Search Results (2,906)

For the measurement of electrical conductivity of metal materials, the traditional contact measurement method has a limited test range and requires periodic electronic calibration. In order to overcome the above shortcomings, this paper takes the inductive response of an RLC circuit driven by alternating sources as the research object and proposes a non-contact method for conductivity measurement of non-ferromagnetic metals engaged by a single-layer solenoid sensor. The effect of the circuit parameters on the inductive sensor characteristics has been described with different resonant modes, and the electric conductivities of different metals can be theoretically calculated based on eddy current. Moreover, the Comsol Multiphysics software is used to conduct finite element analysis to compare the experimental results and the simulation, which is consistent with the theoretical analysis. The measured accuracy of the inductive sensor is verified to be higher than 91% in parallel resonance, which exhibits higher stability and precision than that of series mode. The implementation of this project will provide the theoretical basis and data reference for the detection of electromagnetic properties of unknown metals and has a wide range of applications in non-destructive testing, engineering construction detection, and other fields. Full article

To meet the vibration isolation requirements of engines under diverse operating conditions, this paper proposes a novel magnetorheological fluid-elastomer isolator with high load and tunable parameters. The mechanical and magnetic circuit structures of the isolator were designed and optimized through theoretical calculations and finite element simulations, achieving effective vibration isolation within confined spaces. The dynamic performance of the isolator was experimentally evaluated using a hydraulic testing system under varying excitation amplitudes, frequencies, initial positions, and magnetic fields. Experimental results indicate that the isolator achieves a static stiffness of 3 × 10⁶ N/m and a maximum adjustable compression load range of 105.4%. In light of the asymmetric nonlinear dynamic behavior of the isolator, an improved nine-parameter Bouc–Wen model is proposed. Parameter identification performed via a genetic algorithm demonstrates a model accuracy of 95.0%, with a minimum error reduction of 28.8% compared to the conventional Bouc–Wen model. Full article

This manuscript is intended for readers who have a general interest in the subject of quantum computation and provides an overview of the most significant developments in the field. It begins by introducing foundational concepts from quantum mechanics—such as superposition, entanglement, and the no-cloning theorem—that underpin quantum computation. The primary computational models are discussed, including gate-based (circuit) quantum computing, adiabatic quantum computing, measurement-based quantum computing and the quantum Turing machine. A selection of significant quantum algorithms are reviewed, notably Grover’s search algorithm, Shor’s factoring algorithm, and Quantum Singular Value Transformation (QSVT), which enables efficient solutions to linear algebra problems on quantum devices. To assess practical performance, we compare quantum and classical implementations of support vector machines (SVMs) using several synthetic datasets. These experiments offer insight into the capabilities and limitations of near-term quantum classifiers relative to classical counterparts. Finally, we review leading quantum programming platforms—including Qiskit, PennyLane, and Cirq—and discuss their roles in bridging theoretical models with real-world quantum hardware. The paper aims to provide a concise yet comprehensive guide for those looking to understand both the theoretical foundations and applied aspects of quantum computing. Full article

This research introduces a novel de-anonymization technique targeting the Tor network, addressing limitations in prior attack models, particularly concerning router positioning following the introduction of bridge relays. Our method exploits two specific, inherent protocol-level vulnerabilities: the absence of a continuity check for circuit-level cells and anomalous residual values in RELAY_EARLY cell counters, working by manipulating cell headers to embed a covert signal. This signal is composed of reserved fields, start and end delimiters, and a payload that encodes target identifiers. Using this signal, malicious routers can effectively mark data flows for later identification. These routers employ a finite state machine (FSM) to adaptively switch between signal injection and detection. Experimental evaluations, conducted within a controlled environment using attacker-controlled onion routers, demonstrated that the embedded signals are undetectable by standard Tor routers, cause no noticeable performance degradation, and allow reliable correlation of Tor users with public services and deanonymization of hidden service IP addresses. This work reveals a fundamental design trade-off in Tor: the decision to conceal circuit length inadvertently exposes cell transmission characteristics. This creates a bidirectional vector for stealthy, protocol-level de-anonymization attacks, even though Tor payloads remain encrypted. Full article

Lithium plating (LP), as a specific degradation mechanism in lithium-ion batteries (LIBs), has been thoroughly investigated regarding formation conditions and potential safety hazards, but it is yet unknown how this effect influences the mechanical properties of batteries in the case of mechanical deformation. To address this issue, pouch cells used in EVs were artificially aged (AA) to a state of health of 80–82% in conditions that predominantly cause the formation of LP. These cells were subjected to a mechanical abuse load, and safety-relevant parameters, such as tolerated deformation level, failure force, and the process of thermal runaway (TR), were analyzed and compared with respective fresh (F) and aged cells of the same type. Complementary microscopy analyses were carried out to compare the found changed mechanical response with the different layer morphology caused by LP. The tests did exhibit a significantly different mechanical response of cells in the three states but also clearly altered short-circuiting behavior. The tolerated peak force at discharge state dropped by −28% and at charge state by −37% compared to fresh cells, while the deformation at failure slightly increased by +6% for the AA cells. A clear reduction in stiffness (−16%) of the LP cells was attributed to the formed layer, identified as mossy LP. The significantly stronger voltage drop at failure, seen for the LP cells, was associated with severe exothermal reactions of LP in contact with air and moisture during TR. This study revealed the strong influence of LP on the mechanical properties of LIBs. However, the transferability of the findings to other cell chemistries or formats is unclear, emphasizing the need for further investigations in this research field. Full article

The growing deployment of photovoltaic (PV) power plants has made reliable fault detection and classification a critical challenge for ensuring operational efficiency, safety, and economic viability. Faults on the direct current (DC) side, especially during the commissioning phase, can significantly affect power output and maintenance costs. This paper proposes a fault classification methodology for the direct current (DC) side of PV power plants, using the MATLAB/Simulink 2023b simulation environment for system modeling and dataset generation. The method accounts for different environmental and operational conditions—including irradiance and temperature variations—to enhance fault identification robustness. The main electrical faults—such as open circuit (OC), short circuit (SC), connector faults, and partial shading—are analyzed based on features extracted from current–voltage (I–V) and power–voltage (P–V) curves. The proposed classification system achieved 100% accuracy by applying the One-Versus-One (OVO) and One-Versus-Rest (OVR) techniques, using a dataset with 704 samples for one string and 2480 samples for three strings. The lowest accuracies were observed with the OVO technique: 99.03% for 1024 samples with one string, and 97.35% for 880 samples with three strings. The study also highlights the performance of multiclass machine learning techniques across different dataset sizes. The results reinforce the relevance of using machine learning integrated into the commissioning phase of PV systems, with the potential to improve reliability, reduce losses, and optimize the operational costs of solar plants. Future work should explore the application of this method to real-world data, as well as its deployment in the field to support companies and professionals in the sector. Full article

With the rapid advances of the electronics industry, a large amount of acidic etching waste solutions (AEWS) for etching Printed Circuit Board (PCB) are generated, which require complete remediation and sustainable recycling to avoid environmental pollution and wasting of resources. Herein, the novel purification technology for the acidic copper-containing etching waste solution was exploited via integrated alkali gradient pH control (3.0, 3.2, and 3.5). At pH 3.0, the system demonstrated selective metal removal with 94.02% efficiency for Fe and 82.60% for Mn. Elevating the pH to 3.2 enabled effective elimination of Zn (59.32%), Cr (59.46%), and Al (33.24%), while maintaining minimal copper loss (8.16%). Further pH adjustment to 3.5 achieved enhanced removal efficiencies of 97.86% (Fe), 91.30% (Mn), 59.38% (Zn), 62.10% (Cr), 21.66% (Ca), 34.05% (Al), and 26.66% (Co), with copper retention remaining high at 70.83% (29.17% loss). Furthermore, using the purified AEWS (pH 3.2) as precursor, high-purity nano-CuO was successfully synthesized through a Cu₂(OH)₃Cl conversion-calcination process, exhibiting 99.20% CuO purity with 0.0012% chlorine content and <0.1% metallic impurities. The development and application of the purification technology for AEWS containing copper, along with the production methodology for high-purity CuO, were significant to the fields of electronic information industry, environmental engineering, green industry and sustainable development of the ecological environment. Full article

In recent years, the flexoelectric effect has demonstrated significant potential for applications in sensing, actuation, energy acquisition and other related fields. As the primary structure of flexural output, the flexoelectric beam structure also exhibits substantial potential for development and application. However, flexoelectric output is unable to function effectively at the macroscale, and the impact of the uncertainty of the parameters of flexoelectric material on the flexural output remains unclear. To address the issue of parameter uncertainty, this paper employs the analysis method based on variance-driven coupled with moment-free measure to study the impact caused by structural parameters on the uncertainty of the output voltage of the flexural electron beam in the case of an open circuit, the influence on the output charge uncertainty under short-circuit conditions, and the influence on the effective piezoelectric coefficient uncertainty. This study of parameter uncertainty offers a valuable reference for the reliability assessment and structural optimization design of flexural electric beam and provides theoretical support for the macroscale application of the flexoelectric effect. Full article

The aromaticity of small boron clusters remains under scrutiny due to persistent inconsistencies between magnetic and electronic descriptors. Here, we reexamine B₃⁻, B₃⁺, B₄, B₄²⁺, and B₄²⁻ using a multidimensional approach that integrates Adaptive Natural Density Partitioning, Electron Density of Delocalized Bonds, magnetically induced current density, and the z-component of the induced magnetic field. We introduce a model in which σ-aromaticity arises from two distinct delocalization topologies: a radial 2e⁻ σ-pathway and a tangential multicenter circuit formed by alternating filled and vacant sp² orbitals. This framework accounts for the evolution of aromaticity upon oxidation or reduction, preserving coherence between electronic structure and magnetic response. B₃⁻ features cooperative radial and tangential σ-delocalization, together with a delocalized 2e⁻ π-bond, yielding robust double aromaticity. B₃⁺ retains σ- and π-aromaticity, but only via a tangential 6e⁻ σ-framework, leading to a more compact delocalization and slightly attenuated ring currents. In B₄, the presence of a radial 2e⁻ σ-bond and a 4c–2e π-bond confers partial aromatic character, while the tangential 8e⁻ σ-framework satisfies the 4n rule and induces a paratropic current. In contrast, B₄²⁺ lacks the radial σ-component but retains a tangential 8e⁻ σ-circuit and a 2e⁻ 4c–2e π-bond, leading to a σ-antiaromatic and π-aromatic configuration. Finally, B₄²⁻, exhibits delocalized π- and σ-circuits, yielding consistent diatropic ring currents, which confirms its fully doubly aromatic nature. Altogether, this analysis underscores the importance of resolving σ-framework topology and demonstrates that, when radial and tangential contributions are correctly distinguished, Hückel’s rule remains a powerful tool for interpreting aromaticity in small boron rings. Full article

This paper presents a physics-guided machine learning (PGML) approach to model the I–V characteristics of GaN current aperture vertical field effect transistors (CAVET). By adopting the method of transfer learning and the shortcut structure, a physically guided neural network model is established. The shallow neural network with $tanh$ as the basis function is combined with a hypernetwork that dynamically generates its weight parameters. The influence of transconductance is added to the loss function. This model can synchronously predict the output and transfer characteristics of the device. Under the condition of small samples, the prediction error is controlled within 5%, and the $R^2$ value reaches above 0.99. The proposed PGML approach outperforms conventional approaches, ensuring physically meaningful and robust predictions for device optimization and circuit-level simulations. Full article

Given the decline in fossil energy reserves and the need for less pollution, achieving carbon zero is challenging in major industrial sectors. However, the emergence of large-scale hydrogen production systems powered by renewable energy sources offers an achievable option for carbon neutrality in specific applications. When combined with energy storage systems, static power converters are crucial in these production systems. This paper offers a comprehensive review of various power converter topologies, focusing on AC– and DC–bus architectures that interface battery storage units, electrolyzers, and fuel cells. The evaluation of DC/AC, AC/DC, and DC/DC converter topologies, considering cost, energy efficiency, control complexity, power level suitability, and power quality, represents a significant advancement in the field. Furthermore, the subsequent exploration of battery aging behavioral modeling, characterization methods, and real-time parameter estimation of the battery’s equivalent electrical circuit model enhances our understanding of these systems. Large-scale hydrogen production systems most often use an AC–bus architecture. However, DC–bus configuration offers advantages over AC–bus architecture, including high efficiency, simpler energy management, and lower system costs. In addition, MVDC or HVDC DC/DC converters, including isolated and non-isolated designs based on multiple cascaded DABs and MMC-type topologies, have also been studied to adapt the DC–bus to loads. Finally, this work summarizes several battery energy storage projects in the European Union, specifically supporting the large-scale integration of renewable energy sources. It also provides recommendations, discussion results, and future research perspectives from this study. Full article

This study proposes a rigid-flexible neural optrode integrated with anti-bending SU-8 optical waveguides and locally soft peptide-functionalized microelectrodes to address the challenges of precise implantation and long-term biocompatibility in traditional neural interfaces. Fabricated via microelectromechanical systems (MEMS) technology, the optrode features a PBK/PPS/(PHE)₂ trilayer electrochemical modification that suppresses photoelectrochemical (PEC) noise by 63% and enhances charge storage capacity by 51 times. A polyethylene glycol (PEG)-enabled temporary rigid layer ensures precise implantation while allowing post-implantation restoration of flexibility and enabling positioning adjustment. In vitro tests demonstrate efficient light transmission through SU-8 waveguides in agar gel and a 63% reduction in PEC noise peaks. Biocompatibility analysis reveals that peptide-coated PI substrates improve cell viability by 32.5–37.1% compared to rigid silicon controls. In vivo validation in crucian carp midbrain successfully records local field potential (LFP) signals (60–80 μV), thereby confirming the optrode’s sensitivity and stability. This design provides a low-damage and high-resolution tool for neural circuit analysis. It also lays a technical foundation for future applications in monitoring neuronal activity and researching neurodegenerative diseases with high spatiotemporal resolution. Full article

This paper aims to present an overview of the state-of-the-art materials and technologies that can be used to create electronic circuits on 3D plastic carriers also known as 3D electronics. Strategies for print-based and laser-based 3D electronics will be discussed as well as the techniques to apply the circuit carrier and the way interconnection technology can be used to combine electronic components on top of the circuit carrier. A basic explanation of the functional principles, materials, and applications is given for different substrate and interconnection technologies. The aim is to make it easier to compare different technologies and its required materials to make the right decisions on what technology is best suited for the job. For this purpose, comparison tables for 3D plastic circuit carrier technologies and substrate materials considering their temperature stability were created. It can be concluded that there are a lot of influencing factors that determine which technologies are best suited for application. The most important factors are the 3D complexity and the field of application, the required structure size of the circuit, and the required production quantity. Full article

This work establishes the first derivation of geometry-dependent Kirchhoff’s laws via conformal symmetry, enabling new types of self-sustaining circuits unattainable in classical lumped-element theory. Building on Bessel-Hagen’s extension of Noether’s theorem to Maxwell’s equations, we develop a conformal circuit formalism that fundamentally extends traditional circuit theory through two key innovations: (1) Geometry-dependent weighting factors ($w_i\propto a_i^{-1}$) in Kirchhoff’s laws derived from scaling symmetry; (2) A dilaton-like field ($\delta$) mediating energy exchange between circuits and conformal backgrounds. Unlike prior symmetry applications in electromagnetism, our approach directly maps the 15-parameter conformal group to component-level circuit transformations, predicting experimentally verifiable phenomena: (i) $10.2\%$ deviations from classical current division in RF splitters; (ii) $4.2\%$ resonant frequency shifts with $2.67\times$ Q-factor enhancement; (iii) Power-law scaling ($J_z\propto a^{-2}$) in cylindrical conductors. This theoretical framework proposes how conformal symmetry could enable novel circuit behaviors, including potential self-sustaining oscillations, subject to experimental validation. Full article

Electromagnetic implosions of hollow lithium cylinders can be utilized to compress magnetized plasma targets in the context of Magnetized Target Fusion (MTF). Two small-scale experiments were conducted at General Fusion as a stepping stone toward compressing magnetized plasmas on a larger scale. The first experiment is an electromagnetic implosion of a lithium ring, and the second is a compression of toroidal magnetic flux by imploding a hollow lithium cylinder onto an hourglass-shaped central structure. Here we present the methodology and results of modelling these experiments with OpenFOAM. Our in-house axisymmetric compressible MHD multi-phase solver was further extended to incorporate: (i) external RLC circuit model for electromagnetic compression coils and (ii) diffusion of the magnetic field into multiple solid materials. The implementation of the external RLC circuit model for electromagnetic coils was verified by comparison with results obtained with FEMM software and with the analytical solution. The solver was then applied to model both experiments and the main conclusions are as follows: (i) modelling solid lithium as a high-viscosity liquid is an adequate approach for the problems considered; (ii) the magnetic diffusivity of lithium is an important parameter for the accurate prediction of implosion trajectories (for the implosion of the lithium ring, higher values of magnetic diffusivity in the range $0.2 \le {\eta }_{\mathrm{ring}}[{\mathrm{m}}^2/\mathrm{s}] \le 0.5$ resulted in a better fit to the experimental data with a relative deviation in the trajectory of $\lesssim 20\%$); (iii) simulation results agree well with experimental data, and in particular, the toroidal field amplification of 2.25 observed in the experiment is reproduced in simulations within a relative error margin of 20%. The solver is proven to be robust and has the potential to be employed in a variety of applications. Full article