Search Results (703)

Background: Although Capacitive–Resistive Electric Transfer (TECAR) and vibration therapy (VT) are increasingly used in sports recovery, their effects on muscle oxygenation remain unclear. Objectives: This study compared the short-term influence of TECAR and VT on muscle oxygenation following eccentric exercise in young, active adults. We hypothesized that both interventions would support early metabolic recovery, as reflected by changes in muscle oxygenation, and potentially reduce the risk of musculoskeletal overuse. Methods: Forty-one young, recreationally active adults (age: 19 ± 2 years; height: 168 ± 9 cm; body mass: 63 ± 13 kg) were randomized into two groups: TECAR therapy and VT. Muscle oxygenation was assessed at baseline, post-exercise, and post-intervention using the arterial occlusion method with a MOXY muscle oxygenation monitor (Fortiori Design LLC, USA). The primary variables were mVO₂ (muscle oxygen consumption), ΔSmO₂ (change in oxygen saturation during occlusion), and ΔtHb (change in hemoglobin level during occlusion). Data were analyzed using a two-way repeated-measures ANOVA with post hoc Tukey tests, and statistical significance was set at p < 0.05. Results: Eccentric exercise significantly reduced mVO₂ in both groups (VT: −0.18 ± 0.40 to −1.62 ± 0.70; TECAR: −0.12 ± 0.40 to −1.24 ± 0.70), indicating decreased metabolic demand. Following recovery, mVO₂ increased in both groups (VT: −0.86 ± 0.50; TECAR: −0.35 ± 0.40), with no significant between-group differences (p > 0.05). ΔSmO₂ also decreased after exercise (VT: −0.7 ± 0.4 to −3.2 ± 0.9; TECAR: −0.9 ± 0.6 to −3.45 ± 0.7). After recovery, ΔSmO₂ partially returned to baseline (VT: −2.6 ± 0.8; TECAR: −1.35 ± 0.4), with no significant between-group differences. ΔtHb increased following exercise in both groups (VT: 0.03 ± 0.04 to 0.13 ± 0.09; TECAR: 0.03 ± 0.04 to 0.15 ± 0.07) and decreased after recovery to similar levels (VT: −0.05 ± 0.05; TECAR: −0.06 ± 0.04; p > 0.05). Conclusions: Both TECAR and VT were associated with improved muscle oxygenation during early recovery after eccentric exercise, as reflected by increases in mVO₂ and comparable ΔtHb responses. Although ΔSmO₂ tended to decrease more after VT, this difference was not statistically significant and should be interpreted cautiously. Overall, both modalities appear to be effective recovery-supporting strategies, while further controlled studies are needed to clarify their role in different athletic populations and exercise contexts. Full article

Electric-field-sensitive dielectrics play a crucial role in electric field induction sensing and related capacitive conversion, with interfacial polarization and charge accumulation largely determining the signal output. This paper introduces graphene/transition metal dichalcogenide (TMD) (MoSe₂, MoS₂, and WS₂) heterojunctions as functional fillers to enhance the dielectric response and electric-field-induced voltage output of flexible polydimethylsiloxane (PDMS) composites. Density functional theory (DFT) calculations were used to evaluate the stability of the heterojunctions and interfacial electronic modulation, including binding behavior, charge redistribution, and Fermi level-referenced band structure/total density of states (TDOS) characteristics. The calculations show that the graphene/TMD interface is primarily controlled by van der Waals forces, exhibiting negative binding energy and significant interfacial charge rearrangement. Based on these theoretical results, graphene/TMD heterojunction powders were synthesized and incorporated into polydimethylsiloxane (PDMS). Structural characterization confirmed the presence of face-to-face interfacial contacts and consistent elemental co-localization within the heterojunction filler. Dielectric spectroscopy analysis revealed an overall improvement in the dielectric constant of the composite materials while maintaining a stable loss trend within the studied frequency range. More importantly, calibrated electric field induction tests (based on pure PDMS) showed a significant enhancement in the voltage response of all heterojunction composite materials, with the WS₂-G/PDMS system exhibiting the best performance, exhibiting an electric-field-induced voltage amplitude 7.607% higher than that of pure PDMS. This work establishes a microscopic-to-macroscopic correlation between interfacial electronic modulation and electric-field-sensitive dielectric properties, providing a feasible interface engineering strategy for high-performance flexible dielectric sensing materials. Full article

The field of electrochemical devices, encompassing energy storage, fuel cells, electrolysis, and sensing, is fundamentally reliant on the electrode materials that govern their performance, efficiency, and sustainability. Traditional materials, while foundational, often face limitations such as restricted reaction kinetics, structural deterioration, and dependence on costly or scarce elements, driving the need for continuous innovation. Emerging electrode materials are designed to overcome these challenges by delivering enhanced reaction activity, superior mechanical robustness, accelerated ion diffusion kinetics, and improved economic feasibility. In energy storage, for example, the shift from conventional graphite in lithium-ion batteries has led to the exploration of silicon-based anodes, offering a theoretical capacity more than tenfold higher despite the challenge of massive volume expansion, which is being mitigated through nanostructuring and carbon composites. Simultaneously, the rise of sodium-ion batteries, appealing due to sodium’s abundance, necessitates materials like hard carbon for the anode, as sodium’s larger ionic radius prevents efficient intercalation into graphite. In electrocatalysis, the high cost of platinum in fuel cells is being addressed by developing Platinum-Group-Metal-free (PGM-free) catalysts like metal–nitrogen–carbon (M-N-C) materials for the oxygen reduction reaction (ORR). Similarly, for the oxygen evolution reaction (OER) in water electrolysis, cost-effective alternatives such as nickel–iron hydroxides are replacing iridium and ruthenium oxides in alkaline environments. Furthermore, advancements in materials architecture, such as MXenes—two-dimensional transition metal carbides with metallic conductivity and high volumetric capacitance—and Single-Atom Catalysts (SACs)—which maximize metal utilization—are paving the way for significantly improved supercapacitor and catalytic performance. While significant progress has been made, challenges related to fundamental understanding, long-term stability, and the scalability of lab-based synthesis methods remain paramount for widespread commercial deployment. The future trajectory involves rational design leveraging advanced characterization, computational modeling, and machine learning to achieve holistic, system-level optimization for sustainable, next-generation electrochemical devices. Full article

Silicon carbide (SiC) MOSFETs, as one of the representative power electronic devices, have faced reliability challenges due to threshold voltage (V_th) instability under dynamic gate stress. To explore the underlying mechanisms, this work investigates 4H-SiC MOS structures (P-MOS and N-MOS) under AC bias temperature instability (AC BTI) stress, utilizing a laser to generate minority carriers and simulate realistic switching conditions. Through combined capacitance–voltage (C-V) and gate current–voltage (J_g-V_g) characterizations on P-MOS and N-MOS devices before and after degradation at different temperatures, we reveal a critical temperature dependence in defect interactions. At room temperature, degradation is dominated by electron trapping in shallow interface states and near-interface traps (NITs). In contrast, high-temperature stress activates charge exchange with deep-level, slow states. Notably, a positive V_FB shift is consistently observed in both N-MOS and P-MOS devices under AC stress, confirming that electron trapping is the dominant cause of the commonly observed positive V_th shift in SiC MOSFETs. These findings clarify the distinct defect-mediated mechanisms governing dynamic V_th instability in SiC devices, providing fundamental insights for interface engineering and reliability assessment. Full article

The imperative for EMC-optimized gate drivers in Gallium Nitride (GaN)-based automotive DC-DC converters stems from the stringent CISPR 25 standards and GaN’s intrinsic high-speed switching characteristics, which paradoxically exacerbate electromagnetic interference (EMI). This review distinguishes itself by proposing a novel frequency-domain classification framework (Zone I: <50 MHz for conducted harmonics; Zone II: >50 MHz for switching noise and ringing), which systematically organizes and assesses gate driving techniques against the triad of fundamental GaN EMC challenges: pronounced capacitance nonlinearity, low threshold voltage, and extreme parasitic sensitivity. Unlike prior surveys that primarily catalog techniques, the analysis elevates the gate driver from a simple switch interface to the central “electromagnetic actuator” of the power stage, explicitly elucidating its pivotal role in mediating the critical trade-offs among switching speed, loss, and EMC performance. A comprehensive evaluation and comparison of advanced techniques—from spread-spectrum modulation for Zone I to adaptive current shaping and resonant topologies for Zone II—are provided, alongside an analysis of their design trade-offs. Furthermore, this review presents a first-of-its-kind, phased implementation roadmap towards holistic EMC compliance, integrating intelligent hybrid control, heterogeneous integration, and system-level co-design. This review bridges the gap between device physics and system engineering, offering structured design methodologies and a clear future direction for achieving electromagnetic integrity in next-generation automotive power electronics. Full article

Abnormal electrolyte levels can lead to failures in lead-acid batteries. The capacitive method, as a non-invasive liquid level inspection technique, can be applied to the nondestructive detection of electrolyte level abnormalities in lead-acid batteries. However, due to the high viscosity of sulfuric acid in lead-acid batteries, residual liquid films are easily adhered to the tube walls during rapid liquid level drops, resulting in significant dynamic measurement errors in capacitive methods. To eliminate dynamic measurement errors caused by residual liquid film adhesion, this study proposes a hybrid deep learning model—Poly-LSTM. This model combines polynomial feature generation with a Long Short-Term Memory (LSTM) network. First, polynomial features are generated to explicitly capture the complex nonlinear and coupling effects in the sensor inputs. Subsequently, the LSTM network processes these features to model their temporal dependencies. Finally, the time information encoded by the LSTM is used to generate accurate liquid level predictions. Experimental results show that this method outperforms other comparative models in terms of liquid level estimation accuracy. At a rapid drop rate of 0.12 mm/s, the average absolute error (MAE) is 0.5319 mm, the root mean square error (RMSE) is 0.7180 mm, and the mean absolute percentage error (MAPE) is 0.1320%. Full article

This study systematically investigates the influence of capacitor energy storage parameters on the energy utilization efficiency of the underwater electrochemical explosion process. By integrating spherical and cylindrical shock wave propagation models, the pulse shock wave energy under different capacitor energy storage levels was theoretically calculated and experimentally validated. The results indicate that the applicability of the shock wave propagation model depends on the distance and aquatic environment: the spherical model is more suitable for short-distance, deep-water conditions, whereas the cylindrical model performs better for long-distance or shallow-water conditions. Within the energy storage range of up to 100 J, increasing the capacitance significantly enhances both the pulse energy output and energy utilization efficiency. Specifically, as the stored energy increased from 13 J to 100 J, the shock wave energy rose from 0.051 J to 2.45 J, and the energy utilization rate improved from 0.39% to 2.45%. Nevertheless, the overall energy utilization efficiency remains below 10%. This study confirms that rationally configuring capacitor parameters can effectively regulate the discharge process, providing important experimental and theoretical support for optimizing energy utilization efficiency. Full article

This paper presents a comparative study of various artificial intelligence methods, including artificial neural networks (ANNs), recurrent neural networks (RNNs), k-nearest neighbors (KNN), random forests (RFs), and particle swarm optimization (PSO) techniques, to see which one can predict conducted electromagnetic interference (CEMI) better. The DC/DC converter simulations and experimental results demonstrated a high level of matching. According to the simulation results, the datasets were highlighted by varying key parameters related to the supply voltage, load current, switching frequency, duty cycle, component choice, PCB layout, filter capacitance, and gate resistance in a systematic way. During the assessment, each AI technique is checked regarding prediction accuracy, computational efficiency, and error rates using different metrics such as mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²). It is observed that KNN performs better than the other methods, giving only the lowest error in predictions and showing very fast computing speed. Furthermore, KNN gave the best results with R² above 0.97, MAE below 5.9 dBµV, and RMSE under 7.3 dBµV. This method worked better than others in all test cases. According to the measurements, the predicted and actual EMI levels match very well and show that the proposed method is strong and reliable. Further, basically, these results show that KNN has the same potential to work as an effective and efficient tool for predicting CEMI in power electronics. Its strong performance can further help in developing better and more reliable power systems for practical use, while the system itself provides valuable insights to engineers for electromagnetic compatibility design and compliance. Full article

In this paper, a flexible rectangular loop antenna is designed and proposed for ice, frost and wildfire detection. The antenna is composed of two concentric rings made of a flexible conductor. The proposed antenna was responsive to different materials based on distinct shifts in the resonant frequency, which was employed to differentiate between these materials. The antenna provides a wide response and sensitivity range to detect ice or frost with relative permittivity close to 3 and water with relative permittivity close to 72 at the same time. This wide sensitivity level is attributed to the internal loop which works with the external ring to form a capacitor with a capacitance varying with the relative permittivity of the material under test. The internal loop also enhances coupling with the material under test and fine-tunes the antenna’s response. The antenna achieved a maximum radiation efficiency of 97.1% and gain of 2.83 dBi at 2.45 GHz across the tested scenarios involving frost and ice. It also obtains a maximum radiation efficiency and gain of up to 6.67% and −8.27 dBi, respectively, for water at 40 °C and 50 °C, respectively. Additionally, the antenna preserves the same direction of maximum radiation for all of the investigated materials, which minimizes constraints on the receiving antenna’s radiation pattern requirements. The proposed antenna features simplicity, robust performance and a wide sensitivity range over temperatures between 0 and 50 °C, which makes it a good candidate for environmental monitoring. Full article

This paper proposes a novel seven-level switched-capacitor multilevel inverter featuring a shared front-end DC-link structure that achieves triple voltage gain with reduced component count. A distinctive feature of this design is its inherent capacitor voltage self-balancing capability, thereby eliminating the need for complex control algorithms typically associated with multilevel converters. Moreover, the topology demonstrates particularly significant advantages in three-phase implementations, where a single DC source, front-end switching devices, and capacitors can be shared across all phases—thus substantially reducing component count and system complexity compared to conventional designs. Additionally, this paper proposes an improved carrier-based modulation strategy for this topology requiring only a single triangular carrier, along with a systematic method for determining optimal capacitance values. Through detailed comparative assessment against state-of-the-art switched-capacitor seven-level inverters, the superior performance characteristics of the proposed topology are clearly demonstrated. Finally, simulation results under various operating conditions are presented and subsequently validated through experimental testing on a laboratory prototype, confirming the practical viability of the proposed solution. Full article

This article presents the design of a perovskite photovoltaic (PV)-based power management system, which uses a power converter (a four-stage bootstrap charge pump) to boost the output of the solar cell and supply selectable rectified power rails to CMOS image sensor circuit blocks. A perovskite photovoltaic, also known as a perovskite solar cell (PSC) was fabricated in the laboratory. The PSC has an open-circuit voltage of 1.14 V, short-circuit current of 1.24 mA, maximum power of 0.88 mW, and a current density of 20.68 mA/cm² at 62% fill factor. These measured forward scan parameters were closely reproduced with a solar cell simulation model. In a Cadence simulation that used 180 nm CMOS process, the power converter efficiently boosts the maximum output voltage of the PSC from 0.85 V to a rectified 3.7 V. Stage modulation and level shifting enable selectable output rails in the 1.2–3.3 V range to supply the image sensor circuit blocks. Keeping the output capacitance of the power converter much larger than the flying capacitance reduces the ripple voltage to approximately 73 µV, much smaller than the typical 1 mV in several other literatures. Through simulation, this work demonstrates the concept of directly using PSC (to be implemented on an outer ‘packaging’, not on a die) to supply CMOS image sensor power rails, in the same sense as in wearable devices and other consumer devices. This work highlights a path toward self-powered image sensors with improved conversion efficiency, compactness, and adaptability in low-light and variable operating environments. Full article

Managing complex and large-scale building facilities requires reliable, easily interpretable, and computationally efficient models. Considering the electrical-circuit analogy, lumped-parameter resistance–capacitance (RC) thermal models have emerged as both simulation surrogates and advanced tools for energy management. This review synthesizes recent uses of RC models for building energy management in large facilities and aggregates. A systematic review of the most recent international literature, based on the analysis of 70 peer-reviewed articles, led to the classification of three main areas: (i) the physics and modeling potential of RC models; (ii) the methods for automation, calibration, and scalability; and (iii) applications in model predictive control (MPC), energy flexibility, and digital twins (DTs). The results show that these models achieve an efficient balance between accuracy and simplicity, allowing for real-time deployment in embedded control systems and building-automation platforms. In complex and large-scale situations, a growing integration with machine learning (ML) techniques, semantic frameworks, and stochastic methods within virtual environments is evident. Nonetheless, challenges persist regarding the standardization of performance metrics, input data quality, and real-scale validation. This review provides essential and up-to-date guidance for developing interoperable solutions for complex building energy systems, supporting integrated management across district, urban, and community levels for the future. Full article

The expansion force generated by lithium-ion batteries during charge–discharge cycles is a key indicator of their structural safety and health. Recently, flexible pressure-sensing technologies have emerged as promising solutions for in situ swelling monitoring, owing to their high flexibility, sensitivity and integration capability. This review provides a systematic summary of progress in this field. Firstly, we discuss the mechanisms of battery swelling and the principles of conventional measurement methods. It then compares their accuracy, dynamic response and environmental adaptability. Subsequently, the main flexible pressure-sensing mechanisms are categorized, including piezoresistive, capacitive, piezoelectric and triboelectric types, and their material designs, structural configurations and sensing behaviors are discussed. Building on this, we examine integration strategies for flexible pressure sensors in battery systems. It covers surface-mounted and embedded approaches at the cell level, as well as array-based and distributed schemes at the module level. A comparative analysis highlights the differences in installation constraints and monitoring capabilities between these approaches. Additionally, this section also summarizes the characteristics of swelling signals and recent advances in data processing techniques, including AI-assisted feature extraction, fault detection and health state correlation. Despite their promise, challenges such as long-term material stability and signal interference remain. Future research is expected to focus on high-performance sensing materials, multimodal sensing fusion and intelligent data processing, with the aim of further advancing the integration of flexible sensing technologies into battery management systems and enhancing early warning and safety protection capabilities. Full article

To address the limitations of optical interferometry (strict environmental requirements, high cost) and piezoelectric methods (hysteresis, creep) in micro-displacement measurement, this study proposes a collaborative measurement approach based on the parallel plate capacitance principle—with its core innovation lying in integrated optimization rather than original principles. Unlike existing studies that separately optimize mechanics, hardware, or algorithms, this work achieves the first synergy of three components: a mechanical coupling mechanism (integrating a high-resolution optical mount and a micrometer) for parallel plate regulation, a 21-bit capacitance detection module based on the STM32-PCAP01 (with a resolution of 0.0001 pF), and a linear response model relating capacitance to the reciprocal of displacement. Experimental validation confirms its engineering feasibility for sub-nanometer-level precision: with a 10 cm plate radius and 3–20 mm initial spacing, the system achieves 277.215 ± 0.244 pF·mm sensitivity and <0.05 μm displacement resolution. The relative error of micro-displacement measurement in the 10 μm range is less than 1.56%. Based on the hardware resolution, the system possesses the theoretical capability to detect displacements as low as 10⁻⁸ to 10⁻⁹ m. Compared to laser interferometry, it operates stably in common industrial environments without vibration isolation or darkrooms, reducing costs by ~90% while maintaining comparable accuracy. This cost-effective solution enables online precision measurement in semiconductor manufacturing and MEMS testing, with its multi-physics collaborative design offering a new paradigm for intelligent sensor development. Full article

This study aimed to evaluate the potential of electrical parameters for assessing the quality and health-promoting properties of fruit purees derived from twelve superfruit species native to north-eastern Poland. Their physicochemical characteristics were determined using reference methods, while electrical measurements were conducted with a custom-built system based on an equivalent circuit model (RCC). The recorded electrical parameters included impedance, admittance, and series and parallel capacitance across a frequency range of 100 Hz–1 MHz. Pronounced differences in dry matter, extract, ash content, and bioactive compounds were observed between species. Cluster analysis and PCA revealed that purées with higher bioactive compound content exhibited strong and statistically significant correlations between electrical parameters reflecting impedance and admittance and variables such as dry matter, total extract, and ash (p < 0.01). In contrast, capacitance-based parameters showed weaker and more composition-specific relationships. In purées with lower levels of bioactive compounds, the number and strength of correlations were reduced. These findings indicate that frequency-resolved electrical parameters may serve as a complementary, non-destructive tool for assessing composition-related variability in fruit purées and may support rapid quality evaluation alongside conventional assays. Full article