• New numerical model developed for electrical potential in electrolyte solutions. • The new numerical model can resolve multiple length- and time-scales. • It numerically switches between the Poisson and the electroneutrality equations. • Multiple systems were studied, including a multi-ion multi-valent system. • The new numerical model can produce accurate results for varying systems. The transport of ions is governed by a species conservation equation and the Nernst-Planck flux expression. The latter requires information on the electrical potential, for which an additional transport equation is required. Traditional numerical approaches, such as solving the Poisson equation or applying the electroneutrality condition, face limitations in their applicability. In this work, a new numerical model is introduced for the electrical potential that effectively functions as a numerical switch between the Poisson equation and the electroneutrality condition. This model is tested for three different scenarios: a small-scale system where charge separation is expected in a large part of the domain, a large-scale system where charge separation is significantly less important, and a multi-ion liquid junction system. This new numerical model is capable of producing accurate results for all the tested systems.

A unique scandium oxide-doped cobalt oxide nanosheet array for coupling hydrogen evolution with catalytic conversion of polyethylene terephthalate into formate.

Directed ion transport in liquid electrolyte solutions underlies many phenomena in natural and industrial settings. While nature has evolved structures that drive continuous ion flow without Faradaic redox reactions, establishing this process in synthetic systems has been challenging. Here we report an ion pump that drives aqueous ions against a force using a capacitive ratchet mechanism independent of redox reactions. Modulation of an electric potential between thin metallic layers on either face of a nanoporous alumina wafer immersed in solution results in persistent voltages and ionic currents. This occurs due to the nonlinear capacitive nature of electric double layers, whose repeated charging and discharging sustains a continuous ion flux. Using this approach, we demonstrate ratchet-driven electrodialysis that reaches a 50% decrease in the conductivity of the solution in a dilution cell. These ratchet-based ion pumps can enable continuous desalination and selective ion separation using an electrically powered device with no moving parts.

Abstract Temperature variations between multiple units of a lithium-ion battery affect electrochemical processes during its operation. Resolving each unit’s behavior with a physics-based model is computationally prohibitive, and conventional single-unit approximations fail to capture the battery performance, especially at high charge/discharge rates and/or low temperatures. Our surrogate model preserves multi-unit fidelity, while drastically reducing computational cost, and enables efficient real-time predictions of a battery's state of charge under dynamic conditions. Comprising two convolutional neural networks and one multilayer perceptron, it emulates a standard pseudo-two-dimensional electrochemical model of an individual unit to sequentially advance state variables (lithium concentrations and electric potentials within active material and electrolyte) within each unit. Local temperature and electric current serve as inputs to the surrogate model of each unit, and inter-unit currents are computed via particle swarm optimization. Heat generation predicted by the surrogate is coupled with a one-dimensional thermal model to capture electrochemical–thermal interactions. Our simulations of commercial batteries NMC 21700 and 4680 show that the single-unit approximation overestimates effective discharge capacity, whereas the multi-unit model accurately captures the associated energy loss.

ABSTRACT Apart from remaining life and state-of-age assessments, reliable detection of deeply buried cracks in reformer tubes, important for petrochemical industries, is still challenging. In this work, a theoretical investigation was conducted to determine the electromagnetic response signals from a parallel, axially displaced (offset) rectangular coil positioned outside a two-layer tube, simulating an eddy current inspection of a reformer tube. The second-order vector potential (SOVP) formalism was adopted for this purpose, in which the SOVP is expressed using transverse electric (TE) and transverse magnetic (TM) potentials. The modal coefficients are inherently coupled, which leads to complex analytical expressions, especially for multi-layer tubes. To circumvent the complexities, we numerically treated the boundary conditions at each interface. This method enabled the discrete estimation of modal coefficients, which were then used to accurately predict the magnetic field signature and induced eddy currents. The results were validated against a finite element method (FEM) model. This work provides the necessary theoretical framework for a subsequent investigation into the response signals generated by circumferential cracks in the reformer tubes.

Voltage-sensitive fluorescence imaging is widely used to image action potential waves in the heart. However, although the electrical waves trigger mechanical contraction, imaging needs to be performed with pharmacologically contraction-inhibited hearts, limiting studies of the coupling between cardiac electrophysiology and tissue mechanics. Here, we introduce a multiple-camera optical mapping system with which we image action potential waves at high resolutions across the entire ventricular surface of the beating and strongly deforming heart. We imaged intact isolated rabbit hearts inside a soccer-ball shaped imaging chamber, facilitating even illumination and panoramic imaging. Using 12high-speed cameras, ratiometric voltage-sensitive imaging, and three-dimensional (3D) multiview motion tracking, we reconstructed the entire 3D deforming ventricular surface and performed corresponding voltage-sensitive measurements during sinus rhythm, paced rhythm and ventricular fibrillation (VF). Our imaging setup defines a new state-of-the-art in the field and can be used to study the heart's electromechanical physiology during health and disease at unprecedented resolutions. For example, we measured action potential duration and contractile changes in response to pharmacological blockage of potassium ion channels during sinus rhythm, measured electrical activation times and observed mechanical strain waves following electrical activation fronts during pacing, and observed electromechanical vortices during VF. KEY POINTS: The heartbeat is controlled by electrical impulse phenomena that trigger contractile motion. Optical mapping uses fluorescent dyes to measure electrical impulse phenomena in cardiac muscle tissue. It is an important tool for studying cardiac electrophysiology and rhythm abnormalities. With current optical mapping techniques, it is not possible to image beating hearts. Hearts need to be contraction-inhibited using pharmacological agents. This limits optical mapping studies to measurements of electrical activation patterns and action potentials. Tissue strain and contractile motion cannot be assessed simultaneously. A novel 3D optical mapping system is presented that enables panoramic imaging of action potential waves across the surface of strongly contracting isolated hearts. With this system, it is possible to measure electrical activation and action potential waves simultaneously with deformation and strain.

This study develops a fuzzy-based modelling framework to analyze the uncertain dynamic behaviour of cantilever piezoelectric beams by introducing fuzziness directly into the initial conditions, an aspect not addressed in existing literature. Two widely used fuzzy representations – Triangular Fuzzy Numbers (TFN) and Gaussian Fuzzy Numbers (GFN) – are employed to characterize imprecision in the excitation parameters. The Fuzzy Adomian Decomposition Method (FADM) is applied to derive semi-analytical solutions for the transverse deflection and induced electric potential, followed by an r -cut based uncertainty propagation procedure. Numerical investigations using PZT–5A material reveal that the choice of fuzzy number significantly influences the resulting uncertainty bounds. For mid-span deflection at t = 2 s, the GFN representation produces uncertainty widths approximately 1.8–2.0 times larger than TFN at r = 0.5, and around 1.4–1.6 times larger at r = 0.9. TFN yield compact intervals with lower computational cost, whereas GFN provide smoother and more conservative uncertainty envelopes. Additionally, the uncertainty width monotonically decreases with increasing r and converges to the deterministic response as r → 1. The findings demonstrate that the type of fuzzy number plays a crucial role in the fidelity and robustness of uncertainty modelling in piezoelectric structures. The proposed framework offers a computationally efficient tool for the dynamic analysis, design, and reliability assessment of intelligent piezoelectric devices operating under imprecise or partially known initial conditions.

ABSTRACT During hot dry rock exploitation, fracturing in high-temperature reservoirs may activate nearby faults and induce shear-dominated failures. This study investigates the influence of thermal damage on shear behaviour and geophysical responses of granite, using simultaneous acoustic emission (AE) and electric potential (EP) monitoring under compression-shear loading. Power-law tails of AE energy, inter-event time, and EP amplitude were characterised by complementary cumulative distribution functions (CCDF), and their temporal evolution was tracked with a sliding time window. Normalised Benioff-type exponents in time-to-failure coordinates were employed to assess critical acceleration prior to failure. Results show that thermal treatment intensifies early microcracking, increases AE spatial dispersion, and amplifies EP fluctuations in the plastic stage. The exponents of AE and EP increase with temperature, indicating a transition from concentrated to more dispersed energy release. Throughout the critical process, the Benioff strain for both AE and EP generally exhibits convex upward acceleration, while the Benioff-type exponent decreases with temperature, reflecting weakened acceleration. Thermal damage causes moisture escape, weakened mineral interfaces, and microcrack propagation, resulting in exponent drift in AE and EP signals. These findings enhance understanding of shear instability in thermally damaged granite and support AE–EP-based early warning in deep high-temperature reservoirs.

Abstract This paper investigates Calderón's problem on a conformally transversally anisotropic manifold (M, g) of dimension n ≥ 3, where the conductivity a(s, x, p) might depend on both the electric potential and the electric field. We establish that for all (t, x) ∈ R × M and β ∈ N 1+n the derivatives ∂ β (s,p) a(s, x, p)| (s,p)=(t,0) are uniquely determined by the boundary voltage-current measurements. If a(s, x, p) is analytic in p, then a(s, x, p) can be uniquely recovered.

Abstract Detecting the space charges within micro-regions (less than several μm) at metal/insulation interface induced by high-frequency voltages is challenging for the design and stability of power electronic devices. This study fabricated a novel embedded metal-epoxy insulation micro configuration using a mask magnetron sputtering method to simulate the interface structure under square-wave electric field. An improved Kelvin Probe Force Microscope (KPFM) with probe vibration mode optimization and lateral pressure electrode was employed to detect the interface charges in the interface microregion. A high electric potential occurs at the insulation side of the interface after high-frequency square-wave field excitation. A transition zone of potential distribution is found at the interface within 5 μm, which contributes to the electric field distortion at the interface. Interestingly, significant charge accumulations occur at the interface region less than 10 μm. It decreases initially and then increases with the voltage frequency. It is indicated that the competition of charge injection and the charge recombination at the interface cause the reduction of charges and electric field less than 5 kHz. The study is useful for guiding the design and application of insulation in high power density electronic devices.

Electroencephalograms (EEGs) are time-series records of the electrical potential from collective neural activity in the brain. EEG waveform patterns-rhythmic and irregular oscillations and transient patterns of sharp waves or spikes-are potential phenotypical biomarkers, reflecting genotype-specific neural activity. This is especially relevant to diagnosing epilepsy without direct seizure observations, which is common in clinical settings, as well as in animal models, which often have subtle neurological phenotypes without overt epilepsy. Herein, we investigate genotypic prediction from long-term EEG signals of freely behaving mice belonging to six groups defined by the presence or absence of a neurological disease-genotype (TSC1 gene knockout) in three different inbred strains with distinct genetic backgrounds. We propose a machine learning approach to predict the genotypes of individual mice from the occurrence counts of waveforms that approximate short windows of the EEG. That is, a dictionary of waveforms is optimized to approximate windows from each genotype, and the vectors of waveform occurrence counts are the features for predicting genotypes via logistic regression models. Across two-fold cross-validation of the waveform dictionary learning, and leave-one-individual-out genotype prediction, we find that waveform counts pooled over multiple hour segments enable reliable prediction of mouse strain with an accuracy of 70% (95% CI 62-78) compared to chance rate of 38%. For two of the three strains, DBA2 and C57B6, strain-specific classifiers reliably determined the epilepsy-genotype (TSC1 gene knockout) with accuracies of 86% (95% CI 70-101) and 67% (95% 55-79), respectively. None of the mice of these strains had evidence of overt seizures or EEG-based seizure detection. In comparison, a state-of-the-art time-series classification approach (Hydra) enables higher strain classification at 98%, comparable TSC1-genotype prediction for the two strains (86% and 71% respectively), but the method is not interpretable. The methodologies and results show the potential of EEG waveforms as interpretable phenotypes and bag-of-waves as a feature representation for identifying epilepsy genotypes.

Abstract We obtain semiclassical resolvent estimates for the Schrödinger operator in , , where is a semiclassical parameter, and are real‐valued electric and magnetic potentials independent of . If , , satisfy , , , , for , we prove that the norm of the weighted resolvent is bounded by , . We get better resolvent bounds for electric potentials which are Hölder with respect to the radial variable and magnetic potentials which are Hölder with respect to the space variable. For long‐range electric potentials which are Lipschitz with respect to the radial variable and long‐range magnetic potentials which are Lipschitz with respect to the space variable we obtain a resolvent bound of the form , .

In the context of rapid electric vehicles (EVs) growth and the development of vehicle-to-grid (V2G) technology, accurately assessing their dispatchable potential and implementing cost-effective charging and discharging optimization are key to leveraging the advantages of EVs as distributed energy storage. To address this, an assessment method of electric vehicle schedulable potential and optimal dispatch strategy considering V2G is proposed. First, based on the nonhomogeneous Poisson process assumption and Sequential Monte Carlo method, EV queuing and charging scenarios are simulated. Through analyzing the feasible regions of EV dispatchable capacity under both charging and V2G scenarios, the dispatchable power capacity of EV aggregators is calculated. Secondly, based on the analysis of the management framework for EVs participating in power dispatch and considering the interaction between electric vehicles and the power grid, an optimal dispatch model for EV clusters has been established. By incorporating multiple optimization objectives such as aggregator profits, charging economy, and peak load reduction, this model facilitates the orderly scheduling of EVs while ensuring secure and economical grid operation. Finally, a practical case study is used to verify the proposed model and algorithm.

This study investigates the transient dynamic response and thermomechanical stability of a transversely isotropic piezoelectric medium featuring a spherical cavity. To accurately model the small-scale effects inherent in advanced structural components, a spatiotemporal nonlocal elasticity framework of the Klein-Gordon type is employed, incorporating both internal length and intrinsic time scale parameters. The governing equations of the Moore-Gibson-Thompson (MGT) thermoelastic model are reformulated using a nonsingular Rabotnov-type fractional exponential kernel, providing a robust mathematical formulation to capture memorydependent interactions without the paradox of infinite propagation speeds. The structural boundary of the cavity is subjected to a ramp-type thermal loading and electrical grounding, simulating realistic operational conditions for sensors and actuators. Using the Laplace transform technique and the Zakian numerical inversion method, the transient distributions of temperature, displacement, stress, and electric potential are derived. The results highlight the significant influence of the Rabotnov fractional parameter and spatiotemporal nonlocality on the structural stability and wave-front characteristics. This research provides a unique medium for understanding the latest developments in fractional-order dynamics for piezoelectric micro- and nano-structures, offering practical insights for civil, aerospace, and mechanical engineering applications.

The city of Balikpapan, East Kalimantan, Indonesia, is a growing city that is the support base for the national capital. This leads to an increasing population and economy but also brings waste management challenges. Therefore, it is of great concern to the government to prepare early in order to minimize the negative effects of waste. The purpose of this research is to calculate methane gas production from waste and its conversion into electrical energy using the sanitary landfill concept. Where landfill gas production is after the anaerobic process takes place. This research employed a quantitative, descriptive approach and used the Landfill Gas Emissions Model (LandGEM) v3.02 for data analysis. The calculation results show that in 2019, the methane gas potential at TPA Manggar was 499,350 m³/year, and the potential is projected to continue increasing. The implementation of a Waste-to-Energy Power Plant (PLTSa) at TPA Manggar is crucial, as the conversion of methane gas yields significant electrical power and energy. This conversion not only addresses waste management issues but also supports the local energy grid with renewable power, providing environmental and economic benefits. Based on the experiment, the estimated increase in waste in 2027 is 149,962.49 tons, with a MAPE of 9.64%, or an average annual increase of 1.86%. Meanwhile, the estimated methane gas potential in 2027 is 3,426,000 m³/year and will continue to increase. Furthermore, the estimated electric power potential in 2027 is 1.19 MW, or equivalent to 10,409,010.24 kWh/year. The results of the study provide recommendations that the conversion of energy to electrical energy at the Waste-to-Energy Plant (PLTSa) can be carried out by TPA Manggar, East Kalimantan, Indonesia. Where this aligns with the Government of the Republic of Indonesia's direction on waste management, implementing a sanitary landfill method that continues to innovate. For further research, it is recommended that the waste management unit conduct detailed planning for the construction of the PLTSa and the necessary equipment, thereby aligning Balikpapan's waste management practices with the government's mandate to employ innovative sanitary landfill methods.

This study proposes a novel theoretical framework to analyze the dynamic response of a radially polarized piezoelectric solid sphere under time-dependent thermal loading in the presence of an applied magnetic field. The model incorporates spatiotemporal nonlocal elasticity to account for long-range interatomic interactions and intrinsic relaxation mechanisms. Additionally, it incorporates dual-phase-lag heat conduction to account for finite thermal propagation speeds. The governing equations for displacement, temperature, stress, electric potential, and electric displacement are formulated under spherical symmetry and are solved exactly in the Laplace transform domain, followed by numerical inversion to obtain time-domain solutions. The results demonstrate that both spatial and temporal nonlocal parameters significantly attenuate mechanical and electrical responses while smoothing thermal gradients. This process effectively eliminates the unphysical singularities predicted by classical theories. Moreover, a comparative analysis across multiple thermoelastic models confirms that conventional local approaches tend to overestimate stresses and electrical outputs. The proposed model provides a physically consistent description of size- and rate-dependent behavior in piezoelectric nanospheres, directly relevant to applications in nanoscale sensors, actuators, energy harvesters, and biomedical microdevices.

This study examines buckling in a sandwich nanoplate. The top and bottom layers are made of piezoelectric Barium Titanate (BaTiO3) and Cobalt Ferrite (CoFe2O4), while the central core consists of a metallic TPMS-based auxetic lattice structure constructed from SUS304. The implementation of nonlocal strain gradient elasticity, in conjunction with sinusoidal higher-order deformation theories, was carried out. The equations regulating the motion of the nanosensor sandwich nanoplate were calculated using Hamilton’s principle, considering the magnetostrictive, electroelastic, and thermal properties of the piezoelectric surface plates. After that, Navier equations were solved. The sandwich nanoplate’s TPMS-based auxetic core’s geometric, nonlocal, temperature, and electric and magnetic potentials were studied. Dimensionless natural frequencies of the sandwich nanoplate were the study’s main focus. Topology, porosity, size effects, and external fields can alter auxetic TPMS-cored sandwich nanoplates’ buckling and vibration responses, according to this study. Increasing porosity (V = 0.10–0.75) lowers fundamental frequency by 52% (λ(1,1) ≈8.0→3.8) and raises critical buckling temperature (ΔT ≈ 2000→2110 K), with Type II cores having the strongest thermal resistance. Nonlocal effects reduce λ(1,1) by 22% and ΔT by 30 K, whereas strain-gradient effects increase it by 38%, indicating opposing nanoscale mechanisms. High-temperature nanosensors, transducers, and nanoelectromechanical systems will be developed and deployed.

Earthquakes are measured using well-defined seismic parameters such as seismic moment ([Formula: see text]), moment magnitude ([Formula: see text]), and released elastic energy (E). However, the mechanism by which this tremendous energy accumulates deep within the Earth's crust remains unclear and is one of the most fundamental open questions in seismological research. We investigate a quantitative link between earthquake radiated energy and the generalized Pourbaix electrochemical potential. This analysis forms the basis of a theoretical electrochemical framework for assessing whether electrical processes may contribute to earthquake nucleation. An intriguing similarity has been found between the released energy in an earthquake and Pourbaix potential in a redox reaction at an oxide-aqueous interface. A mathematical equivalence is established to strengthen this connection. This provides new insights into the possible electrochemical mechanism underlying seismic processes. Hydrated smectite, a clay mineral with a distinctive layered structure, is a dominant source of electrochemical potential generation in the Earth's crust. Observations of significant smectite abundance in various deep drilling projects indirectly support this assertion. The layered arrangement of these hydrated clay minerals enables the formation of multiple electrochemical cells, leading to substantial accumulation of electrochemical potential. This observation indicates the presence of electrical potential in the earthquake preparation zone, which may offer a more comprehensive explanation for earthquake lights, negative anomalies in atmospheric electric fields, ionospheric perturbations, and other associated phenomena observed before or during an earthquake.

In this research, the interaction between the anti-diabetic drug of (BNEJa) and armchair single-walled carbon nanotube (SWCNT) has been calculated with density functional theory (DFT) to ameliorate carbon nanotube drug carriers as the applied sensors in drug delivery systems. BNEJa shows NMR shielding between 10–600 ppm, with a sharp peak at 25 ppm and several weak peaks between 100–450 ppm. The hydrogens involved in the O–H of PO3 groups have remarked the high degeneracy in NMR chemical shielding tensors. The largest fluctuation in atomic charge has been observed for the oxygen atoms in the O–H of PO3 groups, as the electronegative atoms in the formation of potent chelation with the carbon nanotube using the drug delivery method, suggesting the modeling of (BNEJa)@SWCNT. Moreover, the electric potential (Ep) as the amount of work energy through transferring the electric charge from one site to another site in the presence of an electric field has been measured for blood pressure (BP) agent of (BNEJa) @ SWCNT complex using CAM–B3LYP/EPR–III, 6–311+G(d,p) level of theory. So, the electric potential of the NQR method for elements of N, P, O, and F, dealing with the interaction site between the BP agent of (BNEJa) and the surface of SWCNT in aqueous medium. The parameter values have shown good stability of the BP agent during Langmuir adsorption on the SWCNT sensor.

This study explores the vibrational behavior of a functionally graded (FG) sandwich microplate subjected to coupled thermo-fluid-structure interactions. The microplate consists of porous magneto-electro-elastic (FGPMEE) facings and a porous graphene nanoplatelet-reinforced (FGPGPLRC) core. The face layers transition from barium titanate (BaTiO 3 ) to cobalt iron oxide (CoFe 2 O 4 ) following a power-law distribution and incorporate four distinct porosity patterns. Meanwhile, the core features four porosity models and four graphene nanoplatelet (GPL) dispersion schemes. To account for size-dependent dynamics and the effect of transverse shear deformation, a modified strain gradient theory (MSGT) is combined with first-order shear deformation theory (FSDT). Thermal effects are modeled through a nonlinear temperature field across the thickness, while fluid interactions are captured using forces derived from the Navier–Stokes equations. The governing equations are formulated using Hamilton’s principle and solved analytically via the Navier technique. A comprehensive parametric study examines the impact of key factors—including temperature gradients, porosity coefficients, GPL distribution patterns, applied electric voltage, magnetic potential, and length scale parameters—on the natural frequencies of the microplate.