Search Results (5,940)

This paper presents the design, modeling, and experimental validation of a capacitive tactile sensor specifically conceived to sense shear-driven contact dynamics in robotic manipulation. The proposed device is a layered flexible capacitive structure, in which controlled tangential interactions are induced. The electrode design maximizes sensitivity to shear motion and promotes an isotropic response with respect to slip direction, thereby addressing two key limitations that affect the majority of existing slip-sensing technologies. An analytical model was developed to describe the essential relationship between shear-induced displacements and the electrical response, providing insight into the design parameters and supporting the selection of geometry and materials. To test the sensor in real conditions, a dedicated capacitive readout circuit based on high-frequency excitation and synchronous demodulation was developed to robustly acquire capacitance variations while rejecting static offsets and parasitic effects. Several formulations for the interposed dielectric layer material were investigated, including viscous fluids and composite mixtures with high-permittivity nanoparticles, with the aim of improving electrical sensitivity while preserving mechanical stability. Experimental results obtained under controlled loading and sliding conditions demonstrate that the sensor is highly sensitive to changes in contact state and tangential interaction dynamics. The sensor responded consistently to both load-induced shear and slip-related phenomena, enabling the reliable monitoring of contact dynamics rather than binary slip detection. A proof-of-concept integration into a robotic finger confirms the suitability of the proposed approach for grasp monitoring. Full article

In our previous study, a taste sensor employing a lipid/polymer membrane modified with 2,6-dihydroxyterephthalic acid (2,6-DHTPA) enabled the detection of the umami substances monosodium glutamate (MSG) and inosinate monophosphate (IMP). The taste sensor was also able to evaluate the synergistic effect, an umami enhancement phenomenon that occurs between MSG and IMP. However, the structural requirements for modifiers capable of detecting IMP have not yet been clarified. In the present study, to elucidate these requirements, nine different modifiers were prepared, and taste sensor measurements for IMP were conducted in combination with ¹H-NMR analysis. As a result, three distinct patterns were observed: (1) modifiers that exhibited chemical shift changes and generated a potential response in the positive direction (i.e., a positive potential response); (2) modifiers that showed chemical shift changes but produced either an almost zero or a negative potential response; and (3) modifiers that exhibited neither chemical shift changes nor any potential response. For receptor membranes that did not exhibit a positive response, the corresponding modifiers either lacked two carboxyl groups or did not possess intramolecular hydrogen bonding involving hydroxyl groups. From these results, it was clarified that the essential conditions for obtaining a positive potential response to IMP are that the modifier (1) contains two carboxyl groups and (2) possesses intramolecular hydrogen bonding. Full article

Fire safety plays a vital role in protecting lives, property, and the environment, and it keeps communities and organizations running safely. Many existing fire pump control systems fall short in educational and small-to-medium industrial settings: they often control only one pump at a time, rely heavily on manual monitoring, and come with high costs that limit accessibility. To address these gaps, we developed an affordable, hands-on educational kit that brings real-world fire safety systems into the classroom using modern automation technology. The system is built around a Delta DVP12SA211R PLC chosen for its built-in real-time clock, integrated RS-232/RS-485 ports for reliable communication, and expanded with DVP16SP11R digital I/O and DVP04AD-S2 analog input modules to interface with simulated sensors mimicking smoke detection and water pressure. Students interact with the system through a Delta DOP-110IS HMI, which features Ethernet connectivity for remote observation, electrical isolation for safe operation, and a 200 ms screen update rate to ensure responsive, realistic feedback. The kit enables learners to explore critical emergency scenarios, including automatic switching between jockey and main pumps, low-pressure alerts, and system failover, transforming theoretical concepts into tangible skills. In user evaluations, 57.1% of students with no prior experience reported that the simulations closely mirrored real-world systems, while 80% of those with a fire safety background found the kit reinforced their existing knowledge; notably, 57.1% of instructors rated it as highly effective for teaching core fire safety principles across diverse learner profiles. By integrating industrial-grade hardware with scenario-based learning, this tool not only deepens understanding of fire protection systems but also better prepares future engineers for the practical demands of fire safety and industrial automation careers. Full article

Glassy carbon electrodes (GCEs) have gained increased attention for the sensitive electrochemical detection of heavy metals due to their excellent chemical stability, wide potential window, and good electrical conductivity. These characteristics make GCEs an effective platform for sensor development. In particular, nanomaterial-modified GCEs have emerged as a promising strategy, offering enhanced sensitivity, selectivity, and faster response compared to conventional analytical techniques. This review summarizes recent advances over the past five years in the use of GCEs modified with chemically synthesized nanoparticles for the simultaneous detection of multiple heavy metal ions, including cadmium, lead, mercury, and chromium. It also includes how quantum chemical methods have aided our understanding of these phenomena. Heavy metals pose significant environmental and public health risks, with well-documented neurological, cardiovascular, reproductive, and carcinogenic effects, highlighting the need for accurate and rapid monitoring methods. Regulatory limits established by organizations such as the World Health Organization and the Environmental Protection Agency further emphasize the demand for highly sensitive detection technologies. This review examines the fundamental properties of GCEs, common nanomaterial modification techniques, and their application in multi-ion detection systems. Key advantages such as cost-effectiveness, portability, and adaptability to diverse sample matrices are highlighted. Current challenges, including electrode fouling, selectivity, and matrix interference, are also addressed, along with future perspectives for improving GCE-based sensors for real-world environmental monitoring. Full article

Vehicle recharge time is a key barrier to widespread adoption of battery electric trucks, where megawatt class charging could be used to achieve refueling times comparable to internal combustion vehicles. This work presents the design and validation of a megawatt-capable rechargeable energy storage system (144 kWh, 40P384S) together with a physics-based modeling framework for safe 1 MW operation. The pack architecture is reconfigurable, enabling nominal 750 V (80P192S) propulsion mode as well as 1125 V and 1500 V charging modes compatible with the Megawatt Charging System (MCS). An equivalent circuit model is developed to relate cell-level parameters to pack-level power, heat generation, and temperature rise, providing guidance on feasible charge profiles and thermal limits. A Simulink-based digital twin of the reconfigurable pack is then used to analyze sensitivity to current sensor mismatch and to verify protection logic for multiple bus voltage configurations. Finally, pack tests up to 1 MW confirm the model-predicted operating envelope and illustrate practical constraints imposed by charger voltage and pack resistance. The combined hardware and modeling approach provides a reusable platform for studying extreme fast charging of medium- and heavy-duty BEV packs-class charging -capable rechargeable energy storage system (144 kWh, 40P384S) together with a physics-based modeling framework for safe 1 MW operation. The pack architecture is reconfigurable, enabling nominal 750 V (80P192S) propulsion mode as well as 1125 V and 1500 V charging modes compatible with the Megawatt Charging System (MCS). An equivalent-circuit model is developed to relate cell-level parameters to pack-level power, heat generation, and temperature rise, providing guidance on feasible charge profiles and thermal limits. A Simulink-based digital twin of the reconfigurable pack is then used to analyze sensitivity to current–sensor mismatch and to verify protection logic for multiple bus-voltage configurations. Finally, pack tests up to 1 MW confirm the model-predicted operating envelope and illustrate practical constraints imposed by charger voltage and pack resistance. The combined hardware and modeling approach provides a reusable platform for studying extreme fast charging of medium- and heavy-duty BEV packs. Full article

In this study, a highly crystalline 2H (hexagonal)-phase MoS₂ sensing layer with a precisely controlled crystal structure was realized through a combination of DC sputtering and sulfurization annealing processes, and subsequently integrated with a D-shaped optical fiber to develop a highly sensitive carbon dioxide (CO₂) sensor. Conventionally sputtered MoS₂ thin films often suffer from the presence of unstable metallic 1T (tetragonal) phases and a high density of sulfur vacancies, which significantly degrade sensor reversibility and long-term stability. Here, high-temperature annealing under a sulfur-rich atmosphere was employed to induce a complete phase transition from the metastable 1T phase to the stable semiconducting 2H phase, while simultaneously healing sulfur vacancies. Enhanced crystallinity was confirmed by Raman spectroscopy. The fabricated sensor exhibited excellent linearity (R² > 0.99) and markedly improved repeatability over a CO₂ concentration range of 1000–10,000 ppm. This significant performance enhancement is attributed to reversible charge transfer induced by sulfur vacancy passivation, which modulates the complex refractive index of the MoS₂ layer and optimizes optical interaction with the evanescent field of the D-shaped fiber. The phase engineering and defect-healing strategy presented in this work effectively addresses the drift issues commonly observed in conventional electrical gas sensors and provides a crucial pathway toward the realization of high-performance optical gas sensors. Full article

Microclimatic stress strongly influences the ecological resilience of Vasconcellea stipulata (Toronche), yet current monitoring approaches rely on sparse measurements and lack real-time predictive capability. This work introduces an AI-enhanced virtual sensing framework based on laser-induced graphene (LIG) designed to emulate the thermoresistive response of an LIG transducer and generate high-resolution environmental indicators for microclimatic analysis. Unlike conventional LIG sensors or standalone IoT systems, the proposed framework integrates experimental calibration, data-driven modeling, and embedded inference into a unified architecture suitable for lightweight deployment on edge devices. A multilayer perceptron (MLP) model trained on laboratory data reproduced the temperature- and humidity-dependent electrical behavior of the transducer with high fidelity, achieving an RMSE of 0.016 k$\Omega$ in the calibrated range (10–60 °C) and remaining below 0.09 k$\Omega$ under noisy and extrapolated conditions. Sensitivity analysis identified temperature as the dominant driver (71%), followed by solar irradiance (19%) and relative humidity (10%), consistent with the microstructural mechanisms governing LIG’s response. The virtual sensor enables continuous, low-cost environmental monitoring and provides quantitative variables that can support downstream ecological interpretation. Overall, the results highlight the potential of AI-enhanced LIG–IoT architectures for advancing real-time microclimatic assessment in resource-limited Andean ecosystems. Full article

Deep eutectic solvent (DES), when used as the continuous phase of eutectogels, can significantly improve their electrical and mechanical properties due to its excellent conductivity, freeze resistance and chemical stability. The development of eutectogels effectively solves the key limitations of traditional hydrogels and organogels, such as low-temperature freezing, high-temperature volatilization, and organic solvent leakage. It also realizes the collaborative optimization of environmental friendliness and comprehensive performance, which makes it show broad application prospects in the field of flexible sensing. This review summarizes the design principles, material selection, sensing mechanisms, and flexible sensing applications of eutectogels. By examining the design of eutectogels, the selection of DES, and the synthesis of the gel network, it provides a theoretical basis for the development of eutectogel-based sensor devices. A detailed description of the sensing mechanism is provided to elucidate the signal generation and transition in eutectogels toward the purpose of the practical applications. Finally, the application prospects of eutectogels for high-performance sensors and detection devices are discussed. Additionally, we provide a theoretical support for their structural design, performance optimization, and practical application. Full article

This work demonstrates the potential of fiber optic sensors for measuring thermal effects in lithium-ion batteries, using a fiber optic measurement method of Optical Frequency Domain Reflectometry (OFDR). The innovative application of fiber sensors allows for spatially resolved temperature measurement, particularly emphasizing the importance of monitoring not just the exterior but also the internal conditions within battery cells. Utilizing inert glass fibers as sensors, which exhibit minimal sensitivity to electric fields, opens up new pathways for their implementation in a wide range of applications, such as battery monitoring. The sensors used in this work provide real-time information along the entire length of the fiber. It is shown that using the herein presented novel sensors in a temperature range of 0–$80°\mathrm{C}$ reveals a linear, high-sensitivity thermal measurement characteristic with a local resolution of a few centimeters. Furthermore, this study presents preliminary findings on the potential application of fiber optic sensors in lithium-ion battery (LIB) cells, demonstrating that the steps required for battery integration do not impose any restrictive effects on thermal measurements. Full article

Precise temperature control is crucial for maintaining product quality and optimizing energy efficiency in multi-zone continuous crystallizers. However, such industrial processes typically exhibit complex nonlinear dynamics and strong coupling effects. More critically, physical constraints often prevent sensor installation, rendering temperatures in key regions unobservable and challenging traditional closed-loop control strategies. To address partial observability and model uncertainty, this paper proposes a Model-Based Reinforcement Learning (MBRL) framework utilizing solely offline historical data. The core innovation lies in developing a Recursive State Space Model (RSSM) that serves not only as a high-fidelity digital twin but, more critically, is deployed as a real-time “virtual sensor” to infer unobservable system states. This virtual sensing capability provides precise state estimates for downstream policy optimization. Additionally, a multi-objective reward function is designed to balance tracking error, stability, and control cost. Experimental results demonstrate that the proposed virtual sensor exhibits exceptional long-term stability, maintaining high fidelity and effectively suppressing error accumulation during long-term multi-step autoregressive predictions. Consequently, the trained agent outperforms traditional Proportional-Integral-Derivative (PID) and Model Predictive Control (MPC) controllers, achieving over 67% improvement in temperature tracking accuracy while reducing control action costs by more than 93%, indicating smoother system operation and enhanced energy efficiency. Full article

This paper presents a model-free Best Efficiency Point (BEP) tracking method for centrifugal pumps without head measurement or manufacturer-provided characteristic curves. The proposed approach combines a discrete finite-difference extremum-seeking control (ESC) scheme with an efficiency approximation proxy derived from measurable variables—namely, flow rate and electrical power. Under constant head conditions, the proxy function is analytically shown to be proportional to the true pump efficiency, enabling real-time BEP localization using only motor feedback signals. The ESC algorithm employs a sign-based gradient rule with adaptive step-size reduction to achieve rapid and stable convergence without mathematical models. A Python-based simulation using a Schneider SUB 15-0.5cv pump demonstrates that the method can track the BEP with negligible steady-state error (less than 0.1% efficiency deviation). The proposed framework offers a cost-effective solution for efficient optimization for mobile pumping applications in large water resources where installing head sensors is impractical. Full article

This paper proposes an advanced sensorless control strategy for Permanent Magnet Synchronous Motors (PMSMs) aimed at enhancing dynamic performance, robustness, and reliability while eliminating the need for mechanical sensors. The core contribution lies in a novel hybrid speed regulation framework that combines a terminal sliding mode control scheme with a high-order super-twisting algorithm (HOT-STA-SMC), ensuring finite-time convergence, effective chattering suppression, and strong disturbance rejection under varying operating conditions. For the inner current loop, an Exponential Reaching Law Sliding Mode Controller (ERL-SMC) is implemented to guarantee fast current response and precise current tracking, even in the presence of parameter uncertainties. Furthermore, the conventional Model Reference Adaptive System (MRAS) observer is embedded within the proposed control architecture, resulting in more accurate speed estimation and enhanced stability during load fluctuations. The complete control system is rigorously modeled and tested in MATLAB R2024b/Simulink, capturing the full interaction between machine dynamics, control loops, and observer mechanisms. The simulation results verify that the proposed design achieves superior torque smoothness, minimal current ripples, and fast transient response compared to conventional sensorless methods. By integrating high-order sliding modes with advanced adaptive observation, this work offers a robust and cost-effective solution for high-performance PMSM drives, suitable for demanding applications such as electric vehicles, renewable energy conversion, and industrial motion control. Full article

High concentrations of ionized ammonia (${\mathrm{NH}}_4^{+}$) have been increasingly reported in municipal drinking water systems, posing a severe public health risk as excessive ingestion can lead to life-threatening conditions. Despite its importance, there is a significant lack of sensing technologies designed for continuous-flow monitoring outside laboratory settings, particularly those providing a robust, low-cost methodology suitable for resource-limited environments. To address these challenges, in this work, we report the development of an impedance sensor featuring a 3D-printed housing (3D-IS) for monitoring aqueous ionized ammonia (${\mathrm{NH}}_4^{+}$). The sensing electrodes, composed of zinc oxide and graphite, allow for the detection of concentrations 10 times lower and 60 times higher than current environmental limits. Its innovative, optimized design, analogous to that of industrial pressure gauges, highlights its potential for use in continuous water flow conditions outside the laboratory, such as water treatment plants. The level of ${\mathrm{NH}}_4^{+}$ in water is monitored by changes in impedance magnitude, with optimal performance observed at a frequency of 100 kHz. At this frequency, the impedance magnitude decreased by nearly two orders of magnitude as the ${\mathrm{NH}}_4^{+}$ concentration increased from 0 to 1 μM. Under these optimized conditions, the sensor exhibited a sensitivity of 2 kΩ/log(μM) and a linearity exceeding 90%. Furthermore, we propose an equivalent circuit model that accurately describes the experimental data, explaining the transduction process. We also describe, from an electrical perspective, the phenomenon of adsorption on the sensor’s transducer surface, thereby ensuring the device’s selectivity. The sensor was evaluated using dilutions of a standard ammonium solution for IC in distilled water, as well as with real groundwater samples, obtaining ∼99.7% of correlation with ion chromatography and a limit of detection of 2 μM. Finally, our device can provide information relatively quickly, with the added advantage of stable response under continuous-flow and real conditions, making it an attractive option for integration into a field sensor node. Full article

In this work, a D-shaped Photonic Crystal Fiber (PCF) sensor with a detection range of 1.30–1.35 is proposed, including Gold (Au), Titanium Dioxide (TiO₂), graphene, and a functionalized sensing region. Instead of filling or coating inside the PCF’s air holes, the Gold (Au) layer is applied to the polished surface. The effects of the larger air holes’ diameter and the thickness of the Au layer are examined. To achieve effective RI sensing, the proposed design leverages the strong coupling between the core mode and the Surface Plasmon (SP) excitation mode. Modal dispersion, confinement loss, and electric field distributions are analyzed for analyte RI values ranging from 1.30 to 1.35 using the Finite Element Method (FEM). The sensor demonstrates improved plasmonic excitation with a maximum Wavelength Sensitivity (WS) of 3000 nm/RIU (Au = 45 nm), strong confinement loss of more than 788.39 dB/cm (at Au = 40 nm), and a highest Figure of Merit (FoM) of 62.5/RIU (at Au = 40 nm with RI = 1.32). The TiO₂ layer enhances mode coupling and resonance sharpness, while the optimized Au thickness boosts sensitivity and spectral resolution. Additionally, the blood components reach the WS of 5000 nm/RIU for plasma and 3000 nm/RIU for Krypton. Because of its high tunability and repeatable performance, the PCF–SPR biosensor is a promising choice for precise biochemical and biomedical sensing applications. Full article

Buildings account for approximately 40% of global energy consumption, with heating, ventilation and air conditioning systems being the primary contributor. Building management systems offer a promising solution for enhancing energy efficiency, particularly in retrofitting older or protected buildings. However, powering numerous wireless sensors required by BMS remains a logistical challenge. This study investigates the feasibility of harvesting thermal energy lost through windowpanes to power ultra-low-power IoT sensors, a concept that was not previously explored in the literature. A thermoelectric energy harvester was developed using a TEC1-12710 thermoelectric module and an EM8900 ultra-low-voltage DC-DC boost converter. Laboratory and field experiments were conducted to evaluate the system’s performance under various thermal conditions, with electrical energy accumulated in a 0.01 mF capacitive energy storage. In laboratory conditions, a temperature difference of ~1 °C enabled the system to generate up to 3.24 V with a power density of 3 mW/m². Field tests during winter yielded lower performance (1.43 V, 1.9 mW/m²), which was attributed to suboptimal thermal gradients and operating points. It was thus experimentally shown that windowpane heat losses can be effectively harnessed for indoor energy harvesting. While the current efficiency is limited, the approach shows promise for powering battery-free IoT devices. Full article