Search Results (533)

Vitamin B6 (pyridoxine) is a biologically relevant micronutrient involved in numerous biochemical processes and may also participate in redox-related reactions. In the present study, the electrochemical behavior and stability of vitamin B6 were investigated in a 0.1 M sodium fluoride (NaF) electrolyte using platinum (Pt) as the working electrode, with particular emphasis on the effects of silver nanoparticles (Ag nanoparticles, 70 mg·L⁻¹) on interfacial redox processes. Cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and UV-Vis spectroscopy were combined to characterize the molecular-electrode interface and the response of vitamin B6 under electrochemical stress. CV demonstrated that vitamin B6 substantially modifies the anodic and cathodic response of Pt, whereas incorporation of Ag nanoparticles partially attenuated the pronounced cathodic processes induced by vitamin B6. EIS revealed a progressive decrease in charge-transfer resistance from 2.19·10³ Ω·cm² in NaF to 1.81·10³ Ω·cm² after vitamin B6 addition and to 882 Ω·cm² in the presence of Ag nanoparticles, accompanied by increased apparent interfacial capacitance and enhanced interfacial heterogeneity. UV-Vis spectroscopy further showed that Ag nanoparticles modify the optical response of the vitamin B6-containing system and exhibit a characteristic plasmonic absorption band. Under galvanostatic electrolysis at 50 mA·cm⁻², vitamin B6 degradation followed apparent first-order kinetics, while the presence of Ag nanoparticles decreased the degradation rate constant from 4.70·10⁻³ to 3.02·10⁻³ min⁻¹ and increased the apparent half-life from 147 to 229 min. These results indicate that Ag nanoparticles substantially influence the redox environment and electrochemical stability of vitamin B6, reducing its degradation under oxidative electrochemical conditions. The combined electrochemical and spectroscopic results provide new insight into the interfacial redox behavior of a biologically relevant vitamin in the presence of metallic nanoparticles and may contribute to understanding antioxidant-related molecular stability and redox processes in complex chemical environments. Full article

Reliable tactile object shape recognition on robotic hands is often achieved using dense sensor arrays or vision-based tactile skins, which increase fabrication complexity and computational cost. This work demonstrates that high-recognition performance can instead be achieved through principled sparse sensing. A minimal multimodal tactile system is developed by fusing soft capacitive stretch sensors at the proximal interphalangeal and metacarpophalangeal joints of the fingers with a sparse six-element palmar pressure array, integrated into a human-like hand mechanically constrained to emulate robotic grasping under a controlled and repeatable protocol. Using an ANOVA-based channel selection, low-informative metacarpophalangeal signals are identified and removed, reducing the number of sensors at the finger joints while improving classification accuracy. A lightweight multi-layer perceptron operating on this low-dimensional input achieves 95.4% size-invariant recognition accuracy across 12 rigid objects representing four geometric primitives—cuboid, sphere, cylinder, and cone—outperforming the denser baseline. Ablation studies confirm the complementary roles of finger-joint deformation, which encodes curvature cues, and palmar force distribution, which captures contact topology; neither modality alone achieves comparable performance. Beyond accuracy, the proposed design reduces sensor count, wiring, and computational requirements, enabling embedded-ready deployment. The results show that data-driven sensor placement, rather than sensors at all finger joints, can yield sufficient grasp-based shape recognition, offering practical guidance for tactile perception in resource-constrained robotic hands. Full article

Recent studies have proposed post-processing strategies that iteratively reconstruct a partial segment of current solutions using Neural Combinatorial Optimization (NCO) solvers, thereby improving their performance on large-scale Vehicle Routing Problem (VRP) instances. However, the reconstruction subproblem is essentially a Shortest Hamiltonian Path Problem (SHPP) instance, which differs fundamentally from the original VRP variant on which the solver is trained. Consequently, the NCO solver may suffer from performance degradation during reconstruction due to limited generalization across problem variants. Moreover, relying solely on the solver may be insufficient to effectively identify and correct complex intersections or suboptimal topological structures. To address these limitations, we propose a post-processing strategy termed Hybrid Reconstruction Framework (HRC). Specifically, HRC first fine-tunes the NCO solver on SHPP instances and then exploits the enhanced solver to perform large-neighborhood random reconstruction. Subsequently, HRC conducts small-neighborhood reconstruction using 2-opt and kNN-DGR. The experimental results on both synthetic and real-world Traveling Salesman Problem and Capacitated Vehicle Routing Problem instances demonstrate that HRC substantially improves the performance of two representative NCO solvers on large-scale instances and achieves better overall performance than the state-of-the-art reconstruction strategy. Finally, ablation studies further validate the effectiveness of all proposed designs. Full article

Lead contamination remains a significant environmental and public health concern, creating a demand for analytical platforms that combine sensitivity, simplicity, and long-term stability. In this work, a graphene-containing molecular membrane matrix was developed for the fabrication of single-piece all-solid-state potentiometric sensors for Pb(II) detection. The sensing membrane consisted of poly(vinyl chloride), plasticizers, a Pb(II)-selective ionophore, lipophilic ionic sites, and dispersed graphene nanostructures, forming an integrated molecular sensing interface. Within the membrane phase, selective complexation of Pb(II) ions by the ionophore was coupled with graphene-assisted ion-to-electron transduction, enabling efficient signal generation without the need for a separate solid-contact layer. The influence of graphene incorporation and membrane thickness on sensor performance was systematically investigated. Among the tested configurations, a membrane prepared from 40 µL of sensing cocktail provided the best overall performance, combining high electrical capacitance, favorable surface properties, and superior potential stability. SEM imaging revealed a homogeneous membrane morphology without large graphene agglomerates, indicating effective dispersion of graphene within the polymer matrix. The optimized sensor exhibited a near-Nernstian slope of 30.3 mV dec⁻¹, a linear response range from 1.0 × 10⁻⁷ to 1.0 × 10⁻² M, a detection limit of 6.3 × 10⁻⁸ M, and a potential drift of only 0.35 mV h⁻¹. These results demonstrate that direct incorporation of graphene into an ion-selective membrane is an effective strategy for constructing robust and scalable single-piece potentiometric sensors for Pb(II) monitoring and highlight the potential of developed membrane materials for electrochemical sensing applications. Full article

Water scarcity in semi-arid regions makes efficient irrigation scheduling a priority, yet farm-level adoption of soil-moisture monitoring remains limited by the cost, low portability and installation complexity of commercial sensing systems. This study presents the development and field validation of WaziSense, a low-cost, solar-powered Internet-of-Things (IoT) smart tensiometer, developed within the OSIRRIS platform for soil-water monitoring and irrigation scheduling. The device couples a Watermark granular-matrix sensor and a DS18B20 temperature probe to an ATmega328P microcontroller (Arduino Pro-Mini, 3.3 V, 8 MHz) with long-range LoRa communication and a maximum-power-point-tracking (MPPT) solar-charging stage, logging soil matric potential and soil temperature every 15 min. An open-source edge/cloud stack (WaziGate, WaziApp) retrieves weather forecasts from an open API and runs an automated machine learning (AutoML) regression pipeline that forecasts soil-water dynamics and the time to a user-defined threshold, from which irrigation is scheduled and its applied volume verified by a flow meter. The system was deployed at three bioclimatic sites in Tunisia (durum wheat at Cherfech, citrus at Nabeul, apple at Sbeitla), with tensiometers installed at 20 and 40 cm depths, and validated against commercial 10HS capacitive probes coupled to a ZL6 data logger, with which the co-located readings were significantly correlated (r = 0.81). Calibrated readings showed a strong relationship between soil–water content and soil–water potential (R² = 0.99), and the edge forecasting model reproduced soil–water dynamics on unseen data (Sbeitla apple site, 5-day horizon) with R² = 0.73, RMSE = 0.35, MAE = 0.23 and MPE = 12.52%. With a material cost under about 90 EUR per node and fully open-source hardware and software, WaziSense is one to two orders of magnitude cheaper than commercial monitoring stations, offering an affordable, reproducible and scalable tool for data-driven irrigation in water-limited agriculture. Full article

Hybrid Electric Vehicles (HEVs) require compact wireless power transfer (WPT) systems, but extreme parking misalignment severely degrades transmission efficiency by virtually eliminating magnetic coupling. To address this, we propose a novel End-Attached Split Plates Hybrid Coupler designed to satisfy strict HEV packaging constraints while withstanding extreme spatial offsets. The theoretical foundation lies in the complementarity of coupling mechanisms: structurally secured mutual capacitance provides a robust electric coupling path that actively defends against the collapse of magnetic coupling. This ensures the overall hybrid coupling remains at a viable level, successfully preventing resonant deviation. The inherent misalignment tolerance is quantitatively validated through equivalent circuit analysis and Finite Element Method (FEM) simulations, extracting key coupling parameters and focusing on scattering parameter networks and electromagnetic field distributions. By isolating the coupler’s performance from complex active control systems, our analysis demonstrates that highly efficient and stable energy transfer is achievable purely through structural and electromagnetic optimization. Consequently, the proposed passive coupler offers a highly reliable and inherently robust solution for severe misalignment conditions in HEV wireless charging applications. Full article

The rapid growth of battery energy storage systems, renewable energy integration, electric vehicles, and DC microgrids has significantly increased the demand for compact, efficient, and bidirectional isolated DC–DC converters. Conventional Dual Active Bridge (DAB) converters commonly employ high-frequency transformers to provide galvanic isolation and bidirectional power transfer. Although transformer-based DAB converters offer excellent performance, their magnetic components increase converter volume, weight, core losses, leakage inductance, manufacturing complexity, and overall cost. Consequently, recent research has explored alternative high-frequency energy transfer techniques based on capacitive coupling, aiming to reduce magnetic components while preserving efficient resonant power conversion.This paper proposes a Capacitively Coupled Dual Active Bridge (CC-DAB) converter employing high-power metallized polypropylene (MKPH) capacitors as the high-frequency energy transfer medium. The proposed converter operates at a relatively low switching frequency while investigating the safe operating conditions of the capacitive coupling network to ensure reliable and efficient power transfer. A microcontroller-based single-phase-shift (SPS) modulation strategy is implemented to generate the gate-driving signals of the full bridges, whereas the switching frequency is selected to achieve zero-voltage switching (ZVS) throughout the investigated operating range. The phase-shift angle ($\varphi$) regulates the transferred power by controlling the voltage difference between the primary and secondary bridges across the capacitive coupling network. The proposed converter is analyzed theoretically and validated through simulation and experimental testing. Experimental results demonstrate stable bidirectional power transfer, soft-switching operation, and a peak conversion efficiency of 91.3% at a relatively low switching frequency of 52 kHz. The experimental verification confirms the practical feasibility of capacitive coupling for resonant bidirectional power conversion and demonstrates its potential as an alternative architecture for low- and medium-power applications requiring compact size, high efficiency, reduced magnetic component requirements, and reversible energy transfer. Furthermore, the proposed topology contributes to the ongoing development of transformerless resonant converters by experimentally validating a high-frequency capacitive coupling network capable of supporting efficient bidirectional power flow under practical operating conditions. Full article

The present work introduces a fully resolved three-dimensional thermal Lattice Boltzmann framework developed to investigate the impact of freely moving, finite-size spherical particles on natural convection within a cubic enclosure. The fluid-phase momentum and energy fields are resolved using coupled double-distribution function kinetic approach, while the solid phase is governed by explicitly coupled linear, angular, and thermal conservation equations. To accurately map the moving spherical surfaces onto the Eulerian lattice grid, a second-order linear interpolated bounce-back scheme is implemented. The conjugate heat transfer between the phases is simplified via a lumped capacitance model, assuming negligible internal thermal resistance within the solid spheres. Short-range particle–particle and particle–wall interactions are handled using Glowinski’s repulsive force model. The spatial accuracy of the framework is validated using a circular Taylor–Couette flow benchmark—demonstrating second-order spatial convergence and a differentially heated natural convection in a cubic cavity benchmark, yielding bulk Nusselt numbers within $1\%$ of established literature data. This validated tool is subsequently used to analyze the complex interplay between particulate motion and bulk thermal transport efficiency. Analysis of the temperature fields reveals that the overall thermal structure is governed primarily by the Rayleigh number, while the low particle concentration produces only minor modifications to the convective heat transfer. In contrast, the particle distribution exhibits a strong dependence on the flow intensity. Full article

Many decision systems require search over continuous parameters or discrete route structures. This study examines whether a rank-weighted population architecture can support both representations while retaining an interpretable decision mechanism. We propose the Chinese Bureaucratic System Algorithm (CBSA), which transforms objective-based population ranks into either continuous movement or edge-based routing probabilities. We expect rank aggregation to be most useful when higher-ranked solutions contain stable information that can be reused in later searches. The framework is instantiated as a continuous variant (CBSA-C) and a discrete routing variant (CBSA-D), and its computational complexity and limiting search properties are analyzed. On the 2014 Congress on Evolutionary Computation (CEC2014) benchmark suite, CBSA-C obtains the best average rank among five algorithms and is strongest on multimodal and hybrid functions. On 50 capacitated vehicle routing instances, CBSA-D ranks second behind iterated local search while outperforming four population- or edge-based baselines. Across all 56 Solomon time-window routing instances, it is most competitive on clustered classes and weaker on random and mixed classes. These results support a conditional interpretation: rank-weighted aggregation is useful when objective ranks are informative and the representation contains reusable structure, while stronger local-search methods remain preferable when such structure is absent. Full article

Memtranstors are emerging higher-order memory elements that establish a state-dependent constitutive relationship between electric charge and magnetic flux, making them attractive for adaptive analog electronics, neuromorphic computing, nonlinear dynamical systems, and memory-enabled signal processing applications. However, existing memtranstor emulators predominantly rely on operational amplifiers, analog multipliers, current conveyors, or behavioral models, leading to increased circuit complexity and limited suitability for monolithic CMOS integration. This paper presents a unified transistor-level dynamic-threshold MOS (DTMOS) framework for realizing both charge-controlled and flux-controlled memtranstor emulators. The proposed architectures synthesize direct and inverse memtranstances through capacitive state integration, state-dependent DTMOS conductance modulation, and current-domain affine processing, thereby eliminating the need for composite active building blocks. Closed-form analytical expressions are derived for both constitutive relations and explicitly related to transistor-level parameters, bias conditions, and state-storage elements. The theoretical framework is further supported by comprehensive analyses of channel-length modulation, finite output resistance, device mismatch, DTMOS body-effect deviations, leakage mechanisms, pseudo-resistor non-idealities, parasitic capacitances, and small-signal stability. Cadence Virtuoso simulations performed in a 180 nm triple-well CMOS technology validate the analytical predictions and demonstrate the characteristic butterfly shaped pinched hysteresis loops of both emulators. The proposed circuits operate from a single 0.8 V supply while dissipating approximately 22 μW and 36 μW for the charge-controlled and flux-controlled realizations, respectively, and exhibit electronic tunability, together with robustness against process and temperature variations. Representative implementations of reconfigurable frequency-selective circuits and a memtranstor-based envelope detector further demonstrate the practical applicability of the proposed architectures. To the best of the author’s knowledge, this work presents the first unified transistor-level DTMOS constitutive synthesis framework for realizing both direct and inverse memtranstive behavior, providing a scalable foundation for future adaptive mixed-signal integrated circuits, programmable analog memory systems, neuromorphic hardware, and in-memory computing platforms. Full article

Capacitive sensors implemented in complementary metal-oxide-semiconductor (CMOS) technology are widely used in lab-on-chip (LoC), biomedical, and microfluidic systems. While most capacitive sensor interfaces are designed for high-resolution capacitance quantification, many practical applications require only binary decisions, event detection, or state discrimination. In such scenarios, conventional readout architectures introduce unnecessary circuit complexity, power consumption, latency, and data-processing overhead. This paper presents a CMOS cross-coupled-based capacitance detector (CBCD) that directly converts the imbalance between a sensing capacitance and a reference capacitance into a digital output. By exploiting regenerative positive feedback in a dynamic latch architecture, the proposed detector integrates sensing, comparison, and digitization within a single stage, eliminating the need for analog amplification, analog-to-digital conversion, frequency-based readout, and external thresholding circuitry. Circuit-level simulations show the ability to detect extremely small capacitance differences, demonstrate robust operation across a wide range of input capacitances, and achieve negligible power consumption. Process-corner, noise, and Monte Carlo analyses further verify reliable operation in the presence of device mismatch and process variations. Owing to its compact structure, digital-native output, and energy-efficient operation, the proposed CBCD is well suited for decision-driven sensing applications, including droplet presence detection, bubble monitoring, threshold-based diagnostics, event detection, and time-of-evaporation (ToE) measurements. The proposed architecture provides a scalable and low-complexity front-end solution for next-generation CMOS-integrated sensing platforms. Full article

Geoelectric field observations play a vital role in geophysical exploration, geological disaster early warning, and underground resource detection. Traditional contact non-polarisable electrodes, which require burial and electrolyte coupling, are hindered by several issues, such as limited adaptability to challenging terrain, significant electrode potential drift, and high susceptibility to environmental interference. Existing non-contact electric field sensors often exhibit insufficient coupling capacitance, poor impedance matching for ultra-weak high-impedance signals, and inadequate low-frequency noise suppression, rendering them unsuitable for the precise acquisition of natural microvolt-level geoelectric field signals. To address these challenges, this study introduces an innovative non-contact geoelectric field sensor with a three-layer composite structure. The sensor operates based on the principle of a parallel-plate capacitor, with a conductive silver paste layer at the top acting as the signal acquisition electrode plate, which forms an equivalent parallel-plate capacitance model with the ground to achieve non-contact capacitive coupling for geoelectric field detection. The intermediate layer uses lead zirconate titanate (PZT) piezoelectric ceramics as a support medium with a high dielectric constant. At the bottom is a silicon-based, flexible, sensitive ground-contacting layer with high elasticity, which allows it to adapt to micro-level surface irregularities, eliminating air gaps between the electrode plate and the ground, increasing plate-to-ground coupling capacitance, and ensuring the stability of the capacitance. The three-layer structure was created using a dry-press sintering integration approach, which eliminates interlayer bonding materials while ensuring consistent dielectric performance and efficient charge transfer. Additionally, a specialised signal-conditioning circuit was designed to match the ultra-high-impedance sensitive unit, utilising the ADA4528-2 ultra-low-noise precision operational amplifier, which achieved low-loss conversion and strong noise suppression for ultra-weak high-impedance charge signals. The circuit simulation results demonstrate that the designed circuit achieves an input impedance of no less than 10 TΩ, an effective operating bandwidth from 0.02 Hz to 20 kHz, and a voltage noise density lower than 1.5 μV/√Hz at 10 Hz, fully covering the ultra-low-frequency effective band of natural geoelectric fields. Field experiments comparing artificial and natural field signals revealed that the proposed sensor could be quickly deployed by simply attaching it to the ground without burial. Its time-domain waveform consistency and frequency-domain component matching were nearly identical to those of commercial standard solid non-polarisable electrodes, with a cross-correlation coefficient greater than 0.98, indicating no significant potential drift or power-frequency interference. By structurally eliminating the inherent electrode potential difference, the sensor offers advantages such as ease of deployment, strong environmental adaptability, high precision for weak signal acquisition, and excellent engineering substitutability. It is well suited for long-term geoelectric field observations in complex field scenarios, including deserts, Gobi areas, and frozen soil regions, and provides a high-performance, novel sensing solution for geoelectric field detection in extreme environments. Full article

Traditional soil management relies on “whole-field” averages, which leads to resource waste and environmental degradation under anthropogenic pressures. While combining electromagnetic induction (EMI) and remote sensing is known for digital soil mapping, current approaches lack a unified, automated framework to handle complex multi-source data dependencies for local-scale precision irrigation. To overcome this limitation, this study introduces a novel integrated methodology that couples high-resolution geophysical datasets and remote/proximal sensing through an automated machine learning workflow, capturing dynamic soil–human interaction boundaries more precisely than traditional empirical overlays. The general methodology was tested in a vineyard plot within the Torre Guaceto Natural Reserve (Southern Italy). Spatial datasets from EMI and remote sensing were integrated. Crucially, the K-means clustering algorithm was deployed early in the workflow to optimize the fused datasets and classify the plot into homogeneous zone clusters. The machine learning approach successfully identified two distinct main soil clusters. The spatial boundaries of these zones were rigorously validated using in situ soil moisture data from capacitance sensors, showing a statistically significant variance in volumetric water content between the two zones. This study demonstrates that integrated machine learning workflows can accurately delineate precision agricultural zones without relying on high-cost exhaustive sampling. It is recommended that farmers and managers within sensitive nature reserves adopt this cluster-based Variable Rate Application (VRA) for water and fertilizers to optimize resource efficiency and prevent nutrient leaching into underlying aquifers. Full article

The deadbeat predictive control (DPC) method has received increasing research interest in the grid-forming converter control strategy, due to its advantages of fast response in emergency grid scenarios and great potential in utilizing a system multi-time-step predictive optimization strategy. However, the voltage–current double-loop DPC of a grid-forming converter is sensitive to the filter inductance and capacitance parameters, resulting in a steady-state tracking error under parameter mismatch conditions. To address this issue, this manuscript proposes a double closed-loop steady-state error compensation strategy for grid-forming converters using double-loop DPC. Based on an analysis of the DPC algorithm and the mechanism of performance degradation caused by parameter mismatch, compensation terms are designed for the inner current loop and outer voltage loop respectively. The compensation terms are constructed from the feedback errors, effectively and rapidly suppressing the performance degradation caused by parameter mismatch, without introducing complex observers that may degrade the system dynamic response speed. A simulation model, which includes both the physical model of the electrical circuit and the discrete-time controller with sample-and-hold characteristics, is established to verify the proposed control strategy under different operating conditions, including load transient and inductor parameter mismatch. The results demonstrate that the proposed compensation method significantly reduces the steady-state tracking error caused by parameter mismatch while preserving the fast dynamic response characteristic of DPC, thereby substantially improving the accuracy of active power output and enhancing the system’s robustness against parameter deviations. Full article

Active ripple suppression branches (ARSBs) are widely employed in switching power converters interfacing ripple-sensitive devices such as batteries, supercapacitors, hydrogen electrolyzers, fuel cells, and photovoltaic panels. Conventional ARSBs share the main converter DC-link voltage and require inductance comparable to that of the primary power stage, resulting in high semiconductor voltage stress and bulky magnetic components. Recent studies have proposed supplying the ARSB from a lower auxiliary voltage source, significantly reducing both inductance value and semiconductor voltage ratings. This paper shows, however, that lowering the ARSB rating while keeping the series capacitance value unaltered inherently increases residual current ripple, degrading ripple-cancellation performance. It is then demonstrated that this limitation should be overcome by increasing the ARSB capacitance in inverse proportion to the rating reduction, thereby restoring ripple suppression performance. Furthermore, it is revealed that for converters operating at a fixed duty cycle, a unique operating point exists where the ARSB capacitor can be eliminated without sacrificing the ripple attenuation ability of the circuit. The resulting capacitorless implementation reduces component count, size, complexity, and cost while improving ripple suppression. Simulation and experimental results validate the theoretical analysis and confirm the feasibility and effectiveness of the proposed capacitorless open-loop operating ARSB. Full article