Search Results (3,057)

Heavy metal contamination, particularly lead (Pb), represents a threat to ecosystems and human health. This study investigates the variety Aloe vera var. Chinensis as a plant sensing platform for detecting the presence of lead by characterizing its bioelectrical response. A low-cost system based on Arduino was developed to acquire real-time electrical signals from 160 plants, equally divided between two groups: control conditions (n = 80) and Pb acetate exposure (500 mg/L; n = 80). Two recording sessions per plant were obtained after the plant had stabilized, resulting in 320 labeled measurements. The signals were characterized using the discrete wavelet transform (DWT), autoregressive (AR) models, and complexity measures based on entropy. Three classifiers—Support Vector Machine, Random Forest, and XGBoost—were trained and evaluated using five-fold cross-validation and a held-out test set with plant disjoint samples. XGBoost achieved the highest performance (accuracy = 93.0%; precision = 92.5%; recall = 93.8%; F1-score = 93.1%; and 95% CI for accuracy: 90.4–95.2% via bootstrap), significantly outperforming the other models. SHAP analysis revealed that midscale wavelet entropy and energy features, along with AR residual variance, were the most discriminative for Pb detection. These findings demonstrate a scalable, low-cost, and interpretable biosensing framework with potential applications in real-time environmental monitoring and early detection of heavy metal contamination. Full article

The integration of organoids with biosensors serves as a miniaturized model of human physiology and diseases, significantly transforming the research frameworks surrounding drug development, toxicity testing, and personalized medicine. This review aims to provide a comprehensive framework for researchers to identify suitable technical approaches and to promote the advancement of organoid sensing towards enhanced biomimicry and intelligence. To this end, several primary methods for technology integration are systematically outlined and compared, which include microfluidic integrated systems, microelectrode array (MEA)-based electrophysiological recording systems, optical sensing systems, mechanical force sensing technologies, field-effect transistor (FET)-based sensing techniques, biohybrid systems based on synthetic biology tools, and label-free technologies, including impedance, surface plasmon resonance (SPR), and mass spectrometry imaging. Through multimodal collaboration such as the combination of MEA for recording electrical signals from cardiac organoids with micropillar arrays for monitoring contractile force, these technologies can overcome the limitations inherent in singular sensing modalities and enable a comprehensive analysis of the dynamic responses of organoids. Furthermore, this review discusses strategies for integrating strategies of multimodal sensing approaches (e.g., the combination of microfluidics with MEA and optical methods) and highlights future challenges related to sensor implantation in vascularized organoids, signal stability during long-term culture, and the standardization of clinical translation. Full article

Engineered living materials (ELMs) integrate living cells—such as bacteria, yeast, or mammalian cells—with synthetic matrices to create responsive, adaptive systems for sensing and actuation. Among ELMs, those endowed with sensing capabilities are gaining increasing attention for applications in environmental monitoring, biomedicine, and smart infrastructure. Central to these sensing functions are synthetic gene circuits, which enable cells to detect and respond to specific signals. This mini-review focuses on recent advances in sensing ELMs empowered by synthetic gene circuits. Here, we highlight how rationally designed genetic circuits enable living materials to sense and respond to diverse inputs—including environmental chemicals, light, heat, and mechanical loadings—via programmable signal transduction and tailored output behaviors. Input signals are classified by their source and physicochemical properties, including synthetic inducers, environmental chemicals, light, thermal, mechanical, and electrical signals. Particular emphasis is placed on the integration of genetically engineered microbial cells with hydrogels and other functional scaffolds to construct robust and tunable sensing platforms. Finally, we discuss the current challenges and future opportunities in this rapidly evolving field, providing insights to guide the rational design of next-generation sensing ELMs. Full article

This project presents the development of an open-source educational platform based on an automotive Electrically Excited Synchronous Machine (EESM) repurposed from a KIA Sportage mild-hybrid vehicle. The introduction provides an overview of hybrid drive systems and the primary configurations employed in automotive applications, including classifications based on power flow and the placement of electric motors. The focus is placed on the parallel hybrid configuration, where a belt-driven starter-generator assists the internal combustion engine (ICE). Due to the proprietary nature of the original control system, the unit was disassembled, and a custom control board was designed using a Texas Instruments C2000 Digital Signal Processor (DSP). The motor features a six-phase dual three-phase stator, offering improved torque smoothness, fault tolerance, and reduced current per phase. A compact Anisotropic Magneto Resistive (AMR) position sensor was implemented for position and speed measurements. Current sensing was achieved using both direct and magnetic field-based methods. The control algorithm was verified on a modified six-phase inverter under simulated vehicle conditions utilizing a dynamometer. Results confirmed reliable operation and validated the control approach. Future work will involve complete hardware testing with the new control board to finalize the platform as a flexible, open-source tool for research and education in hybrid drive technologies. Full article

Theoretical studies have indicated that borophene is a promising two-dimensional material characterized by remarkable chemical, mechanical, and electrical properties. Nonetheless, its practical applications in areas such as catalysis and gas sensing are hindered by the limited density of reactive sites in its pristine form. To address this limitation, the present study explores the controlled fluorination of borophene nanoflakes as a strategy to modify their surface chemistry and enhance the availability of active sites. Furthermore, it is anticipated that surface fluorination will improve hydrophobicity, which is crucial for reducing humidity-related interference in sensing applications. In this study, we report the successful functionalization of borophene nanoflakes with fluorine using a plasma arc discharge technique for the first time. Borophene nanolayers were synthesized via a sonochemical-assisted exfoliation method, yielding nanosheets with an average lateral dimension of approximately 100 nm. The fluorinated samples were characterized using X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), and high-resolution transmission electron microscopy (HRTEM). A systematic investigation of plasma exposure durations demonstrated that fluorine was effectively introduced as a dopant while maintaining the crystallinity of the borophene lattice. Full article

The electric power equipment industry is rapidly advancing toward “informationization,” with the swift progression of intelligent sensing technology serving as a key driving force behind this transformation, thereby triggering significant changes in global electric power equipment. In this process, intelligent sensing has created an urgent demand for high-performance integrated power systems that feature compact size, lightweight design, long operational life, high reliability, high energy density, and low cost. However, the performance metrics of traditional power supplies have increasingly failed to meet the requirements of modern intelligent sensing, thereby significantly hindering the advancement of intelligent power equipment. Energy harvesting technology, characterized by its long operational lifespan, compact size, environmental sustainability, and self-sufficient operation, is capable of capturing renewable energy from ambient power sources and converting it into electrical energy to supply power to sensors. Due to these advantages, it has garnered significant attention in the field of power sensing. This paper presents a comprehensive review of the current state of development of energy harvesting technologies within the power environment. It outlines recent advancements in magnetic field energy harvesting, electric field energy harvesting, vibration energy harvesting, wind energy harvesting, and solar energy harvesting. Furthermore, it explores the integration of multiple physical mechanisms and hybrid energy sources aimed at enhancing self-powered applications in this domain. A comparative analysis of the advantages and limitations associated with each technology is also provided. Additionally, the paper discusses potential future directions for the development of energy harvesting technologies in the power environment. Full article

To expand the application of silver nanowires (AgNWs) in the field of flexible sensors, this study developed a stretchable flexible sensor based on thermoplastic polyurethane (TPU). Initially, the TPU nanofiber membrane was prepared by electrospinning. Subsequently, high-aspect-ratio AgNWs were synthesized via a one-step polyol reduction method. The AgNWs with the optimal aspect ratio were selected for the conductive layer and spray-coated onto the surface of the TPU nanofiber membrane. Another layer of TPU nanofiber membrane was then laminated on top, resulting in a flexible thin-film sensor with a “sandwich” structure. Through morphological, chemical structure, and crystallinity analyses, the primary factors influencing AgNWs’ growth were investigated. Performance tests revealed that the prepared AgNWs had an average length of approximately 130 μm, a diameter of about 80 nm, and an average aspect ratio exceeding 1500, with the highest being 1921. The obtained sensor exhibited a low initial resistance (26.7 Ω), high strain range (sensing, ε = 0–150%), high sensitivity (GF, over 19.21), fast response and recovery time (112 ms), and excellent conductivity (428 S/cm). Additionally, the sensor maintained stable resistance after 3000 stretching cycles at a strain range of 0–10%. The sensor could output stable and recognizable electrical signals, demonstrating significant potential for applications in motion monitoring, human–computer interaction, and healthcare fields. Full article

Much as the information generated by our fingertips is used for fine-scale grasping and manipulation, closed-loop dexterous robotic manipulation requires rich tactile information to be generated by artificial fingertip sensors. In particular, fingertip shear sensing dominates modalities such as twisting, dragging, and slipping, but there is limited research exploring soft shear predictions from an increasingly popular single-material tactile technology: electrical impedance tomography (EIT). Here, we focus on the twisting of a screwdriver as a representative shear-based task in which the signals generated by EIT hardware can be analyzed. Since EIT’s analytical reconstructions are based upon conductivity distributions, we propose and investigate five reduced-order models which relate shear-based screwdriver twisting to the conductivity maps of a robot’s single-material sensorized fingertips. We show how the physical basis of our reduced-order approach means that insights can be deduced from noisy signals during the twisting tasks, with respective torque and diameter correlations of 0.96 and 0.97 to our reduced-order parameters. Additionally, unlike traditional reconstruction techniques, all necessary FEM model signals can be precalculated with our approach, promising a route towards future high-speed closed-loop implementations. Full article

Tissue chips (TCs), otherwise known as organs-on-a-chip (OoC), organ chips (OCs), or microphysiological systems (MPS), are rapidly gaining prominence as an extension of or even replacement for traditional animal models of disease physiology. They also have recognized utility in the context of drug development: for example, data from TCs can now be submitted in place of some animal testing to the FDA. In principle, TCs are structured to allow measurement of any number of outputs that yield information about the tissue. However, to date, measurements made during experiments with TCs have been largely restricted to immunofluorescence microscopy and benchtop assays performed on media extracted from the cell culture within the device. With the development of biosensors that are sensitive and have an ever-shrinking footprint, on-board biosensing is now in the early stages of exploration. This review discusses the importance of tissue chips and the advances in sensing that will aid the complexity and utility of tissue chip research moving forward. We cover several sensing modalities, including electrical and optical sensing modes. Finally, challenges and opportunities for the future are discussed. Full article

To address the demands for wide-range and high-precision current measurement, this paper proposes a novel current sensor design that integrates spin sensing technology, magnetic shunt effect, and a multi-sensor data fusion algorithm. The spin valve sensors accurately detect the magnetic field generated by the signal current, while the soft magnetic shunt structure attenuates the magnetic field to a level suitable for the spin valve sensors. Consequently, the detection current range can be extended by 6.8 times. Using four spin valve sensors and data fusion with an averaging algorithm, the system can calibrate the errors caused by the displacement or tilt of the current-carrying wire. Experimental results demonstrate that the current sensor achieves a sensitivity of 61.6 mV/V/A, an excellent linearity of 0.55%, and robust measurement performance, as well as strong anti-interference capability. Our study offers a novel solution for high-precision, wide-range current measurement in applications such as those in new energy vehicle electronics and precision electric energy metering. Full article

This review examines the emerging role of manganese-based nanoparticles (Mn-NPs) in detecting heavy metal pollutants in environmental matrices. Heavy metals such as cadmium, lead, zinc, and copper pose serious environmental and health concerns due to their tendency to persist in ecosystems and accumulate in living organisms. As a result, there is a growing need for reliable methods to detect and remove these pollutants. Manganese nanoparticles offer unique advantages that scientists could consider as replacing other metal nanoparticles, which may be more expensive or more toxic. The physicochemical properties of Mn-NPs—including their multiple oxidation states, magnetic susceptibility, catalytic capabilities, and semiconductor conductivity—enable the development of multi-modal sensing platforms with exceptional sensitivity and selectivity. While Mn-NPs exhibit inherently low electrical conductivity, strategies such as transition metal doping and the formation of composites with conductive materials have successfully addressed this limitation. Compared to noble metal nanoparticles (Au, Ag, Pd) and other base metal nanoparticles (Bi, Fe₃O₄), Mn-NPs demonstrate competitive performance without the drawbacks of high cost, complex synthesis, poor distribution control, or significant aggregation. Preliminary studies retrieved from the Scopus database highlight promising applications of manganese-based nanomaterials in electrochemical sensing of heavy metals, with recent developments showing detection limits in the sub-ppb range. Future research directions should focus on addressing challenges related to scalability, cost-effectiveness, and integration with existing water treatment infrastructure to accelerate the transition from laboratory findings to practical environmental applications. Full article

Unmanned aerial vehicles (UAVs) are increasingly used in urban management and public services, but their potential misuse poses serious risks to public safety. Electrostatic sensors offer a promising approach for UAV detection and interception by capturing their electrostatic signatures during dynamic encounters. However, the sensor output is affected by the coupling between encounter parameters and circuit characteristics, making accurate modeling challenging. This study proposes an analytical modeling method for electrically floating electrostatic sensor signals, calibrated under actual boundary conditions. The model incorporates the effects of encounter angle, miss distance, relative velocity, and equivalent input resistance-capacitance parameters, enabling efficient prediction of sensor signals under multivariable coupling. To validate the model, the electrostatic signatures during dynamic encounters were obtained using the airborne data acquisition and storage system. Results show that the predicted signals correlate well with measured data, with a correlation coefficient above 0.9. The proposed model demonstrates high computational efficiency and supports the design and optimization of electrostatic sensing systems for low-altitude UAV detection and interception. Full article

Conventional conductive hydrogels are susceptible to swelling in aquatic environments; which compromises their mechanical integrity; a limitation that poses a potential challenge to their long-term stability and application. In this study, a zwitterionic ion-conductive hydrogel was fabricated from polyvinyl alcohol (PVA), acrylic acid (AA), and [2-(methacryloyloxy)ethyl]dimethyl-(3-sulfopropyl)ammonium hydroxide (SMBA), forming a dual-network structure. A copolymer of zwitterionic SBMA and AA formed the first network, and PVA formed the second network by repeated freeze–thawing. The equilibrium state of zwitterionic SBMA was modulated by AA to protonate the SBMA, which resulted in the conversion of -SO₃⁻ to -SO₃H; thus, hydrogels had the anti-swelling property driven by electrostatic repulsion. In addition, the prepared hydrogels possessed excellent mechanical properties (tensile strength of 0.76 MPa, elongation at break of 322%, and compressive strength of 0.97 MPa at 75% compressive strain) and remarkable anti-swelling properties (80% swelling after 120 h of immersion). Owing to the zwitterionic nature of SBMA, the hydrogel also showed inherent antimicrobial properties and high electrical conductivity, which could be capable of monitoring human movement and physiological signals. This work provides a facile strategy for designing hydrogels with remarkable mechanical properties and anti-swelling characteristics, expanding the application environment of hydrogels in flexible sensing Full article

This article presents the results of an experimental study focused on evaluating the potential to harvest electrical energy from vertical vibrations affecting a child car seat installed on an ISOFIX base with a support leg during real driving conditions. The objective was to measure vibration levels in the seat structure and assess the feasibility of converting this mechanical energy into electrical power. The study involved two child seat models, each tested under loads of 9 kg and 15 kg, while driving over smooth asphalt, damaged asphalt, and speed bumps. Acceleration data were collected at three key structural locations: the seat surface, the ISOFIX base, and the support leg. These measurements served as the basis for estimating the mechanical energy available and the resulting electrical output. Findings show that in poor road conditions, the system can generate enough energy to power a 10 µW sensor for more than 42 days. The results confirm the feasibility of using vibration energy harvesting to supply smart safety features such as presence detection, temperature monitoring, or posture sensing in child seats, without the need for batteries or a connection to the vehicle’s electrical system. Full article

The dynamic response in propane atmospheres at different voltages was investigated for samples made from powders of the semiconductor oxide CoSb₂O₆ synthesized using the microwave-assisted colloidal method. Powders of the compound calcined at 700 °C were studied with X-ray diffraction, confirming the CoSb₂O₆ crystalline phase. The microstructural characteristics of the oxide were analyzed using scanning and transmission electron microscopy (SEM/TEM), revealing a high abundance of nanorods, nanoplates, and irregular nanoparticles. These nanoparticles have an average size of ~21 nm. Using UV-Vis, absorption bands associated with the electronic transitions of the CoSb₂O₆’s characteristic bonds were identified, which yielded a bandgap value of ~1.8 eV. Raman spectroscopy identified vibrational bands corresponding to the oxide’s Sb–O and Co–O bonds. Dynamic sensing tests at 300 °C confirmed the material’s p-type semiconductor behavior, showing an increase in resistance upon exposure to propane. Critically, these tests revealed that the sensor’s baseline resistance and overall response are tunable by the applied voltage (1–12 V), with the highest sensitivity observed at the lowest voltages. This establishes a clear relationship between the electrical operating parameters and the sensing performance. The samples exhibited good operational stability, capacity, and efficiency, along with short response and recovery times. Extra-dry air (1500 cm³/min) was used as the carrier gas to stabilize the films’ surfaces during propane detection. These findings lead us to conclude that the CoSb₂O₆ could serve as an excellent gas detector. Full article