Search Results (794)

This research study conducts a computational analysis of a two-terminal (2T) Perovskite-on-silicon (PVK-Si) solar cell with a tandem configuration. The motivation for this analysis arises from the outstanding potential of PVK-Si solar cells to surpass the efficiency limitations of conventional photovoltaic technology. The tandem configuration utilizes a combination of CsSnBr₃ in the top sub-cell and crystalline silicon (c-Si) in the bottom sub-cell. After optimizing parameters of the top sub-cell (FTO/TiO₂/CsSnBr₃/rGO/Au), which included the thicknesses of CsSnBr₃ (500 nm), TiO₂ (40 nm), rGO (50 nm), the interface defects (10¹³ cm⁻²), and the bandgap of CsSnBr₃ (1.78 eV), the PVK-Si tandem device was simulated. As a result, the top CsSnBr₃ sub-cell achieved an efficiency of 21.62%, while the bottom silicon sub-cell achieved an efficiency of 23.48%. Subsequently, the sub-cells were interconnected in series using filtered spectra and current-density matching. After interpolating the J-V curves, the tandem exhibited a global efficiency of 29.76%, a fill factor (FF) of 85.30%, a matched current density (J_SC) of 19.02 mA/cm², and an open-circuit voltage (V_OC) of 1.83 V. The EQE results confirmed efficient photon management via complementary sub-cell absorption. The performance is competitive with experimental lead-based tandems and exceeds that of current lead-free simulations. Therefore, this research proposes a viable pathway for the development of non-toxic, cost-effective tandem solar systems with manufacturing capabilities. Full article

Asymmetric Cascaded Multilevel Inverters (ACMLIs) have emerged as a prominent solution for medium- and high-power applications due to their ability to provide an increased number of output voltage levels with fewer power switches. However, maintaining low total harmonic distortion (THD) and ensuring robust stability under varying operating conditions remain significant challenges. This study experimentally validates a voltage-feedback Proportional-Resonant (PR) control strategy for a seven-level ACMLI. Unlike conventional current-feedback methods, the proposed approach directly regulates the output voltage, providing superior harmonic suppression and enhanced steady-state accuracy. The stability and dynamic performance of the controller were theoretically analyzed using Bode diagrams and root locus methods, and further verified through the MATLAB Curve Fitting Tool (CFT) with a high correlation (R² = 0.9989). Experimental results demonstrate that the integration of the PR controller significantly improves power quality, reducing the current THD from 6.55% to 3.68% and the voltage THD to 2.94%. These findings confirm that the system fully complies with IEEE 519 standards and outperforms several existing strategies in the literature. The results establish the voltage-feedback PR control as a robust, high-precision, and practical alternative for power quality-oriented multilevel inverter applications in modern energy systems. Full article

The widespread integration of wind energy conversion systems has fundamentally reshaped modern power grid architecture. However, the limited dynamic response of wind turbine (WT) converters during grid faults—particularly their inability to provide sufficient reactive current and maintain voltage stability under severe dips—necessitates a redefinition of the conventional low-voltage ride-through (LVRT) curve. This study addresses this challenge by proposing a Mutation-Adaptive Mean Variance Mapping Optimization (A-MVMO) algorithm for the control of grid-side converters (GSCs) in wind farms (WFs). To systematically assess post-fault voltage recovery, a Time-Segmented Analysis for Voltage Recovery (T-SAVR) approach is developed with a multi-objective function. The performance of the proposed A-MVMO is benchmarked against standard MVMO and conventional particle swarm optimization (PSO) under both moderate (0.7 pu) and severe (0.15 pu) voltage dips using the IEEE 39-bus system implemented in DIgSILENT/PowerFactory. The results demonstrate that A-MVMO achieves fast, oscillation-free voltage recovery with negligible overshoot (<1%) and lower current injection than PSO and MVMO, while satisfying all engineering constraints. Moreover, the co-optimization of Park-level and turbine-level controllers ensures seamless coordination, as evidenced by the close tracking between the farm-wide reactive power reference and the aggregated turbine response. The T-SAVR method proves essential for focusing optimization on controllable recovery dynamics, yielding a superior LVRT curve. Full article

Weak alternating electric fields are widely used in neuromodulation techniques such as transcranial alternating current stimulation (tACS), yet the precise biophysical mechanisms underlying neuronal responses remain incompletely understood. Current computational models often neglect the electrical properties of the extracellular microenvironment, limiting their predictive accuracy. Motivated by experimentally observed frequency-dependent modulation of neuronal activity, we developed a two-compartment model of hippocampal CA3 pyramidal neurons in which extracellular resistance is explicitly parameterized and systematically examined as a key factor influencing neuronal response properties under external electric fields. Within a dual-compartment Hodgkin–Huxley framework, the neuron is divided into a “soma–basal dendrite unit” and an “apical dendrite unit,” accounting for voltage polarization induced by external fields. Using phase-locking ratio curves and three-dimensional parameter response surface, we systematically characterized neuronal sensitivity to field parameters and examined how potassium equilibrium potential ($V_K$) and extracellular resistance ($R_{\mathrm{out}}$) modulate these responses. Our results demonstrate that increasing $R_{\mathrm{out}}$ enhances neuronal responsiveness to external fields, while $V_K$ variations primarily regulate intrinsic excitability. These findings provide mechanistic insights into the frequency-dependent modulation of neuronal responses under weak electric fields, consistent with phenomena observed in biological neural systems, and provide a mechanistic and theoretical framework for understanding the joint effects of electric field amplitude and frequency on neuronal sensitivity to weak electric fields, which may help inform future neuromodulation strategies. Full article

Accurate assessment of the State of Health (SOH) is critical for battery management systems in aviation. As a step towards this goal, this study presents a proof-of-concept for a novel SOH estimation method based on an Improved Particle Swarm Optimization-Extreme Learning Machine (IPSO-ELM) model, validated under controlled laboratory cycling conditions. Although traditional Extreme Learning Machines (ELM) are widely used due to their fast computation and good generalization, their random parameter initialization often leads to unstable convergence and limited accuracy. To address these limitations, this paper proposes a novel SOH estimation method based on an Improved Particle Swarm Optimization (IPSO) algorithm to optimize the key parameters of ELM. Three health indicators (HI)—constant-current charging time, equal-voltage-drop discharge time, and average discharge voltage—were extracted from charge–discharge curves as model inputs. The IPSO algorithm dynamically adjusts the inertia weight, introduces a constriction factor and a termination counter to enhance global search capability and avoid local optima. Experimental results on open-source datasets (B005, B007, B0018) and laboratory datasets (A001, A002) demonstrate that the proposed IPSO-ELM model achieves a Root-Mean-Square Error (RMSE) below 0.7% and a Mean Absolute Percentage Error (MAPE) below 0.5%. Compared with standard ELM and PSO-ELM models, it significantly outperforms them in accuracy (e.g., for B0018, RMSE is reduced to 0.21% and MAPE to 0.14%), convergence speed, and robustness, establishing a foundation for future development of aviation-ready SOH estimators. Full article

This study proposes a novel state of health (SOH) estimation method by extracting two-level hierarchical features linked to fundamental degradation mechanisms. At the module level, the length of the incremental power curve during constant current charging is extracted, capturing cumulative effects of subtle changes. At the cell level, a combined temperature-weighted voltage inconsistency curve is constructed. The state of charge (SOC) at its distinct knee point within the high-SOC range is a key indicator, signifying the accelerated failure stage where polarization and thermoelectric feedback intensify. This knee-point SOC quantitatively reflects the degree of SOH degradation, making it a valid feature for accurate SOH estimation. The proposed Temporal Convolutional Network–Transformer–Squeeze-and-Excitation (TCN–Transformer–SE) model assigns weights to these features via Squeeze-and-Excitation (SE) and uses Temporal Convolutional Network (TCN) and Transformer branches for parallel local and global temporal decisions. Aging experiments demonstrate the method’s superiority through multi-feature comparison, ablation studies, and benchmark evaluation, achieving a maximum mean absolute error (MAE) of 0.0031, a root mean square error (RMSE) of 0.0038, a coefficient of determination (R²) of 0.9937 and a mean absolute percentage error (MAPE) of 0.3820. The work provides a fusion estimation framework with enhanced interpretability grounded in electrochemical analysis. Full article

There has been no experimental work on CuCrO₂-ZnSnN₂ heterojunctions (HJs), though theoretical work shows that their photoelectric conversion efficiency is around 20%. Here, CuCrO₂ thin films and p CuCrO₂-n ZnSnN₂ HJs are prepared by varying the sputtering power of the Cu-Cr alloy target while the other parameters are held constant. The as-deposited Cu_xCr_yO_z thin films are amorphous, with CuCrO₂ as the major phase. The CuCrO₂ thin films are p-type conductive, with an optical band gap of about 3.64–3.84 eV. The ZnSnN₂ thin films are wurtzite and n-type conductive. The dark current density J versus voltage V curve measurements show that all the HJs showed rectification, while only the samples deposited at 40 and 50 W had a photo-induced current. Further analysis shows the HJs deposited at 40 W have the lowest shunt conductance, saturation current density, and trap density, implying an effect of fabrication conditions on the properties of HJs. Full article

The accelerating integration of electric vehicles (EVs) presents considerable operational challenges for distribution networks, particularly through aggravated voltage deviations and compromised protection coordination during periods of simultaneous charging. In response, this study introduces a novel protection-constrained Binary Evolutionary Algorithm (BEA) designed for expedited electric vehicle-oriented Distribution Network Reconfiguration (DNR) to enhance EV hosting capacity without necessitating costly infrastructure upgrades. The proposed framework uniquely embeds the inverse time–current characteristics of protective fuses—termed Protection Curve Consideration (PCC)—within the optimization process. By explicitly accounting for the thermal inertia of protection devices, the algorithm identifies reconfiguration strategies that uphold voltage stability under elevated EV transportation loading, including configurations typically deemed infeasible by conventional voltage-driven approaches. This selective coordination precludes unnecessary fuse operations, thereby preserving the continuity of electric vehicle charging services. Simulation results on a 16-bus radial distribution system, evaluated under four high-demand scenarios reflective of concentrated EV transportation charging, validate the efficacy of the BEA-PCC methodology. The approach achieves a maximum voltage deviation reduction of up to 15.2%, thereby enhancing power quality for all consumers. Moreover, compared to standard metaheuristic techniques, it reduces Energy Not Supplied (ENS) by 8% and switching operations by 20%, contributing to improved grid resilience and operational efficiency. These outcomes underscore the potential of BEA-PCC as an effective real-time control strategy for distribution system operators seeking to accommodate increasing electric vehicle penetration while safeguarding protection coordination and minimizing customer disruptions. Full article

Photovoltaic (PV) performance in desert environments is significantly hindered by soiling and partial shading. To bridge the gap in empirical data for extreme Saharan conditions, this study presents a novel techno-economic assessment of uniform horizontal shading (UHS) specifically conducted in Mauritania. Controlled outdoor experiments were performed using a 250 W crystalline silicon PV module and a PVPM 2540C I–V curve tracer, applying progressive shading levels from 2.5% to 20%. The novelty of this work lies in the integration of high-resolution experimental I–V/P–V characterization with a localized techno-economic model for five pre-commercial PV plants. It was observed that PV modules are exceptionally sensitive to shading; specifically, a mere 10% shaded area leads to a catastrophic 90% drop in power and current, while the voltage remains remarkably stable. Thermographic analysis further validates the thermal gradients and bypass diode functionality. By quantifying the financial impacts, this research highlights that cumulative economic losses across the five real-world sites reached 87.95%, exceeding 55,000 MRU. These findings provide a strategic framework for optimizing PV systems in arid terrains and offer a robust tool for enhancing the design and operation of large-scale solar applications in desert environments. Full article

Serotonergic projections innervate both the dorsal and ventral cochlear nuclei; however, the electrophysiological consequences of serotonergic input in the ventral cochlear nucleus (VCN) remain incompletely understood. This study aimed to identify the serotonin receptor subtypes involved in serotonergic modulation of stellate cells in the mouse anteroventral cochlear nucleus (AVCN) and to determine the underlying ion channel mechanisms. Whole-cell patch-clamp recordings were performed in acute brain slices obtained from postnatal day 12–17 mice. Bath application of serotonin (25 µM) induced membrane depolarization (~5 mV) and increased action potential firing. Pharmacological experiments demonstrated that antagonists of 5-HT1A, 5-HT2A, and 5-HT2C receptors partially reversed the depolarization and reduced serotonin-induced inward currents, indicating that multiple receptor subtypes contribute to serotonergic excitation. Blockade of hyperpolarization-activated cyclic nucleotide-gated (HCN) channels with extracellular Cs⁺ suppressed approximately 95% of the serotonin-induced depolarization and inward current, implicating HCN channel-mediated Ih as a principal ionic mechanism. Serotonin significantly increased Ih amplitude. Analysis of steady-state activation revealed no statistically significant shift in V0.5; however, under near-resting membrane potential conditions, serotonin significantly reduced the slope factor of the activation curve, consistent with altered voltage sensitivity of Ih gating. Immunohistochemical analysis confirmed the presence of 5-HT1A, 5-HT2A, and 5-HT2C receptors in the AVCN. Together, these findings indicate that serotonergic excitation of AVCN stellate cells is mediated by coordinated activation of multiple 5-HT receptor subtypes and primarily involves modulation of HCN-dependent subthreshold membrane dynamics. Full article

The self-directed channel (SDC) class of memristors employs a multilayer architecture that is designed to enable robust Ag ion conduction, long cycling lifetime, and thermal stability. While several layers contribute to mechanical and chemical reliability, two layers primarily govern the electrical behavior: the amorphous Ge–chalcogenide active layer that is adjacent to the bottom electrode and the overlying metal–chalcogenide source layer. In this work, we investigate how the variation in the chalcogen species in these two layers influences switching characteristics in the pre-write regime, both in the pristine state and after a write/erase cycle, as well as the conduction behavior at room temperature. The devices were fabricated using Ge-rich chalcogenides containing O, S, Se, or Te, combined with SnS, SnSe, or Ag₂Se metal–chalcogenide layers. The DC current-voltage measurements were analyzed using the standard linearization approaches to examine whether the transport behavior in the pre-write regime exhibits characteristics that are associated with Ohmic, Schottky, Poole–Frenkel, or space charge limited conduction. These measurements specifically probe the pre-write region of the I-V curve, where early ionic redistribution and structural rearrangement precede the abrupt formation of the conductive channels responsible for the resistive switching. The results show that the chalcogen composition strongly affects the threshold voltage, the resistance window, and the onset of field-enhanced transport, reflecting the differences in ionic distribution and channel formation dynamics. The results indicate that transport evolves with a bias and a compliance current, transitioning between regimes that are influenced by the interface injection and bulk-limited conduction, depending on the material stack. These findings clarify the role of chalcogen chemistry in governing the SDC switching behavior and provide guidance for the material selection in application-specific device design. Full article

Mitigating pollution in cities where transportation powered by fossil fuels has a significant impact on human health is a public health priority. Although electric vehicles are one solution to this problem, their high acquisition and maintenance costs have limited their rapid adoption; therefore, other solutions may be useful in supporting reduction efforts. Therefore, this paper proposes a power control system for an Anion Exchange Membrane Fuel Cell (AEMFC) generator powered by hydrogen with the capacity to supply a direct current (DC) motor of 0.75 kW. A mathematical model of the AEMFC was proposed, and the parameters were adjusted to obtain polarization and power curves defining safe operating ranges (12.45–17.9 V). A boost converter was designed to increase the voltage of the cell output to 48 V to meet the requirements of the DC motor. The performance of the power converter was studied by analyzing its small-signal ripple, operating modes, and efficiency. The models and simulations were implemented using MATLAB and PSIM. A cascaded control system with proportional–integral (PI) and proportional–integral–derivative (PID) controllers was implemented to maintain voltage stability in the presence of input and load variation. The results show that the AEMFC is reliable and that the boost converter presents an efficiency higher than 98% in continuous mode. The robustness of the model was validated through simulations and using a prototype. Full article

Metallic contaminants in powdered foods represent a serious safety concern. Therefore, effective detection is crucial for food safety. This study aimed to develop an electric-field-based detection system and quantitatively evaluate its performance. An alternating (+/−) electrode array (gap 1–2 mm) was designed, and resonance analysis identified 15 kHz with a 2 mm gap as the optimal operating condition. Using an IGBT-based high-voltage source, 1.35 kV was selected to ensure stable operation without partial discharge. A real-time algorithm based on a minimum current-change threshold was implemented, and detection responses to stainless steel (SUS), aluminum (Al), and copper (Cu) particles in three size classes (<0.5, 0.5–1.0, and 1.0–2.0 mm) were evaluated using hit/miss modeling and logistic regression to obtain probability-of-detection (POD) curves and limits of detection (LOD). The system achieved POD ≥ 0.9 for 1.0–2.0 mm particles; in the 0.5–1.0 mm range, observed POD values were 84%, 90%, and 68% for SUS, Al, and Cu, respectively. Safety was assessed by COMSOL-based localized heating simulation validated by infrared thermography and by ozone monitoring for real-time operation. Compared with conventional inspection approaches, the proposed system provides a compact, cost-effective architecture while reporting inspection-oriented reliability metrics (POD/LOD) for process-line deployment. Full article

TiO₂ films with a thickness of 20 nm were obtained by anodizing a titanium film with an aluminum sublayer on a glass substrate. The I–V characteristics were studied in a temperature range of 100–300 K. Three linear sections can be distinguished on the I–V curves in logarithmic coordinates with a bias voltage of up to 2.5 V. The first section is an ohmic section with a bias voltage sweep from 0 V. The second section is associated with the space-charge-limited currents. The third section is characterized by the flow of Poole–Frenkel currents. In the third section, the slope of the approximating line is greater than in the second one due to the flow of higher currents. This is explained by the transition of electrons from donor centers to trap levels, which leads to a decrease in the number of free traps available for capturing electrons injected from the contacts into the conduction band. The obtained values of the Fermi energy of 0.032 and 0.028 eV for temperatures from 100 to 300 K, respectively, indicate that the electron traps in the forbidden zone of TiO₂ are shallow. The value of the donor level energy E = 0.082 eV is close to the values of the activation energy of thermal conductivity. This indicates the formation of donor centers in anodic TiO₂ by the mechanism of donor vacancies. In anodic TiO₂ films, the concentration of electron traps is 10¹⁵ cm⁻³, which is approximately three orders of magnitude less than their concentration in anodic TiO₂ films obtained by vacuum deposition. Full article

Bipolar Polymer Membranes (BPMs) enable the creation of large, stable pH gradients by driving water dissociation (WD) at the cation/anion junction under reverse bias, a process central to electrodialysis, CO₂ capture, and emerging acid–alkaline water electrolysis. Yet despite decades of study, the mechanism by which intense interfacial electric fields accelerate WD remains debated and is often modeled with ad hoc assumptions. In this study, we present a power dissipation model in which minority ions from water autoprotolysis act as carriers that continuously dissipate field-supplied power in the hydrated nanometric junction. This dissipative input increases the local probability of heterolytic O–H bond cleavage and analytically leads to a quadratic dependence of the dissociation rate constant on the field. Without adjustable parameters, the model reproduces the required orders of magnitude for the enhancement ratio k_d(E)/k_d(0), where k_d(E) is the field-enhanced water dissociation rate constant and k_d(0) is its zero-field value across typical BPM fields, and yields a quadratic current–voltage junction law. A proof-of-principle measurement on a commercial Fumasep^® FBM bipolar membrane confirms the quadratic current–voltage trend, supporting a power-dissipation-driven water dissociation mechanism and providing a concise, falsifiable baseline for future studies. Full article