Electric field-induced bending in ferroelectric ceramics has attracted considerable interest recently, but the mechanisms of this phenomenon are not fully understood. In this work, we show that electric field-induced bending deformation can be observed in (Ba,Sr)TiO3 ceramics with different concentrations of SrTiO3. Experimental results, such as reduction in bending deformation by removing surface layers and increasing ceramic thickness, suggest that this phenomenon can be well explained by the polarized surface layer mechanism, a key mechanism for the flexoelectric response in ferroelectric ceramics. The variation in bending deformation behaviors with ceramic compositions and temperature was further measured, and the results also support the surface layer mechanism. Our results indicate that the magnitude of the applied electric field is also an important factor affecting the bending behavior of the ceramics due to the modification of the polarization state of bulk when the field is higher than the coercive field of the ferroelectric ceramics. Our study provides a deeper understanding of the electric field-induced bending phenomenon in ferroelectric ceramics.

Electrical phenomena associated with vegetation in contact with medium voltage conductors: summary of field experiments

The photovoltaic (PV) solar energy sector has expanded rapidly in response to the urgent need to diversify the global electrical matrix. The efficiency of silicon-based PV modules depends on several factors, with cell operating temperature being one of the most critical. High temperatures reduce open-circuit voltage and energy yield, making accurate thermal-electrical modeling essential. The main objective of this work is to develop a robust thermoelectrical model, and based on its results, perform a detailed parametric sensitivity analysis to understand how the input variables influence the output variables of interest, namely, the cell temperature and efficiency. The model integrates thermal and electrical phenomena in a multi-physics approach. The thermal component is one-dimensional and transient, accounting for heat conduction across module layers, internal generation in cells and glass, and radiative and convective exchanges at both surfaces. The electrical component uses the single-diode model to reproduce current-voltage (I-V) characteristics. A transient differential sensitivity analysis identifies the parameters most affecting cell temperature. Sensitivity coefficients are evaluated over a simulated day to capture dynamic effects. Results show that ambient air temperature, solar irradiation on the tilted plane and open-circuit voltage exert the greatest influence, requiring precise determination to improve model reliability. Conversely, parameters with low sensitivity, such as glass thermal conductivity, are less critical, enabling simplifications without sacrificing accuracy. This systematic approach enhances the predictive capability of PV performance modeling, supporting both accurate system simulations and informed design decisions.

Lithium metal anodes hold great promise for next-generation high-capacity batteries but face rapid degradation due to dendrite growth and dead lithium formation. To understand these degradation mechanisms, we develop an electrochemical phase field model based on nonequilibrium thermodynamics [1]. This model, validated against analytical solutions, quantitatively captures essential processes such as diffusion, migration, reaction, and capillarity. The flexibility of the phase field approach allows our model to adeptly handle multiphysics coupling among chemical, electrical, thermal, and mechanical phenomena, which are crucial for accurate physics-based battery modeling. Here, we demonstrate our method through three key applications: Dead Lithium Mechanism and Mitigation. We discover an asymmetry mechanism for dead lithium formation [2]. We propose a new metric to quantify dead lithium risk, closely related to Coulombic efficiency, enabling several strategies to mitigate dead lithium and improve cycle life. Roles of Anisotropy in Electrode Design. By modeling surface energy and kinetic anisotropy, we show that thermodynamic anisotropy is crucial for nucleation, while kinetic anisotropy plays a significant role during growth [3]. The crystal orientation (texture) of the electrode can be engineered to reduce dead lithium formation and enhance performance. Multiscale Understanding of the Double Layer Effect. By coupling mesoscale phase field dynamics with nanoscale electric double layer physics, we developed a multiscale phase field model to investigate the impact of the electric double layer on dendritic electrodeposition. Optimizing double layer properties can help to stabilize electrodeposition morphology. This work provides a powerful computational tool for understanding degradation mechanisms, which can lead to improved battery design and optimization for industrial applications.

Objective.To study the electrical performance of large-diameter spherical-like catheters used for pulsed field ablation (PFA) in treating cardiac arrhythmias, especially evaluating the effect of electrode radius, applied voltage and maximum current delivered on the PFA-induced lesion depth.Approach.An analytical model was proposed which used spherical coordinates to estimate the value of the electric field around a spherical metal electrode. Lesion depth was estimated by the myocardium lethal electric field threshold. The paper also reviews the literature and proposes a relationship between this threshold and the total cumulative duration of the pulses applied during PFA.Main results.Despite the model´s simplicity in geometric terms, the estimated values of baseline impedance (44 Ω) and impedance drop due to PFA (7-9 Ω) are closely related to those observed in experimental studies (43 Ω and 5-7 Ω, respectively). The PFA-induced lesion size potentially increases with the applied voltage, but this is only true if the generator can provide the electrical current needed to maintain the required voltage. A nonlinear relationship was identified between PFA-induced lesion depth and the electrode diameter, which was also modulated by the total duration.Significance.The analytical model offers a physical explanation for the PFA-Induced electrical phenomenon, while emphasising how certain technical characteristics (maximum current, applied voltage, electrode diameter) can affect the depth of the lesion.

Achieving low contact resistance in advanced quantum electronic devices remains a critical challenge. With the growing demand for faster and energy-efficient devices, 2D contact engineering offers a promising solution. Beyond graphene, 1T'-WTe2 has attracted attention for its excellent electrical transport, quantum phenomena, and Weyl semimetallic properties. Here, the direct wafer-scale growth of 1T'-WTe2 via molecular beam epitaxy (MBE) and its use as a 2D contact for layered materials, such as InSe, are demonstrated. The 1T'-WTe2/InSe interface exhibits a barrier height nearly half that of conventional metal contacts, and its contact resistance is reduced by a factor of 21, effectively suppressing Fermi-level pinning and enabling efficient electron injection. InSe/1T'-WTe2 photodetectors show broad photoresponsivity (0.14-217.58A W-1) under NIR to DUV illumination with fast rise/fall times of 42/126ms, compared to lower responsivity (8.65×10-4-3.64A W-1) and slower response (150/144ms) for InSe/Ti-Au devices. The 1T'-WTe2/InSe devices thus exhibit ≈60 × higher responsivity and ≈4 × faster response than conventional metal contacts. These results establish MBE-grown 1T'-WTe2 as an effective 2D electrode, enhancing photodetection performance while simplifying device architecture, making it a strong candidate for next-generation nanoelectronic and optoelectronicdevices.

Electrified powertrains in the transportation sector have increased significantly in recent years, thanks to the need for decarbonization of the on-the-road transport means. However, management of powertrains still deserves particular attention to assess necessary improvements for reducing electric consumption and increasing the mileage of the vehicles. In this regard, electric motor cooling is essential for maintaining optimal performance and longevity. In fact, as electric motors operate, they generate heat due to electric and magnetic phenomena as well as mechanical friction. If not properly managed, this heat can lead to decreased efficiency, accelerated wear, or even failure of critical components. Effective cooling systems ensure that the motor runs within its ideal temperature range, reducing the occurrence of the mentioned concerns. This improves operational reliability and, at the same time, contributes to energy savings and reduced maintenance costs over the components’ life. In this study, the cooling of the rotor of a 130-kW electric motor via refrigerating fluid circulating inside the shaft has been investigated. Two configurations of fluid passages have been considered: a direct-through flow crossing the shaft along its axis and a hollow shaft with recirculating flow, with three types of rotating helical configurations at different pitches. The benefits when using nanofluids as a cooling medium have also been evaluated to enhance the heat transfer coefficient and decrease temperature values. Compared with the baseline configuration using standard fluids (water), the proposed solution employing nanofluids demonstrates effectiveness in terms of heat transfer coefficients (up to 28% higher than pure water), with limited impact on pressure losses, thus reducing rotor temperature by up to 30 K with respect to the baseline. This study opens the possibility of integrating the cooling of the rotor with whole electric motor cooling for electric and hybrid powertrains.

Twisted bilayer transition metal dichalcogenides (TMDs) have generated diverse unusual electrical and optical phenomena and can provide a powerful platform for designing nanodevices with tunable interlayer interaction. Striving to explore novel excitons with spin response in these semiconductor systems is highly desirable, as they highlight the possibility to access complex electronic band structure and magneto-exciton effect, thereby facilitating efficient spin-based information storage via exciton degrees of freedom. Here, fabrication of bilayer WSe2/Fe5GeTe2 (FGT) heterostructures with different stacking phases is reported, and a new hybridized excitonic state T* is defined in both 3R and 2H bilayer WSe2, which exhibits strong correlations dependent on the FGT spin order. This spin-dependent hybridized exciton is demonstrated to originate from the coupling between injected spin-polarized electrons and neutral excitons, because of the spin-cross-polarized band that obstructs the normal electron-hole annihilation process. Besides, the difference in the coupling strength of the T* exciton attributed to the distinct stacking symmetries in twisted bilayer WSe2 is further unveiled. These findings open an accessible avenue for designing tailored excitonic states in twisted bilayers, thus offering prospects for the future applications of stacking-engineered opto-spintronics at the integration level.

Radiofrequency ablation (RFA) is a widely used minimally invasive technique for solid tumors. Real-time feedback on the thermoelectric effects induced by RFA is very important for precise, personalized treatment. Current computer simulations help predict the electrical and thermal phenomena related to RFA, their high computational cost limits practical clinical use, especially for real- time monitor. In this study, a physics-integrated neural network-based model was proposed to predict the coupled real-time electric and temperature fields during the treatment. This approach, similar to traditional simulation methods, simulates the physical changes during the radiofrequency ablation process with input of treatment parameters. The combined deep learning model consists of a DeepONet network predicting the electrical potential distribution and a coupled ConvLSTM network forecasting the temperature distribution over time. The networks were trained using results from the thermoelectric coupling FEM model, and validated through bio-mimic phantom experiments. The DeepONet network achieves a mean absolute error (MAE) of 0.0241 and a mean maximum relative error (MRE) of 1.44%. The coupled ConvLSTM network achieves an MAE of 0.0286, an MRE of 3.46%, and a Dice score of 0.9334 for areas above 45°C. The model developed can provide coupled temperature and electric field predictions for a 120-s RFA process with varying properties in less than 1 s. This rapid prediction method is expected to be integrated with control calibration algorithms in the future, enabling the acquisition of real-time three-dimensional temperature fields and facilitating more precise temperature control.

This study aims to evaluate the effects of total ionizing dose (TID) radiation on the performance of n−MOSFET current mirrors. We propose an ovel experimental approach to analyze the interaction between charge trapping in the MOSFET gate oxide and the resulting current mirror degradation by subjecting devices to TID doses from 50 krad(Si) to 300 krad(Si) using a 60Co gamma source Experimental data show that threshold voltage shifts by up to 1.31 V and transconductance increases by 27%. This degradation leads to this a reduction of more than 10% in current mirror output accuracy occurs at the highest dose. These quantitative criteria establish a clear benchmark for assessing the impact of TID on current mirror performance. These effects are attributed to positive charge trapping in the gate oxide and at the Si–SiO2 interface induced by ionizing radiation. This study focuses exclusively on radiation effects; electrical stress phenomena such as over−voltage or electrostatic discharge (ESD) are not addressed. The results highlight the critical importance of accounting for TID effects when designing high−performance n−MOSFET current mirrors for radiation−hardened applications.

Invited Paper: Liquid flow electrification phenomenon: Review

Abstract Designing and conducting experiments to simulate real-world conditions with varying gas insulators and electrode geometries can be intricate. Ensuring repeatability and accuracy across experiments poses a challenge due to the inherent complexity of electrical phenomena in gas insulators. This manuscript proposes an investigation of electrical characteristics for rod to point within gas insulators. The proposed approach combines the Mother Optimization Algorithm (MOA) and Circular Dilated Convolutional Neural Network (CDCNN), Hence it is named as MOA-CDCNN technique. The major objective of the paper is to investigate the electrical characteristics of rods and govern breakdown voltage, electric field distribution, and discharge patterns in gas-insulated systems. The MOA is used to optimize the gas insulation system. The CDCNN method is utilized to predict the insulator parameters. The performance of the proposed method is put into practice on the MATLAB platform and contrasted with methods that are currently in use. Particle swarm optimization (PSO), heap-based optimizer (HBO), and wild horse optimizer (WHO) all yield inferior results compared to the proposed strategy. From the result, it is concluded that the proposed method shows a maximum electric field of 6.8 kV/mm compared with other existing methods.

In the history of physics, one of the deepest integrations that classically combined the phenomena of magnetism, optics and electricity into one theoretical structure is represented by Maxwell’s equations. This analysis gives a thorough mathematical formulation of the four fundamental equations elegantly. It provides a deeper understanding beginning from their historical basis in the works of Faraday, Gauss and Ampère and finishing in Maxwell’s vital input – the displacement current. We illustrate how these four equations beautifully surface from experimental laws when united with advanced vector calculus via comprehensive mathematical analysis. A disclosure that changed the concept of light is a consequence of Maxwell’s equations which has surpassed classical electromagnetism that led immediately to the forecast of electromagnetic waves. James C. Maxwell, when synthesizing magnetic and electricity, he has proven that light alone is an electromagnetic phenomenon. Today, in this modernized world, Maxwell’s equation has become the basis for electronic and electrical engineering. Nevertheless, examined here are some of their restrictions, notably in relativistic contexts and quantum mechanical where more enhanced theories become crucial. This analysis goals to give both thorough mathematical treatment and a well-defined understanding of the foundation equations of theoretical physics.

The polymer network liquid crystal (PNLC), in which a polymer network is formed within a nematic liquid crystal, can be regarded as a porous medium. In this study, we discover electroconvection in the PNLC and examine its nonlinear, nonequilibrium physical properties. As the monomer weight fraction increases, distortions in the convective pattern emerge and the temporal fluctuations slow. The polymer network hinders the reorientation of the liquid crystal molecules, thereby suppressing the motion of the convection pattern. Furthermore, measurements of the electric Nusselt number, which characterizes the electrical transport phenomena, reveal that, as the monomer weight fraction increased, the threshold voltage for the onset of convection increased and the efficiency of charge transport decreased. This study quantitatively evaluated the nonlinear, nonequilibrium phenomena of the PNLC as flow phenomena in porous media.

Abstract This study investigates the electrostatic dissipation dynamics and sedimentation-induced electrification phenomena during the loading and resting processes of benzene tank trucks. Based on electrostatic dissipation equations and the mechanical equilibrium characteristics of impurity particle sedimentation in benzene media, we establish a computational model for liquid surface potential generated by particle sedimentation electrification in benzene storage tanks. The research reveals the correlation between tank electrostatic potential and dissipation time under variations of benzene medium parameters. These findings offer crucial engineering references for determining scientifically grounded resting durations and mitigating electrostatic accident risks in petrochemical transportation operations.

Influencing the adsorptive processes of gases by external stimuli is an ongoing research task of academic and technological relevance. Technologically external stimuli like pressure, vacuum, temperature, magnetic field, or electrical phenomena are the most common ones with which adsorptive and desorptive processes can be influenced. In the case of pure electric field swing adsorption (EFSA) of solid/gas mixtures, however, experimental knowledge concerning carbon materials is lacking so far. A new approach to the electrical field effect on gas adsorption and desorption is presented. Ar, N2 and CO2 interact with an all‐solid composite material composed of activated porous carbon and silica characterized by a high amount of charged interfaces under isothermal conditions and ambient temperature. The intimate contact of both components in the composite allows for the formation of multiple resistor‐conductor interfaces enabling the reversible physisorption of these gases using electric fields in the lower V and mA range. The adsorptive/desorptive swing effect depends on the polarizabilities of the gases in particular their dipoles and to an even larger extent on the field induced quadrupole moments of the probe gases Ar, N2 and CO2.

The dielectric relaxation behavior of polymeric materials is critical to their performance in electronic, insulating, and energy storage applications. This study presents an electrical fractional model (EFM) based on fractional calculus and the complex electric modulus (M*=M'+iM″) formalism to simultaneously describe two key relaxation phenomena: α-relaxation and interfacial polarization (Maxwell-Wagner-Sillars effect). The model incorporates fractional elements (cap-resistors) into a modified Debye equivalent circuit to capture polymer dynamics and energy dissipation. Fractional differential equations are derived, with fractional orders taking values between 0 and 1; the frequency and temperature responses are analyzed using Fourier transform. Two temperature-dependent behaviors are considered: the Matsuoka model, applied to α-relaxation near the glass transition, and an Arrhenius-type equation, used to describe interfacial polarization associated with thermally activated charge transport. The proposed model is validated using literature data for amorphous polymers, polyetherimide (PEI), polyvinyl chloride (PVC), and polyvinyl butyral (PVB), successfully fitting dielectric spectra and extracting meaningful physical parameters. The results demonstrate that the EFM is a robust and versatile tool for modeling complex dielectric relaxation in polymeric systems, offering improved interpretability over classical integer-order models. This approach enhances understanding of coupled relaxation mechanisms and may support the design of advanced polymer-based materials with tailored dielectric properties.

Biological research and agriculture are increasingly benefiting from the use of artificial intelligence algorithms, which are becoming integral to various areas of human activity. Fundamental knowledge of the mechanisms of plant germination, growth/development, and reproduction is the basis for plant cultivation. Plants provide food and valuable biochemicals and are an important element of a sustainable natural environment. An interdisciplinary approach involving basic science (biology and informatics), technology (artificial intelligence), and farming practice can contribute to the development of precision agriculture, which in turn increases crop and food production. Nowadays, a progressive elucidation of the mechanisms of plant growth/development involves studies of interrelations between electrical phenomena occurring inside plants and movements of plant organs. Recently, there have been increasing numbers of reports on methods for classifying plant electrograms using statistical and artificial intelligence algorithms. Artificial intelligence procedures can identify diverse electrical signals—signatures associated with specific environmental abiotic and biotic factors or stresses. At the same time, a growing body of research shows methods of precise and fast analysis of time-lapse videos via automated image analysis and artificial intelligence to study the movement and growth/development of plants. In both research fields, scientists introduce modern and promising methods of studying plant growth/development. Such basic research along with technological innovations will contribute to the development of precision agriculture and an increase in yields and production of healthier food in future.

The article of this study focuses on the defects caused by the platinum (Pt) atoms implanted in the silicon (Si) with the changes of their electrophysical properties after the high temperature thermal treatments. The introduction of the platinum atom into the silicon crystal lattice creates deep-level defect centers where the sensitive electrical properties and phenomena caused by temperature changes can be observed more clearly than in intrinsic defects. Of particular focus on platinum atoms incorporation, extensive studies have demonstrated significant changes of the defect structure in silicon and substantial transformation of its electrophysical properties related to the electrical conduction mechanisms and carrier scattering phenomena. Exclusive electrophysical effects were observed for platinum-doped silicon samples, which underwent high-temperature thermal annealing at 1050 °C and 1150 °C, primarily associated with the clustering of boron and platinum atoms, and the formation of complex defect aggregates. These thermal treatments enhance the interaction of isolated defects leading to the formation of clusters and complex defect entities, which greatly enhances the scattering mechanisms. These interactive effects of defects were found to be dominant in changing charge carrier transport and recombination processes in silicon crystals. Furthermore, experimental results showed a combination of scattering mechanisms that includes neutral defects, deep energy levels induced by platinum impurities, and their respective charged states. Platinum-induced defects thus enable multiple scattering mechanisms, and such hybrid mechanisms play a critical role in a silicon electrical and electronic behaviors, which influence the semiconductor applicability of the materials in high-temperature or high-performance, etc.

This study aims to analyze the utilization of virtual reality (VR) technology in simulating Ohm's Law experiments in physics education. Ohm's Law is a fundamental concept in physics that relates voltage, current, and resistance in electrical circuits, which is essential for understanding various electrical phenomena. However, conventional physics experiments are often limited by the availability of equipment, infrastructure, and costs. Virtual reality offers a solution by providing immersive and interactive simulations, allowing students to conduct experiments without physical constraints. This study employs literature review approach to evaluate trends in the use of VR in Ohm's Law experiments. The results show that VR technology can enhance students' understanding of abstract physics concepts, increase engagement and motivation, and offer more flexible and safer access to physics experiments. This study also notes that combining real and virtual experiments yields better results than relying on one type of experiment. Therefore, the use of VR in physics education holds significant potential to overcome existing limitations and support more effective and engaging learning.