm{m}$, even at $98.1 {\displaystyle \frac{\mathrm{m}}{{\mathrm{s}}^2}}$ ($10 \mathrm{g}$) and $50 \mathrm{H}\mathrm{z}$. The strong agreement between simulation and experimental data demonstrates the reliability of the coupled FEM approach. Overall, the proposed VEH design meets the defined performance targets and provides a robust solution for powering wireless sensor systems under a wide range of vibration conditions. Full article

Triboelectric nanogenerators (TENGs) have gradually been applied in various practical scenarios, mainly focusing on core areas such as wearable motion monitoring devices, medical security systems, and natural resource exploration technology. However, they have the problem of low output energy and have not yet formed effective integration with mature commercially available products, which has hindered the industrialization process. This situation still significantly limits its global promotion and application. In this study, TENG was used as the sensing module for intelligent automotive airbags. We tested the voltage and current output characteristics of the system under different impact forces and frequency conditions. During the testing process, the electrical energy generated under different operating conditions is transmitted to the control system via Metal-Oxide-Semiconductor Field-Effect Transistor (MOSFET) circuits. The system will quickly determine whether to trigger the airbag deployment based on the received electrical signals, and activate the ignition device when necessary to achieve rapid inflation and deployment of the airbag. Compared with traditional triggering mechanisms, the airbag system based on this designed sensor has higher sensitivity and reliability. The sensor can stably capture collision signals, and experiments have shown that as the collision speed increases, the slope of its open-circuit voltage gradually approaches infinity. Applying TENG to automotive airbags not only effectively improves the triggering efficiency and accuracy of airbags, but also provides more reliable safety protection for drivers and passengers. Finite element simulation of the automotive airbag was conducted to provide specific data support for evaluating its safety performance. With the continuous advancement of TENG technology and further expansion of its application scenarios, we believe that such innovative safety technologies will play a more critical role in the future automotive industry. Full article

Defects such as gaps, delamination, and the misalignment of fibres impair the performance of carbon fibre-reinforced composites and can lead to structural failure during operation. Eddy current testing has proven to be a suitable method for detecting these defects early in the manufacturing process. However, validated electromagnetic modelling techniques are required to develop new eddy current sensors and gain a better understanding of the eddy current signals caused by different defect sizes. This paper proposes a novel finite element modelling approach to better account for fibre heterogeneity using spline approximation. Further, adaptive mesh refinement is used to reduce FEM solution errors. A defect in the form of a gap is modelled by adjusting the spline approximation accordingly. Finally, the model also accounts for inter-laminar current paths between carbon fibre layers, which are determined by four-terminal resistance measurement. The results show that the electromagnetic properties of the structure can be successfully modelled. The simulation is validated by comparing the virtual scans with eddy current scans of dry carbon fibre fabric with and without artificially manufactured gaps. Full article

Pesticides are applied to promote performances in the agricultural field, sustaining crop productivity by counteracting the damages induced by pests and weeds. Under conditions of uncontrolled application, their negative influences exerted on soil, water and biodiversity mean contamination of food and impact on human health. The reactive oxygen species generation induced by pesticides impair the antioxidant protective ability. For humans, pesticides can have cytotoxic, carcinogenic, and mutagenic potential. They can be classified relying on the chemical structure or on the targeted organism. Optical sensors are based on UV-Vis absorption, fluorescence, chemiluminescence, surface plasmon resonance or Raman scattering. Based on their coloring features, nanomaterials are used in optical sensing platforms. They impart high specific surface area, small sizes, facility of surface modification by biorecognition elements (enzyme, antibody, aptamer, molecularly-imprinted polymer) and promote sensitivity and selectivity in biosensing platforms. The present paper highlights the performances of the optical sensing platforms in pesticide assay. Relevant novel applications are discussed critically, following the attempts to improve analytical features of chemical and biochemical sensors. Critical comparison of the techniques is performed in the last section. Advances in nanofabrication like the inclusion of novel nanomaterials and optimizing data interpretation by integration of algorithms can further enhance performances. Full article

Bio-inspired flow-sensing and actuation mechanisms offer a promising path for enhancing the stability of flapping-wing flying robots (FWFRs) operating in dynamic and noisy environments. This study introduces a bio-inspired sensing and actuation feather unit (SAFU) that mimics the covert feathers of falcons and serves simultaneously as a distributed flow sensor and an adaptive actuation element. Each electromechanical feather (EF) passively detects airflow disturbances through deflection and actively modulates its flaps through an embedded actuator, enabling real-time aerodynamic adaptation. A reduced-order bond-graph model capturing the coupled aero-electromechanical dynamics of the FWFR wing and SAFU is developed to provide a physics-based training environment for a proximal policy optimization (PPO) based reinforcement learning controller. Through closed-loop interaction with this environment, the PPO policy autonomously learns control actions that regulate feather displacement, reduce airflow-induced loads, and improve dynamic stability without predefined control laws. Simulation results show that the PPO-driven SAFU achieves fast, well-damped responses with rise times below 0.5 s, settling times under 1.4 s, near-zero steady-state error across varying gust conditions and up to 50% alleviation of airflow-induced disturbance effects. Overall, this work highlights the potential of bio-inspired sensing-actuation architectures, combined with reinforcement learning, to serve as a promising solution for future flapping-wing drone designs, enabling enhanced resilience, autonomous flow adaptation, and intelligent aerodynamic control during operations in gusts. Full article

Coprime arrays enable enhanced degrees of freedom through the construction of virtual array equivalent signals. However, the presence of large “holes” leads to discontinuous co-arrays, which severely hampers direction-of-arrival (DOA) estimation techniques that rely on uniform array structures. This paper explores the practical application of co-array domain signal processing for underwater acoustic coprime arrays. We propose a novel array configuration based on coprime minimum disordered pairs, enabling the formation of continuously connected co-arrays without interpolating. To address the challenge of limited snapshots in underwater environments, DOA estimation can be achieved by utilizing traditional multipath matching pursuit (MMP) algorithms under the proposed continuous co-array implementation scheme. In practical applications, physical array element failures are inevitable, and faulty elements can create holes in the originally continuous co-array. While interpolation techniques can mitigate small gaps, their performance deteriorates significantly in the presence of large holes or uneven data distribution. To overcome these limitations, we introduce a sparse signal recovery (SSR) method using a fragment array data validation technique for sparse DOA estimation with an underwater acoustic coprime array. Based on the designed continuous array expansion scheme, the resulting continuous co-array is used to map the positions of element failures, revealing the gaps in the co-array. A validation model is established for partially continuous sub-arrays within the discontinuous co-array, enabling signal direction estimation based on the fragmented array validation. Both simulation and sea trial results confirm that the proposed approach maximizes the utilization of co-array elements without relying on interpolation or prediction, offering a robust solution for scenarios involving sensor failures. Full article

Surface plasmon resonance (SPR)-based optical fibre sensors have transformed label-free biosensing; however, single-interface evanescent field interactions continue to limit their sensitivity. This study presents a novel π-configuration optical fibre-based surface plasmon resonance sensor that greatly increases sensitivity by enabling dual plasmonic excitation on two symmetrically polished surfaces coated with optimized metallic thin films (Ag, Au, or Cu). We show, using finite element method simulations in COMSOL Multiphysics v6.3, that the π-configuration increases the interaction volume between the analyte and guided light, resulting in an enhanced sensitivity of 3300 nm/RIU for silver at refractive index (RI) 1.37–1.38, which is a 120% improvement over traditional D-shaped sensors (1500 nm/RIU). The maximum field norm for the π-configuration sensor is approximately 1.4 times greater than the maximum observed for the D-shaped SPR sensor at an analyte RI of 1.38. The sensor’s performance is evaluated using full-width half-maximum, wavelength sensitivity, and wavelength interrogation metrics. For the π-configuration sensor at an analyte RI of 1.38, the values of the FWHM, figure of merit, detection accuracy, and confinement loss were 36 nm, 94.29 RIU⁻¹, 0.94, and 38.5 dB/cm, respectively. The results obtained are purely simulated using COMSOL. With the support of electric field confinement analysis, a thorough theoretical framework describes the crucial coupling regime that causes ultra-high sensitivity at low RI. This design provides new opportunities for environmental monitoring, low-abundance biomarker screening, and early-stage virus detection, where it is necessary to resolve minute RI changes with high precision. Full article

To address the limitations of conventional eddy current magnetic-field-measurement techniques, this study proposes a novel measurement method for non-magnetic metals. First, the time-varying current in the Earth Field Simulator is calibrated using background magnetic sensors to obtain the coil magnetic field. This approach avoids repetitive errors caused by multiple current injections into the coil and ensures the simultaneity of current and magnetic field measurements. Additionally, the background eddy current magnetic field is approximated as a first-order RL-equivalent circuit, enabling the calculation and elimination of the background interference to improve the measurement accuracy of eddy current magnetic fields in non-magnetic metals. Next, experiments are carried out to measure the eddy current magnetic field of the non-magnetic metal plates under both ramp and sinusoidal magnetic field excitations. Finally, the eddy current magnetic simulations of the non-magnetic metal plates are conducted based on the finite element method. Under various excitation conditions, the maximum relative deviation between simulated and measured values remains below 5%, demonstrating the high precision of the proposed measurement method. This research provides a new approach for eddy current magnetic field measurement in non-magnetic metals. Full article