Search Results (999)

Fetal electroencephalogram (fEEG) recording could address the gap of functional fetal brain testing and enable direct assessment of fetal brain status during pregnancy and labor. However, the fetal EEG signal (~1 µV) is masked by roughly 80 dB (about 10,000×) of maternal ECG and other bioelectric interferences. This synthetic-data feasibility study presents whether the fetal EEG can be recovered from the maternal abdomen surface by using the following three sensing architectures on physiologically scaled synthetic data: single electrode, dual electrode, and a concentric 28-electrode array centered over the fetal head. We show that exploiting a known geometric attenuation steering vector with a minimum-variance distortionless-response (MVDR) beamformer raises recovery correlation from ~0 to ~0.28 and improves signal-to-noise ratio (SNR) by ~67 dB. A follow-up validation replaces the synthetic EEG generator with actual neonatal EEGs recorded from the scalp, obtained from OpenNeuro ds004577 and the Helsinki Zenodo corpus. Comparable recovery performance across both datasets confirms that the synthetic feasibility conclusion generalizes to real neonatal EEG composition and behavioral characteristics. The primary evaluation is based on waveform-level engineering metrics (correlation, SNR, and RMSE), while exploratory secondary analyses assess aEEG envelopes, band-power trends, and burst detection. These analyses are not intended as clinical validation. Independent per-electrode sensor noise remains the dominant residual limiter under the homogeneous geometric model. The reported r ≈ 0.28 should not be interpreted as expected in vivo performance at all gestational ages: an intact vernix layer can add approximately 35 dB of attenuation and makes the required noise floor substantially more stringent. This analysis adds a five-layer volume-conductor analysis, misalignment, depth and impedance-drift stress tests, recording-level statistics, comparisons with classical extraction families (PCA, ICA, adaptive cancelation, and multichannel Wiener filtering), an interference-alignment sensitivity analysis, and exploratory biomarker-level analyses, which together bound the idealizations of the forward model. Full article

Flexible and stretchable electronics inevitably undergo stretching, compression, bending and torsion when conformally attached to skin, soft tissues and dynamic organs. While deformation-induced electrical variations act as target signals for motion sensors, they cause resistance/impedance drift, baseline shift and sensitivity degradation in physiological electrodes, temperature/chemical sensors, interconnects and stimulation devices, leading to motion artifacts and reduced long-term reliability. Hydrogels are pivotal materials for soft bioelectronic interfaces owing to their high water content, low modulus, tissue compatibility and ionic conductivity. However, their conductive networks are susceptible to structural reconstruction under deformation, dehydration, swelling and cyclic fatigue, meaning that stretchability is by no means equivalent to strain insensitivity. This review focuses on stable resistance/impedance and functional output within a specified strain window, this paper reviews three representative material systems, liquid metal (LM)-based composite hydrogels, conductive polymer/elastic network composite hydrogels, and hydrogen-bonded isotropic architectures. It further summarizes three design strategies—geometric and functional compensation, mechanical decoupling and strain isolation, and interfacial engineering for conductive network stabilization—and discusses their applications in wearable epidermal and implantable bioelectronics. Finally, unified evaluation metrics for strain insensitivity are proposed, with future directions covering high-conductivity–low-modulus synergy, long-term water/ionic stability, robust soft-hard interfaces, multiaxial deformation tolerance and scalable manufacturability. Full article

Chitosan-based films and nanocomposites have attracted growing attention as renewable, biodegradable, and chemically adaptable materials for sustainable electronics. This systematic review synthesizes evidence on chitosan-containing functional films, membranes, polymer electrolytes, dielectric substrates, optoelectronic nanocomposites, electrochemical sensors, impedance sensors, and smart-device interfaces. This review adopts a structure–property–function perspective, examining molecular interactions, crystallinity, amorphous fraction, morphology, filler dispersion, processing routes, and interfacial architecture in relation to optical, electrical, dielectric, electrochemical, and sensing performance. The literature was identified using Scopus-oriented Boolean searches combining chitosan/chitin terms with thin-film or membrane descriptors, electronic-function terms, and structure–property terminology. Relevant studies were grouped into four themes: structural engineering and processing, dielectric/electrical/impedance properties, optoelectronic and band-gap engineering, and electrochemical/impedance-sensing applications. The synthesis shows that chitosan becomes electronically functional when its semi-crystalline, hydrogen-bonded matrix is modified through salt doping, plasticization, blending, conductive polymers, carbon materials, metal oxides, metal–organic frameworks, or noble metal nanoparticles. Reported advances include ionic conductivities up to 10⁻³ S/cm, improved dielectric behavior, band-gap reduction, and low detection limits. However, inconsistent reporting of material source, molecular weight, degree of deacetylation, film thickness, humidity, stability, and sustainability metrics limits comparability. Full article

The Bi/Bi₂O₃-type oxygen sensor is extensively employed for oxygen monitoring in liquid lead-bismuth eutectic (LBE)-cooled reactors, yet its low-temperature measurement accuracy remains a critical bottleneck limiting engineering deployment. This study aims to extend the lower operating temperature limit of the sensor through synergistic optimization of the reference electrode and solid electrolyte. The effects of the Bi/Bi₂O₃ mass ratio, filling amount, and yttria-partially stabilized zirconia (YPSZ) electrolyte wall thickness on sensor performance were systematically investigated over 300–600 °C. Electrochemical impedance spectroscopy and finite element simulations (COMSOL Multiphysics^® 6.3, COMSOL Inc., Stockholm, Sweden) were used to elucidate the underlying mechanisms. The results show that the optimized sensor with a Bi/Bi₂O₃ mass ratio of 95:5, a filling amount of 10 g, and a YPSZ wall thickness of 1.5 mm extended the stable operating limit from 350 °C to 300 °C, achieving a relative electromotive force error of 3.13% at 300 °C and maintaining over 4000 h of drift-free service. The improved low-temperature accuracy is attributed to the reduced oxygen ion migration activation energy (0.48 eV) and lower bulk impedance of the thick-walled YPSZ after high-temperature activation. These findings provide a material optimization strategy and theoretical basis for wide-temperature-range, long-lifetime oxygen sensing in lead-based reactors. Full article

Dissolved oxygen concentration is a key parameter determining the oxidation and corrosion behavior of structural materials in lead-based fast reactor coolants. Therefore, achieving high-precision in-situ oxygen measurement in LBE systems is a fundamental technical requirement for implementing oxygen-controlled corrosion protection. Currently, nuclear-grade Bi/Bi₂O₃ solid-state electrochemical oxygen sensors have a minimum effective operating temperature of 350 °C, which prevents them from meeting the real-time oxygen monitoring needs under low-temperature conditions. This temperature limitation has become a major bottleneck for the engineering application of oxygen control technology. To address these requirements and challenges, this study develops a novel electrochemical oxygen sensor based on an 8 mol% Y₂O₃-stabilized ZrO₂ (8YSZ) solid electrolyte and La_0.6Sr_0.4Co_0.2Fe_0.8O_3±δ (LSCF) electrode system. Comparative experiments with Bi/Bi₂O₃ sensors are conducted to quantitatively assess the advantages of the LSCF/Air sensor in low-temperature applications within 205~550 °C. Furthermore, considering the irradiation environment in nuclear reactors, oxygen ion irradiation was employed as an accelerated simulation method to preliminarily investigate the electrochemical transport properties of 8YSZ after irradiation. The effects of oxygen ion irradiation on the apparent impedance and apparent oxygen ion conductivity of 8YSZ were evaluated. The results show that the LSCF/Air oxygen sensor has the potential to extend the lower operating-temperature limit of 8YSZ-based oxygen sensors in static, oxygen-saturated LBE environments. Oxygen ion irradiation increased the apparent impedance and decreased the apparent ionic conductivity of the tested 8YSZ samples. These results provide preliminary experimental data for the development of oxygen sensors for oxygen monitoring and corrosion control in liquid-metal-cooled reactor systems over a wider temperature range. Full article

Data silos and topological incompatibility between building information modeling (BIM) geometric models and finite element method (FEM) analysis models in transportation infrastructure engineering represent critical bottlenecks that impede real-time digital twin analysis and the intelligent transformation of the industry. Based on a critical review of existing BIM-to-FEM conversion methods and their limitations, this study proposes a “BIM-FEM” seamless conversion and dynamic twin mapping method that integrates parametric modeling with finite element meshing, with modeling and repair time reduced from 16 h to 3 h, and the maximum element aspect ratio improved from 84.78 to 16.59. In terms of geometric topology, we propose a collaborative construction method in which finite element hexahedral meshing rules drive BIM parametric modeling in reverse. By regularizing the decomposition of axis lines and cross-sectional feature points of linear transportation structures and optimizing their topology, we achieve fully automated hexahedral meshing without topological errors. In terms of mechanical analysis, an “offline pre-solution, online superposition” computational order-reduction model is proposed. This reduces the high-dimensional full-range finite element solution of dynamic traffic loads to a dot product operation between the influence line matrix and real-time load vectors, enabling sub-second computational response under high-concurrency dynamic traffic conditions—specifically, single-point mapping takes less than 0.27 ms, incremental updates are controlled within 0.2 s. In terms of spatiotemporal mapping and system applications, a high-fidelity “FEM-BIM” mapping mechanism based on inverse isoparametric transformation and AABB (Axis-Aligned Bounding Box) spatial indexing has been established, supporting real-time rendering of 3D cloud maps on the web and digital twin applications in engineering. Applications of this method in real-world bridge engineering digital twin systems have demonstrated its ability to perform automatic structural safety assessments and health condition predictions with an overall computation time reduction of approximately 73% compared to conventional approaches. This addresses the shortcoming of traditional structural health monitoring—which emphasizes sensor-based identification over mechanistic evaluation—and provides a viable path for intelligent, precise management and maintenance of transportation infrastructure throughout its entire life cycle. Full article

Neuromorphic imaging sensors (event cameras) offer a promising paradigm for computational imaging and human pose estimation (HPE) under extreme illumination conditions. Nevertheless, dark-scene background activity originating from photodiode dark current and circuit thermal noise, together with hot-pixel noise, severely corrupts event streams and impedes reliable HPE in low-light scenarios. To address this issue, we propose an adaptive event denoising framework built upon a spatiotemporal Gaussian-weighted neighborhood model with a dynamic thresholding mechanism. It can effectively suppress background activity and hot-pixel noise while preserving edge and motion details critical for pose estimation. Leveraging this denoising front-end, we construct a complete dark-scene neuromorphic HPE pipeline by transferring the pre-trained MediaPipe model onto event-based time-surfaces. Quantitative and qualitative evaluations on public and self-collected datasets demonstrate that our approach outperforms state-of-the-art denoising methods with an improvement of over 20% on public benchmarks and over 30% on self-collected dark-scene data. We expect our work to pave the way toward reliable dark-scene human–robot interaction through robust neuromorphic pose estimation. Full article

This paper addresses the need for compact, low-cost soil moisture sensors operating at low microwave frequencies that can provide accurate, texture-aware (i.e., sensitive to different soil particle-size distributions such as sand and loam) characterization of soil permittivity and moisture content. Conventional techniques and many existing microwave resonator sensors are constrained by limited penetration depth, relatively large or complex structures, and calibration procedures that do not robustly account for different soil textures and moisture ranges. A dual-port microstrip square split ring resonator (SRR) sensor on Rogers RO3010 (Rmit University, Melbourne, Australia) is designed for operation at 1.3 GHz and analyzed using full-wave 3D electromagnetic simulations. The structure employs a T-shaped feedline and a shunt quarter-wavelength matching section to achieve strong field confinement in the sensing region and effective impedance matching. Soil is modeled as sandy and loamy superstrates over practical agricultural moisture ranges, with their complex permittivities drawn from reference datasets. Empirical calibration models are then developed, including polynomial curve fitting between resonance frequency shift and real permittivity, machine-learning-based calibration using resonance frequency and transmission loss features, and multiple linear regression linking moisture content to both real and imaginary permittivity components. The sensor exhibits a resonance frequency shift of about 115 MHz over 0–30% moisture for sand and 0–40% for loam, with a maximum sensitivity of 3.4%. Calibration models achieve mean absolute error below 1.22%, root mean square error under 1.58%, and coefficients of determination R² > 0.98 for both soil textures. These results demonstrate that a compact 1.3 GHz square SRR sensor with data-driven calibration, i.e., empirical models learned from simulated and measured S-parameters, enables sensitive, reproducible, and texture-aware soil moisture estimation suitable for agricultural and environmental monitoring. Full article

Biofilms remain a major challenge in meat and poultry processing because conventional sanitation verification methods provide only indirect evidence of attached microbial communities. Emerging microbiological and sensor-based technologies offer new opportunities to improve biofilm detection by providing information on biofilm structure, cellular membrane integrity, composition, and spatial distribution. This review evaluates advanced imaging techniques, molecular assays, extracellular polymeric substance (EPS)-focused analyses, and real-time sensor platforms for their potential to strengthen risk-based biofilm monitoring in meat and poultry processing environments. Confocal and epifluorescence microscopy, scanning electron microscopy, optical coherence tomography, and in situ fluorescence imaging provide detailed visualization of biofilm architecture and viability, supporting validation of routine monitoring methods and assessment of sanitation practices. Quantitative PCR, digital PCR, amplicon sequencing, and metagenomics characterize biofilm communities, identify persistent microorganisms, and evaluate sanitation effectiveness, while EPS analyses of polysaccharides, proteins, extracellular DNA, and lipids indicate biofilm maturity and resilience. Electrochemical impedance, quartz crystal microbalance, surface acoustic wave sensors, and microfluidic platforms show promise for near-real-time detection of attached biomass. Collectively, these technologies provide a framework for more targeted, data-driven biofilm surveillance that can improve sanitation verification and reduce pathogen persistence in meat and poultry processing facilities. Full article

Wireless Power Transfer (WPT) has emerged as a compelling alternative to wired charging; however, efficiency and safety are highly dependent on magnetic field distribution and leakage. This work presents a compact MEMS–based magnetic field induction sensor, including its design, fabrication, and electrical characterization, for real-time diagnosis in WPT systems. The sensor employs a Ni/Cr metallic inductor fabricated on a SiO₂ substrate using standard photolithography and occupies a footprint of 5 mm × 5 mm. The device is electrically characterized through impedance, quality factor, and frequency response measurements, followed by experimental validation using an industry-standard wireless charging system and dedicated signal-conditioning circuitry. The results from inductive coupling simulations, performed with the Magpylib Python library, align with experimental data showing that the sensor accurately follows theoretical magnetic field decay, detecting AC signals between 40 mV and 140 mV with a functional limit of 30 mm. Furthermore, experimental characterization through spatial mapping successfully identifies magnetic leakage hot spots, while thermal validation via infrared thermography correlates these magnetic readings with localized temperature increases. This integrated approach supports EMC optimization and thermal risk mitigation, providing a low–cost and effective diagnostic tool for enhancing safety and performance in WPT applications. Full article

Viscosity is a key quality parameter in cosmetic manufacturing, yet conventional rheological measurements require direct contact with the sample and are not suitable for continuous monitoring of the same specimen. This study investigates the feasibility of electromechanical impedance (EMI) sensing as a proof-of-concept approach for non-destructive monitoring of viscosity changes in cosmetic gels. Hydroxyethyl cellulose (HEC)-based model gels with HEC concentrations ranging from 0.0 to 1.0 wt% were prepared, providing viscosities between 1 and 794 cP. An acrylic-coated piezoelectric (PZT) sensor embedded in each gel was used to measure the electrical admittance spectra. The resonance peak conductance decreased progressively with increasing viscosity, whereas the resonance frequency remained nearly constant, indicating that resonance peak conductance is sensitive to viscosity-related changes in the surrounding gel. Continuous monitoring over 24 h under naturally varying temperature conditions further demonstrated that the EMI response changed consistently with the thermal behavior of the gel. These findings demonstrate the feasibility of EMI sensing as a non-destructive technique for continuously monitoring viscosity-related changes in cosmetic gels and provide a foundation for future studies using practical cosmetic formulations. Full article

Lower-limb rehabilitation exoskeletons have the potential to improve gait recovery after stroke by providing intensive and repetitive training. However, conventional control strategies often rely on fixed control parameters and exhibit limited adaptability to patient-specific characteristics, sensor noise, and dynamic uncertainties. This paper proposes an adaptive control framework that combines deep reinforcement learning (RL) with model-based impedance control for personalised lower-limb exoskeleton assistance. Patient-specific biological parameters are incorporated into the simulation environment and reward formulation to improve adaptability and robustness. Three state-of-the-art deep RL algorithms, Deep Deterministic Policy Gradient (DDPG), Twin Delayed Deep Deterministic Policy Gradient (TD3), and Soft Actor-Critic (SAC), are evaluated in a continuous control environment under varying signal-to-noise ratio (SNR) conditions ranging from 5 dB to noise-free conditions. Results demonstrate that TD3 achieves the most stable learning performance, obtaining a mean reward of −354.24 under noise-free conditions, while DDPG provides the highest joint-angle tracking accuracy with an RMSE of 0.0369 rad. SAC exhibits superior robustness in noisy environments, achieving the highest learning ratio of 0.51 at 5 dB SNR. Furthermore, the proposed personalised framework reduces tracking errors by up to 27% compared with non-personalised baseline approaches. The findings indicate that integrating patient-specific information with RL-based adaptive control can significantly enhance robustness, tracking performance, and personalisation in exoskeleton-assisted gait rehabilitation, providing a promising direction for future intelligent rehabilitation systems. Full article

Data-driven models are transforming structural health monitoring (SHM) for composites. However, excessive sensor costs and scarce, noise-contaminated data hinder model accuracy and generalizability. In this study, an electromechanical impedance (EMI)-based physical features and data-driven framework for high-precision damage assessment under conditions with noise and limited data is proposed. An experimental system that incorporates random noise to simulate operational environment noise was used to simulate seven progressive simulated damage states in CFRP laminates. Adaptive low-pass parabolic filtering via a fast Fourier transform smoothing filter (FFT-SF) denoised conductance signals in the frequency domain, increased efficiency over the Hinkley criterion, and significantly suppressed false alarms from sensor drift. Three input variables were selected: the resonant frequency F (reflecting structural stiffness), the resonant amplitude A (reflecting structural damping), and the root mean square deviation (RMSD) index (a statistical measure of spectral deviation). These three variables, two physics-based features and one statistical index, formed the inputs to a three-input artificial neural network (ANN). The fusion model achieved an RMSE of 0.0752 and an R² of 0.9807 on 105 small samples, significantly outperforming a purely data-driven single-input RMSD-ANN (RMSE of 0.1066; R² of 0.9612). Critically, Shapley Additive Explanation (SHAP) analysis revealed that the physical features significantly enhance model interpretability and predictive reliability, maintaining >98% simulated damage identification accuracy with extremely limited real data. This provides a cost-effective, high-precision, and scalable paradigm for aerospace composite SHM. Full article

The very fast transient overvoltage (VFTO) generated by the operation of disconnectors in gas-insulated switchgears (GIS) features a wide frequency band and a steep rising edge, posing a severe threat to the safe and stable operation of power grids. Accurate measurement of VFTO is a core prerequisite for the insulation protection design of power equipment. Aimed at solving the problem that ultra-high frequency (UHF) sensors widely installed in existing GIS are limited by—the built-in inductance and inability to fully reconstruct the VFTO waveform—this paper proposes a broadband VFTO measurement method based on modified UHF sensors. The VFTO measuring system based on UHF sensors was constructed and tested by modifying the structure of UHF sensors and a broadband pulse source capable of outputting equivalent frequencies from 1 kHz to 10 MHz. The results showed that the measuring system exhibited excellent high-frequency (100 kHz~1 MHz) response performance but poor low-frequency (~1 kHz) response. By introducing a voltage-follower impedance transformation module based on a high-speed operational amplifier, the UHF measuring system could accurately reconstruct the VFTO waveform during the opening and closing of the 750 kV GIS disconnector. This non-contact measurement scheme provided a highly promising and reliable technical path for on-site VFTO detection for in-service equipment without altering the existing structure of GIS. Full article

Hydrophilic osmium(Os)-based redox polymers were designed as electron-transfer mediators for fungal flavin adenine dinucleotide-dependent glucose dehydrogenase (FAD-GDH)-based glucose sensors. Poly(vinylimidazole-co-hydroxyethyl acrylate) (PVI-HEA) copolymers with different HEA compositions were synthesized and coordinated with Os(dmo-bpy)₂Cl₂ to prepare PVI-HEA-Os(dmo-bpy)₂Cl₂ redox mediators. The synthesized mediator systems were characterized using ¹H-nuclear magnetic resonance spectroscopy, Fourier transform infrared spectroscopy, ultraviolet–visible spectroscopy, field emission scanning electron microscopy/energy dispersive spectroscopy, zeta potential analysis, cyclic voltammetry, and electrochemical impedance spectroscopy. The results confirmed the successful formation of Os redox polymer structures and their immobilization on the electrode surface. The electrochemical behavior and glucose sensing performance strongly depended on the PVI-HEA composition. Among the compositions tested, PVI-HEA(3.5:1)-Os(dmo-bpy)₂Cl₂ showed the strongest redox current response, stable aqueous dispersion behavior, and relatively low interfacial charge-transfer resistance. Glucose-sensing measurements using FAD-GDH/mediator-modified electrodes showed linear current responses over the glucose concentration range of 1.25–20 mM. The PVI-HEA(3.5:1)-Os(dmo-bpy)₂Cl₂-based electrode showed the highest sensitivity of 16.18 μA cm⁻² mM⁻¹, which was significantly higher than those observed at lower-HEA compositions. The optimized mediator system also showed selective glucose responses against representative biological interferents, including ascorbic acid, uric acid, dopamine, and serotonin. Stable catalytic current responses were maintained under Human Plasma-Like Medium conditions, suggesting improved matrix tolerance compared to conventional PVI-based Os redox polymers. The improved sensing performance was attributed to the hydrophilic polymer environment introduced by the HEA units, which may facilitate favorable interfacial charge-transfer behavior within the enzyme–mediator layer. The results show that the hydrophilic copolymer composition plays an important role in the electrochemical behavior and glucose sensing performance of Os redox polymer mediators. The proposed PVI-HEA-Os(dmo-bpy)₂Cl₂ system may be a promising candidate for future enzymatic glucose sensing and continuous glucose monitoring-related applications. Full article