Electric Charge Research Articles

Charged Dyson shells supported in curved spacetimes of spherically symmetric central compact objects

AbstractIt is proved that charged thin shells (charged Dyson shells) can be supported in unstable static equilibrium states around spherically symmetric central compact objects. The regime of existence of the composed central-compact-object-static-charged-shell configurations is characterized by the inequalities $$\sqrt{m(m+2M)}<|q|<M+\sqrt{M^2+m^2}$$ m ( m + 2 M ) < | q | < M + M 2 + m 2 , where $$\{m,q\}$$ { m , q } are respectively the proper mass and the electric charge of the supported shell and M is the mass of the central compact object (a black hole or an horizonless compact star). We reveal the physically interesting fact that the supported charged shells become marginally-stable in the $$|q|/\sqrt{m(m+2M)}\rightarrow 1^+$$ | q | / m ( m + 2 M ) → 1 + limit, in which case the lifetime (instability timescale) of the composed system can be made arbitrarily large. Our analysis goes beyond the test shell approximation by properly taking into account the exact gravitational and electromagnetic self-interaction energies of the spherically symmetric shell in the curved spacetime. In particular, the existence of the composed compact-object-charged-shell static configurations in the Einstein–Maxwell theory is attributed to the non-linear electromagnetic self-energy of the supported shell.

Divergent imaginings: Transitioning to decarbonised mobility in ‘post-coalonial’ County Durham

ABSTRACT What happens when the various parties involved in constructing decarbonised futures’ infrastructure diverge in their imaginaries? Much of the published research on the sociotechnical imaginaries relating to electric vehicles (EVs) describes the creation of the future of decarbonised transport as a process mired in conflict, with various interested parties represented as strenuously disagreeing in their assessment of the most efficacious solution. The aim of the article is to offer an alternative account, based upon data gathered through participant observation, interviews, and grey literature. It describes the sort of personal transportation futures currently being imagined in the United Kingdom. The focus is specifically on the installation of electric vehicle charge points. The author contrasts Whitehall’s national vision for this infrastructure with the ‘post-coalonial’ vision of officers of Durham County Council in North East England have articulated an alternative, a ‘post-coalonial’ vision, and finds that the vision of both the British civil service and Government of the United Kingdom focused on private ownership and commuting, while Durham County Council envisioned publicly accessible charge points that enabled various types of different journeys. Despite the striking differences the conclusion is that contrary to the findings of previous studies the existence of these divergent infrastructural imaginaries led not to conflict but to co-existence.

Physicochemical Rationale of Matrix Effects Involved in the Response of Hydrogel-Embedded Luminescent Metal Biosensors

There is currently a critical need for understanding how the response and activity of whole-cell bacterial reporters positioned in a complex biological or environmental matrix are impacted by the physicochemical properties of their micro-environment. Accordingly, a comprehensive analysis of the bioluminescence response of Cd(II)-inducible PzntA-luxCDABE Escherichia coli biosensors embedded in silica-based hydrogels is reported to decipher how metal bioavailability, cell photoactivity and ensuing light bioproduction are impacted by the hydrogel environment and the associated matrix effects. The analysis includes the account of (i) Cd speciation and accumulation in the host hydrogels, in connection with their reactivity and electrostatic properties, and (ii) the reduced bioavailability of resources for the biosensors confined (deep) inside the hydrogels. The measurements of the bioluminescence response of the Cd(II) inducible-lux biosensors in both hydrogels and free-floating cell suspensions are completed by those of the constitutive rrnB P1-luxCDABE E. coli so as to probe cell metabolic activity in these two situations. The approach contributes to unraveling the connections between the electrostatic hydrogel charge, the nutrient/metal bioavailabilities and the resulting Cd-triggered bioluminescence output. Biosensors are hosted in hydrogels with thickness varying between 0 mm (the free-floating cell situation) and 1.6 mm, and are exposed to total Cd concentrations from 0 to 400 nM. The partitioning of bioavailable metals at the hydrogel/solution interface following intertwined metal speciation, diffusion and Boltzmann electrostatic accumulation is addressed by stripping chronopotentiometry. In turn, we detail how the bioluminescence maxima generated by the Cd-responsive cells under all tested Cd concentration and hydrogel thickness conditions collapse remarkably well on a single plot featuring the dependence of bioluminescence on free Cd concentration at the individual cell level. Overall, the construction of this master curve integrates the contributions of key and often overlooked processes that govern the bioavailability properties of metals in 3D matrices. Accordingly, the work opens perspectives for quantitative and mechanistic monitoring of metals by biosensors in environmental systems like biofilms or sediments.

An electrodiffusive network model with multicompartmental neurons and synaptic connections.

Most computational models of neurons assume constant ion concentrations, disregarding the effects of changing ion concentrations on neuronal activity. Among the models that do incorporate ion concentration dynamics, simplifications are often made that sacrifice biophysical consistency, such as neglecting the effects of ionic diffusion on electrical potentials or the effects of electric drift on ion concentrations. A subset of models with ion concentration dynamics, often referred to as electrodiffusive models, account for ion concentration dynamics in a way that ensures a biophysical consistent relationship between ion concentrations, electric charge, and electrical potentials. These models include compartmental single-cell models, geometrically explicit models, and domain-type models, but none that model neuronal network dynamics. To address this gap, we present an electrodiffusive network model with multicompartmental neurons and synaptic connections, which we believe is the first compartmentalized network model to account for intra- and extracellular ion concentration dynamics in a biophysically consistent way. The model comprises an arbitrary number of "units," each divided into three domains representing a neuron, glia, and extracellular space. Each domain is further subdivided into a somatic and dendritic layer. Unlike conventional models which focus primarily on neuronal spiking patterns, our model predicts intra- and extracellular ion concentrations (Na+, K+, Cl-, and Ca2+), electrical potentials, and volume fractions. A unique feature of the model is that it captures ephaptic effects, both electric and ionic. In this paper, we show how this leads to interesting behavior in the network. First, we demonstrate how changing ion concentrations can affect the synaptic strengths. Then, we show how ionic ephaptic coupling can lead to spontaneous firing in neurons that do not receive any synaptic or external input. Lastly, we explore the effects of having glia in the network and demonstrate how a strongly coupled glial syncytium can prevent neuronal depolarization blocks.

Schwinger vs Coleman: Magnetic charge renormalization

Abstract The kinetic mixing of two U(1) gauge theories can result in a massless photon that has perturbative couplings to both electric and magnetic charges. This framework can be used to perturbatively calculate in a quantum field theory with both kinds of charge. Here we reexamine the running of the magnetic charge, where the calculations of Schwinger and Coleman sharply disagree. We calculate the running of both electric and magnetic couplings and show that the disagreement between Schwinger and Coleman is due to an incomplete summation of topological terms in the perturbation series. We present a momentum space prescription for calculating the loop corrections in which the topological terms can be systematically separated for resummation. Somewhat in the spirit of modern amplitude methods we avoid using a vector potential and use the field strength itself, thereby trading gauge redundancy for the geometric redundancy of Stokes surfaces. The resulting running of the couplings demonstrates that Dirac charge quantization is independent of renormalization scale, as Coleman predicted. As a simple application we also bound the parameter space of magnetically charged states through the experimental measurement of the running of electromagnetic coupling.

Electric-Field Control of the Local Thermal Conductivity in Charge Transfer Oxides.

Phonons, the collective excitations responsible for heat transport in crystalline insulating solids, lack electric charge or magnetic moment, which complicates their active control via external fields. This presents a significant challenge in designing thermal equivalents of basic electronic circuit elements, such as transistors or diodes. Achieving these goals requires precise and reversible modification of thermal conductivity in materials. In this work, the continuous tuning of local thermal conductivity in charge-transfer SrFeO3-x and La0.6Sr0.4CoO3-x oxides using a voltage-biased Atomic Force Microscopy (AFM)tip at room temperature is demonstrated. This method allows the creation of micron-sized domains with well-defined thermal conductivity, achieving reductions of up to 50%, measured by spatially resolved Frequency Domain Thermoreflectance (FDTR). By optimizing the oxide's chemical composition, the thermal states remain stable under normal atmospheric conditions but can be reverted to their original values through thermal annealing in air. A comparison between Mott-Hubbard and charge-transfer oxides reveals the critical role of redox-active lattice oxygen in ensuring full reversibility of the process. This approach marks a significant step toward fabricating oxide-based tunable microthermal resistances and other elements for thermal circuits.

Starting electro-motor loads in battery-less off-grid photovoltaics using a super-capacitor based charge-pump

Starting Electromotor (EM) loads with off-grid photovoltaics (PV) is always challenging. Because their starting current makes the PV voltage fall, leading to converter instability. A practical solution is using batteries as a backup, but the repeating inrush current lowers the battery lifespan. This paper proposes an electrical charge pump based on a super-capacitor-bank (SCB) to fix the stability issue in off-grid battery-less photovoltaics. It contains an SCB connected to the DC-link of the inverter via a bi-directional DC/DC converter. The SCB is first pre-charged from the PV. Then at the moment of starting an electromotor load (EM), it supplies the DC-link capacitor with a pulse current. The main challenge here is to control the charging/discharging current of the SCB. In the proposed method, the system model is first developed by circuit analysis. Then a pre-defined charging pattern is obtained to charge the SCB from the zero to nominal voltage with a controlled current with no transient inrush. When injecting the pulse current as a charge pump (SCB discharging mode), constant duty-cycle switching for a pre-defined period is applied to ensure the converter stability in boost mode. To get the best results, a time shift is implemented between the pulse current injection and the load start-up, as a pre-charging phase. Experimental results from a 100 w system prototype running a 70 w universal motor show that a 2 Amp/200 ms pulse current, starting 50 ms before the load start-up, keeps the DC-link voltage deviation under 10 %, and no current transient is observed.

Numerical study on characteristics of spiked electrode electrostatic precipitator

The characteristic of the spiked electrode electrostatic precipitator was numerically studied. The spiked electrode had significant effects on the distribution of electric field and charge density. The electric field and charge density were quite high in the area around the spiked electrode tips while decreased rapidly in other area far from the tips. Complicated electrohydrodynamic flow was observed and strong vortices were formed in the upstream and side areas of the spiked electrode. The gas velocity at the tip of the spiked electrode was as high as 28.1 m/s at the working potential of 45 kV. The working potential was more effective for the removal of large particle. The removal efficiency increased 42.4% for 10 µm particle and 15.2% for 0.25 µm particle as working potential increased from 22.5 kV to 45 kV. Low gas velocity was beneficial to the removal of all size particles. The removal efficiency increased by 10% to 15% for all size particles when the gas velocity decreased from 1.0 m/s to 0.6 m/s.

Unraveling the physiochemical characteristics and molecular insights of Zein protein through structural modeling and conformational dynamics: a synergistic approach between machine learning and molecular dynamics simulations

This research article presents a comprehensive investigation into the three-dimensional structure, physicochemical characteristics and conformational stability of the Zein protein. Machine learning (ML) based homology modeling approach, was employed to predict the 3D structure of Zein protein. Convolutional neural networks (CNNs) were utilized for refining the model, capturing complex spatial features and improving decoy refinement. The predicted 3D structure of Zein protein showed a high-confidence score, i.e. C-score of 0.96. Physiochemical characteristic was also analyzed to investigate its protonation and deprotonation behavior across a range of pH values. A comprehensive analysis of the titration curve and electrostatic charges was performed to uncover valuable molecular insights into the zein protein’s charge distribution, electrostatic interactions and potential conformational changes. Molecular dynamics (MD) simulations were performed to analyze the zein structural behavior under different pH values (2.0, 4.5, 6.8, 10.0 and 12.5), ionic strengths (0 mM, 25 mM, 50 mM, 75 mM, 100 mM) and temperatures (300K, 350K, 375K). Our results demonstrated the influence of these factors on zein protein’s stability and conformational dynamics. At extreme pH values of 2.0 and 12.5, the Zein protein exhibited increased structural deviations and potential unfolding, while intermediate pH values closer to the protein’s isoelectric point (pI) demonstrated more compact and stable conformations. Analysis of root mean square deviation, radius of gyration, solvent accessible surface area and Ramachandran plot provided clear understandings of the protein’s compactness and surface exposure, confirming the impact of pH, ionic strength and temperature on the protein’s conformation.

Prediction of electric vehicle charging demand using enhanced gated recurrent units with RKOA based graph convolutional network

Prediction of electric vehicle charging demand using enhanced gated recurrent units with RKOA based graph convolutional network

Competing mechanisms of charge-induced electric field distortion and charge-involved neutralization on surface discharge

Charge-induced surface discharge poses a critical risk to the operational reliability of high-voltage direct current gas-insulated equipment and pulsed power system. In this study, we investigate the effects of charge-induced electric field distortion and charge involvement during surface discharge by separately depositing charge spots on two dielectric layers. The results show that deposited positive charges inhibit positive streamer development, whereas negative charges facilitate it, primarily due to electric field distortion induced by deposited charges. Nevertheless, the involvement of deposited charges in streamer development predominantly exhibits a neutralizing effect, exerting an opposite influence on the streamers. This highlights a competitive relationship between deposited charge involvement and electric field distortion. Additionally, the neutralization of deposited charges with electron avalanches reduces the impact of charge-induced electric field distortion, thereby mitigating its effects on discharge.

Spatio‐temporal dynamic navigation for electric vehicle charging using deep reinforcement learning

AbstractThis paper considers the real‐time spatio‐temporal electric vehicle charging navigation problem in a dynamic environment by utilizing a shortest path‐based reinforcement learning approach. In a data sharing system including transportation network, an electric vehicle (EV) and EV charging stations (EVCSs), it is aimed to determine the most convenient EVCS and the optimal path for reducing the travel, charging and waiting costs. To estimate the waiting times at EVCSs, Gaussian process regression algorithm is integrated using a real‐time dataset comprising of state‐of‐charge and arrival‐departure times of EVs. The optimization problem is modelled as a Markov decision process with unknown transition probability to overcome the uncertainties arising from time‐varying variables. A recently proposed on‐policy actor–critic method, phasic policy gradient (PPG) which extends the proximal policy optimization algorithm with an auxiliary optimization phase to improve training by distilling features from the critic to the actor network, is used to make EVCS decisions on the network where EV travels through the optimal path from origin node to EVCS by considering dynamic traffic conditions, unit value of EV owner and time‐of‐use charging price. Three case studies are carried out for 24 nodes Sioux‐Falls benchmark network. It is shown that phasic policy gradient achieves an average of 9% better reward compared to proximal policy optimization and the total time decreases by 7–10% when EV owner cost is considered.

Efficient Hybrid Electric Vehicle Power Management: Dual Battery Energy Storage Empowered by Bidirectional DC–DC Converter

ABSTRACTThis work offers a fuel cell power system with the ability to distribute power to the load from the electrical source and charge an auxiliary battery utilizing regenerative power flows created by the load. The approach is established on a bidirectional closed‐loop DC converter. A bidirectional DC–DC converter is presented as a means of achieving extremely high voltage energy storage systems (ESSs) for a DC bus or supply of electricity in power applications. This paper presents a novel dual‐active‐bridge (DAB) bidirectional DC–DC converter power management system for hybrid electric vehicles (HEVs). The proposed system makes it possible to charge an additional battery with regenerative power flows and distributes power from the electrical source to the load efficiently. The two main stages of the DAB converter, which are the focus of this work, are an interleaved buck/boost converter on the battery and a three‐phase wye‐wye series resonating converter on the DC bus. Each switch's current stress is greatly reduced by this design, which lowers transmission losses and enhances thermal performance. The interleaved buck conversion on the battery allows for lesser current stress in each switch, resulting in lower transmission loss. The increasing complexity and power of automotive embedded electronic systems have made the use of more potent power electronic converters in automobiles necessary. In recent years, many dual volt (42 V/14 V) bidirectional inverter topologies for automotive systems have been presented. However, the majority of them are either inefficient or use a huge number of transistors and magnetic devices in both parallel and series arrangements. As a result, in this study, a bidirectional high‐efficiency inverter with fewer components is provided. The design, modes of operation, and performance metrics of the DAB converter are examined, emphasizing its ability to achieve zero‐voltage switching (ZVS) and zero current switching (ZCS) throughout its operating range. The suggested system seeks to maximize EV power management, guaranteeing high dependability and efficiency. To test all of the aforementioned qualities, an evaluation version was created, with an average efficiency of 97.5%. This research could have a substantial impact on the advancement of power electronic converters for automotive applications, leading to better EV power management, increased system reliability, and increased overall efficiency.

Enhancing distribution system performance by optimizing electric vehicle charging station integration in smart grids using the honey badger algorithm

The global surge in electric vehicle (EV) adoption has driven significant research into electric vehicle charging stations (EVCS) due to their environmentally friendly attributes, including low CO₂ emissions. However, integrating EVCS into existing distribution grids presents challenges such as power losses and voltage instability, especially with the increasing incorporation of renewable distributed generation (RDG) sources and battery energy storage systems (BESS). This study introduces a novel honey badger optimization algorithm (HBOA), designed to enhance solution convergence and optimize multi-objective criteria efficiently. HBOA strategically places EVCS while considering vehicle-to-grid (V2G) capabilities and user driving behaviors over a full 24-hour cycle, effectively addressing uncertainties and dynamic conditions. Simulations on modified IEEE 69-bus and Indian 28-bus radial distribution systems (RDS) demonstrate significant results: in the IEEE 69-bus system, power loss is reduced by 62.0%, the voltage stability index (VSI) increases from 0.7139 to 0.8311, and CO₂ emissions decrease by 66.0%. In the Indian 28-bus system, power loss decreases by 55.5%, with VSI improving from 0.7394 to 0.9964, leading to a 50.0% reduction in CO₂ emissions. The proposed smart microgrid (MG) structure incorporates interconnected MGs for residential, commercial, and industrial sectors, emphasizing the efficacy of RDGs in mitigating the impact of EVCS on RDS. The advantages of the HBOA method lie in its superior optimization capabilities, which enhance system performance, reduce operational costs, and promote sustainability, highlighting the proposed methodology’s potential for future integration of EVCS in distribution networks.

Design and DFT study of new nano buds from the combination of C60 fullerene and nanobowl

In this research, new nano buds of C60 fullerene compound and nanobowls were designed and their structure, electrical properties and optical properties were calculated. The absence of imaginary frequency and high cohesive energies is proof of stability and confirmation of the possibility of their formation. Calculation of the relative population showed that the E configuration is the dominant population. The calculation of electrical properties showed that combining the structures with each other improves the electrical properties of nanobuds. The highest electric charge transfer from nanobowl to C60 was observed in the configuration of C nanobuds. A high improvement in NLO properties was observed in all nanobud configurations. Calculating the contribution of the dispersion term in the energy of the nanobuds showed that compared to the parents, larger dispersion energy was obtained for the designed nanobuds (especially configuration E). It was shown that the energy of nano buds and their parents decreases in the presence of solvents. The decrease in energy as a function of increasing the dielectric constant of the solvents may be due to the increase in the dipole moments of the nanobud as a result of the electron transfer from the nanobowl to the C60 fullerene.

A Variant of the Growing Neural Gas Algorithm for the Design of an Electric Vehicle Charger Network

The Growing Neural Gas (GNG) algorithm constitutes an incremental neural network model based on the idea of a Self-Organizing Map (SOM), that is, unsupervised learning algorithms that reduce the dimensionality of datasets by locating similar samples close to each other. The design of an electric vehicle charging network is an essential aspect in the transition towards more sustainable and environmentally friendly mobility. The need to design and implement an efficient network that meets the needs of all users motivates us to propose the use of a model based on GNG-type neural networks for the design of the network in a specific geographical area. In this paper, a variant of this iterative neural network algorithm is used with the objective that, from an initial dataset of points in the plane, it calculates a new simplified dataset with the main characteristic that the final set of points maintains the geometric shape and topology of the original set. To demonstrate the capabilities of the algorithm, it is exemplified in a real case, in which the design of an electric vehicle charging network is proposed. This network is built by applying the algorithm, taking as the original set of points the ones formed by the nodes of the gas station network in the geographical area studied. Several tests of running the algorithm for different sizes of the final dataset are performed, showing the differences between the original network and the computationally generated one.

Electrical and work function-based chemical gas sensors utilizing NC3 and graphene combination

Research has been conducted on the potential practical uses of heterostructures made of graphene and carbon nitride (NC3) following their successful synthesis. The remarkable gas sensing properties of these 2D nanosheets have captured significant interest, attributed to distinctive electronic characteristics and exceptional surface-to-volume ratio that are resulted from combination of NC3 and graphene. In this study, we present a detailed analysis of electronic and structural features of pristine NC3 and graphene (PG), and their in-plane heterostructures using first-principles density functional theory. Our investigation utilizes the B3LYP and dispersion-corrected van der Waals (vdW) functional WB97XD, along with 6-311G (d, p) basis set. Our findings indicate that the nanosheets we anticipated exhibit robust structural stability, characterized by a desirable cohesive energy. Furthermore, we observed a gradual increase in the bandgap as the concentration of N–C in the nanosheets increases. Additionally, we investigated the adsorption characteristics of these heterostructures towards toxic gas molecules such as SO2 and CO. Among the studied heterostructures, GNC3I demonstrated higher adsorption energy (Eads), with values of approximately −0.283 and −0.491 eV when exposed to SO2 and carbon monoxide gas molecules respectively. Electronic characteristics, including LUMO and HOMO energy values, energy gap (Eg) between HOMO and LUMO, work function, Fermi level, and conductivity, underwent notable modifications upon SO2 gas adsorption over nanosheets, except for PG. However, these parameters remained relatively unchanged following carbon monoxide adsorption. Natural bond orbital (NBO) and Mulliken charge analysis demonstrates that there is a transfer of charge from gas molecules to nanosheets. Although nanosheets exhibit slightly higher adsorption energy (Eads) values for CO gas compared to SO2 gas, various assessments, including molecular electrostatic potential (MEP) mapping, electronic properties, and charge transfer (CT) analysis, suggest that these nanosheets are superior sensors for detecting SO2 gas rather than carbon monoxide gas molecules.

Controllability of optical, electrical, and magnetic properties of synthesized Cd-doped MgFe2O4 nanostructure

This study explores the synthesis of cadmium-magnesium co-doped magnesium ferrite (Mg1+xCdxFe2–2xO4, 0.00 ≤ x ≤ 0.30, Δx = 0.05) via a citric acid-assisted sol-gel auto-combustion method. X-ray diffraction (XRD) analysis revealed crystal structure changes due to Cd and Mg co-doping, with the 0.25 Cd sample showing the smallest crystallite size and highest lattice strain. High-resolution transmission electron microscopy (HRTEM) confirmed the nanostructure and largest surface area for this sample. Diffuse reflectance spectra, analyzed using the Kubelka-Munk relation, indicated that the 0.25 Cd sample had the lowest energy gap (2.08 eV), supported by band position calculations. Electrical conductivity and charge transport mechanisms were examined at various temperatures. Magnetization studies showed reduced saturation magnetization at x = 0.25, ascribed to cation distribution changes and spin canting, with coercivity and anisotropy decreasing with higher Cd content. Overall, the 0.25 Cd sample exhibited optimized optical, electrical, and magnetic properties due to its distinct microstructure.

Optimal expansion planning of a self-healing distribution system considering resiliency investment alternatives

This paper proposes a three-stage optimization framework for the expansion planning of a self-healing distribution system that determines the optimal characteristics of distributed generation, energy storage systems, electric vehicle charging stations, and sectionalizing switches for the planning horizon. The main contribution of this model is that the proposed model considers the resilient investment alternatives in the expansion planning exercise to reduce the system's vulnerability against external shocks. The mobile energy storage system commitment in contingent conditions is another contribution of this paper. In the first stage, the optimal location, capacity, and time of installation of the electricity facilities are calculated. Then, the optimal allocation of sectionalizing switches is performed in the second stage. The third stage consists of three levels. In the first and second levels, the optimal normal and contingent operational scheduling are determined, respectively. The system is sectionalized into multi-microgrid systems in contingent conditions. Finally, the resilient investment alternatives for the designed system are evaluated. The proposed model utilizes a self-healing index and resilient expansion planning index to assess the impacts of resilient investment alternatives on the operational scheduling conditions. The proposed model was evaluated using the IEEE 123-bus system. The proposed method reduced the estimated average value of the worst-case energy not supplied by 50.52 % for the 5th year of the planning horizon concerning the no-resiliency investment case. Further, the proposed resilience investment method increased the self-healing index by about 9.32 % concerning the no-resiliency investment case.

Preliminary study of electrohydrodynamic motion (EHD) and electrostatic charging tendency (ECT) of transformer esterbased nanofluids

Preliminary study of electrohydrodynamic motion (EHD) and electrostatic charging tendency (ECT) of transformer esterbased nanofluids