Hasil untuk "Electric apparatus and materials. Electric circuits. Electric networks"

Öykü Simsek, Philip Zimmer, Simon Muench et al.

In this study, we developed gel polymer electrolytes (GPEs) containing cyclic carbonate side chains produced via UV-induced free radical polymerization, a fast and cost-efficient synthesis route, for Li-organic batteries. Cyclic carbonate methacrylate (CCMA) was copolymerized with diethylene glycol methyl ether methacrylate (DEGMEM) for 1 h. Then the resultant polymer films were swelled in 1 M LiPF6 in EC/DMC (50/50, v/v) with an electrolyte uptake of 500 %. These novel GPEs with an ionic conductivity of 1.1 mS cm−1 at 20 °C were electrochemically tested in Li//PTMA cells in comparison with LP30. They were found to show maximum discharge capacities (62.6 vs. 63.9 mAh g−1, GPE vs. LP30) at 0.1 C in addition to better compatibility with Li anodes (25.7 vs. 40.2 mV overpotential in Li stripping/plating tests) and a comparable electrochemical stability window. The results confirm that these GPEs are promising candidates for Li-organic batteries.

Carsten Dubs, Oleksii Surzhenko

Abstract Magnetically full or partially compensated insulating ferrimagnets with perpendicular magnetic anisotropy (PMA) offer valuable insights into fundamental spin‐wave physics and high‐speed magnonic applications. This study reports on key magnetic parameters of nanometer‐thin Ga substituted yttrium iron garnet (Ga:YIG) films with saturation magnetization 4πMs below 200 G. Vibrating sample magnetometry (VSM) is used to determine the remanent magnetization 4πMr and the polar orientation of the magnetic easy axis in samples with very low net magnetic moments. Additionally, the temperature dependence of the net magnetization of magnetically compensated Ga:YIG films, with compensation points Tcomp near room temperature, is investigated. For films with remanent magnetization values below 60 G at room temperature, the compensation points Tcomp are determined and correlated with their Curie temperatures TC. Ferromagnetic resonance (FMR) measurements at 6.5 GHz show that the FMR linewidths ΔHFWHM correlate inversely proportional with the remanent magnetization. The reduced saturation magnetization in the Ga:YIG films leads to a significant increase in the effective magnetization 4πMeff and thus enables films with robust PMA. This opens up a new parameter space for the fine‐tuning of potential magnonic spin‐wave devices on commonly used GGG substrates.

Yaqing Yang, Zhen Zhang, Liwen Zhang et al.

The electrical control of pure spin current remains a central challenge in spintronics, particularly in time-reversal symmetric systems composed of nonmagnetic elements, where spin and electric fields interact only indirectly. In this work, we develop a theoretical framework for electrically tuning pure spin photocurrent in two-dimensional materials with time-reversal symmetry via a gate electric field. Through theoretical analysis, we demonstrate that in systems with spin-orbit coupling and in-plane mirror symmetry, an out-of-plane electric field induces spin splitting and reversal in the band structure near the Fermi energy, enabling magnitude control and direction reversal of the pure spin photocurrent. To validate this mechanism, we perform first-principles calculations on germanene, an experimentally realized two-dimensional material. Beyond amplitude modulation, we reveal that reversing the direction of the applied electric field leads to a corresponding reversal of the pure spin photocurrent. Furthermore, we show that the pure spin photocurrent can be tuned by varying the photon energy and the incident angle of light, providing additional degrees of control over spin transport. These findings establish a robust strategy for electric-field-controlled pure spin transport in two-dimensional materials, offering new possibilities for the development of optospintronic devices.

Pufan Qi

This paper examines the impact of electric vehicles on total annual electricity consumption across 58 counties in California from 2010 to 2021. Employing a log linear model to analyze the relationship between electricity consumption and EV ownership, alongside a linear log model with an instrumental variable approach, the study finds that annual per capita electricity consumption increased by 0.23% for each additional electric vehicle per 10,000 residents over the 12 years period. The analysis identifies partisanship, measured as the annual percentage of voter registration for the Democratic Party by county, as a robust instrumental variable. Specifically, a 1% increase in Democratic voter registration corresponds to the adoption of approximately two additional EVs per 10,000 residents.

Wen Lu, P. Ajay

Presently, PV system is considered the best renewable energy source for electricity production. In order to maximize energy output in photovoltaic systems, a system for tracking the sun's position and adjusting panel positions was created. Despite the fact that several models for tracking solar radiation have been suggested to improve energy production, it faces challenges in continuous tracking and power consumption. This paper proposes a novel sensor-based solar tracking system with numerical optimization to increase photovoltaic systems' energy output. The initial model was for a two-axis tracking system based on sensors. Solar panel and sun positions are detected by this system using ultraviolet and microelectromechanical sun sensors. To improve tracking movements and photovoltaic energy production, we recommend using solar sensors to construct a novel two-axis solar tracking device. This technology benefits from increased solar radiation and solar energy harvesting capabilities. The main disadvantage of dual-axis active solar tracking systems is that the drive mechanism frequently uses up the output power of the solar panels. As a result, the net power gain of the solar panel is less than its maximum. Further, a control system was developed with proportional integral derivative (PID) controller and arithmetic optimization (AO) to adjust the panel position relative to suns movement. The AO technique tunes the PID controller gains until it reaches its desired level to enhance the production of power. With the use of sensors, the tracking system can use mathematical optimization algorithms and the sun's position to minimize power consumption while still maintaining optimal solar panel positioning. The developed model was designed in the MATLAB software and the outcomes are analyzed. The paper also presents an energy analysis of the system, which evaluates the energy input, output and overall system efficiency. We recommend using solar sensors to create a unique dual-axis solar tracking system. This technology benefits from increased solar radiation and solar energy harvesting capabilities.

Jecel Mattos de Assumpção, Oswaldo Hideo Ando, Hugo Puertas de Araújo et al.

This work introduces a novel custom-designed 16-bit RISC-V processor, intended for educational purposes and for use in low-resource equipment. The implementation, despite providing registers of 16 bits, is based on RV32E RISC-V ISA, but with some key differences like a reduced instruction set that is optimized for embedded systems, the removal of floating-point instructions, reduced register count, and modified data types. These changes enable the processor to operate efficiently in resource-constrained environments while still maintaining assembly-level compatibility with the standard RISC-V architecture. The educational aspects of this project are also a key focus. By working on this project, students can gain hands-on experience with digital logic design, Verilog programming, and computer architecture. The project also includes tools and scripts to help students transform assembly code into binary format, making it easier for them to test and verify their designs. Additionally, the project’s open-source nature allows for collaboration and the sharing of knowledge among students and researchers worldwide.

Hyeran Cho, Seung Yeol You, Gyu‐Tae Kim

Abstract Rhenium disulfide (ReS2) is a type of transition metal dichalcogenides (TMDs) that has potential electronic and photoelectrical applications. However, limited research has been conducted on improving its electrical properties and understanding the effect of doping on ReS2‐based devices. In this study, the enhanced electrical and photoelectrical performance of a 2D/0D heterostructure constructed by decorating the In2O3 quantum dots (QDs) on a multilayer ReS2 field‐effect transistor (FET) is reported. The In2O3 QDs are characterized by using a transmission electron microscope, optical absorption, and photoluminescence spectroscopy. The n‐doping effects with improved mobility are clearly observed, which is attributed to the electron transfer induced by the relatively high conduction band level of In2O3 QDs. Owing to the channel migration of ReS2 and traps at the ReS2/In2O3 QDs interface, additional performance improvements are observed, including reduced contact resistance, improved subthreshold swing, and increased photoresponsivity; however, the photoresponse speed is decreased. In summary, the findings suggest a novel mixed‐dimensional heterostructure for enhancing the performance of ReS2 transistors and provide insights into doping‐induced channel migration for 2D materials.

Li Jingning

With the popularity of English learning and the development of mobile technology, the development of intelligent assisted learning system has become an effective learning method. The purpose of this study is to improve the speech recognition performance of English education intelligent learning assistance system by using mobile sensor networks, and improve the learning effect of learners. This paper uses a speech recognition algorithm based on mobile sensor network to acquire learners' speech signals with multiple sensor nodes. By preprocessing and feature extraction of acquired signals, machine learning algorithm is applied to recognize them, thus improving the recognition accuracy. The experimental results show that compared with traditional speech recognition methods, the speech recognition system based on mobile sensor network has better performance in the case of background noise and speaker change. When learners use the system to learn English, they can get more accurate and reliable speech recognition results, improve the learning effect and interactive experience, and provide a more effective learning tool for English learners.

Victor H. Arzate-Palma, David G. Rivera-Orozco, Gerardo Molina Salgado et al.

A general overview of Noise-Shaping Successive Approximation Register (SAR) analog-to-digital converters is provided, encompassing the fundamentals, operational principles, and key architectures of Noise-Shaping SAR (NS SAR). Key challenges, including inherent errors in processing circuits, are examined, along with current advancements in architecture design. Various issues, such as loop filter optimization, implementation methods, and DAC network element mismatches, are explored, along with considerations for voltage converter performance. The design of dynamic comparators is examined, highlighting their critical role in the SAR ADC architecture. Various architectures of dynamic comparators are extensively explored, including optimization techniques, performance considerations, and emerging trends. Finally, emerging trends and future challenges in the field are discussed.

Peter Medle Rupnik, Luka Cmok, Nerea Sebastian et al.

Direct viscous mechano-electric response is demonstrated for a room-temperature ferroelectric nematic liquid, which combines large spontaneous electric polarization with 3D fluidity. The mechano-electric transduction is observed in the frequency range 1-200 Hz via a simple demonstrator device. The liquid is placed into a deformable container with electrodes and the electric current induced by both periodic and irregular actuation of the container is examined. The experiments reveal a rich interplay of several distinct viscous mechano-electric phenomena, where both shape deformations and material flow cause changes in the electric polarization structure of a ferroelectric nematic liquid. The results show that the mechano-electric features of the material promise a considerable applicative perspective spanning from sensitive tactile sensors to energy harvesting devices.

Jasmine Gnana Malar A, Tina Elizabeth Mathew, Kumar S. N et al.

The new notion of Agri (from ‘Agre’, latin for Land) – Culture (latin for Cultivation) is what made the transition of human race from primitive hunter-gatherers to more civilized and ordered societies. The invention of agriculture can be regarded as a key point in the timeline and dawn of modern civilization as we know it today. With the advent of digital electronics, we are now capable of carefully device systems to make any processes more optimized and generate significantly higher output, this is also true for the agriculture sector and many works are carried out recently aimed towards this objective and even created a new domain of precision agriculture. In this research work, an IoT-based system was developed that enables the farmers to monitor various micro-climatic parameters and assess the irrigation water requirement. The soil moisture and temperature were sensed with the aid of sensors and were fed to the LoRA system, in the receiver side, data is analyzed for the estimation of evapotranspiration. The global evapotranspiration was estimated using Cropwat software. The sensor data were analyzed using Mcguinnes-Bordne formulation and the outcome of this research work paves the way towards the estimation of the evapotranspiration in the microclimate environment.

M. Binari, A.C. Lokhande, F. AlMarzooqi et al.

Li-ion capacitors (LICs) have emerged as promising energy storage devices within the electronic industry. The performance of LICs is predominantly influenced by the electrode material utilized, making the proper selection and development of said material of utmost importance. This study focuses on fabricating a composite electrode material using a simple, cost-effective, and environmentally friendly technique, combining Manganese dioxide (MnO2) nanotube and graphene oxide (GO). The low cost, high natural abundance, and high theoretical specific capacity (1230 mAh/g) of MnO2 enables it to be effectively used in energy storage systems. The resulting material showcases a distinctive architecture where MnO2 nanotube nanorods are enveloped by GO nanosheets. By employing a binder-free buckypaper approach, the MnO2 nanotube/GO composite anode exhibits exceptional electrochemical performance, including high energy (213.29 Wh/kg) and power density (28.5 kW/kg), improved rate capability, and excellent cyclic stability. These findings undoubtedly indicate a promising future for the MnO2 nanotube/GO composite anode in lithium-ion-based energy storage systems.

Peter H. Siegel

Our fall issue of <sc>IEEE Journal of Microwaves</sc> opens with great news: we are now being indexed on Clarivate&#x0027;s Web of Science&#x0021; We will also have our papers appearing on Elsevier&#x0027;s Scopus database as early as November. In July, we were joined by a new assistant editor, Jackie Steele who, in addition to the normal editorial functions, is helping to promote our authors and our journal. Jackie has already set up a JMW forum on LinkedIn&#x2122; and started a monthly newsletter. In this issue you will find 16 new research papers covering topics as diverse as optimal power beaming transmitter arrays to a new ultra-wideband cryogenic isolator, plus the more usual components, filters, radar, and communications articles. We also share the third iteration of our continuing series of articles from Topic Editor Allison Marsh, on <italic>Women in Microwaves</italic>. Our subject is University of Minnesota Professor Rhonda Franklin. Our usage numbers now top 420,000 and our latest IEEEXplore citation count sits at 8.79 per article published. Also in this issue &#x2013; our full list of 2023 reviewers. Based on feedback from our July special series article <italic>Making Waves: Microwaves in Climate Change,</italic> we have started planning for a full special issue on this topic to be released in late 2024. A Call for Papers can be found at the end of this issue&#x0027;s table of contents.

Menaka N, Jasmine Samraj

Massive entities with significant state denaturation exist in the physical world, and users urgently want real-time and intelligent entity information acquisition. Now a days hand held devices and Electronic Device usage are increased rapidly. For effective cloud service edge systems are used. As rapid growth of internet and hardware technologies many edge providers are in the market. For Cloud Service Providers (CSP) it will be a chaos to choose optimised and reliable edge system for their usage. A hybrid convolutional neural network with Long Short-Term Memory (HCNN-LSTM) based Edge System Recommendation (ESR) Algorithm for Cloud Service Providers. An entity identification technique suitable for the edge is suggested to enhance the precision and responsiveness of entity state data search. It conducts accurate entity identification using the HCNN-LSTM while considering entity feature information. Even for new CSP optimised Edge System will be easily allocated. For this Recommendation Algorithm a total of 3 Servers and 25 Edge Systems with varied configuration are tested. 1000 GB hybrid data is used for transaction. The dataset collected from Kaggle online web-based repository. This proposed algorithm proves 95% more effective than existing techniques.

Daniel Miek, Fynn Kamrath, Kennet Braasch et al.

In this paper, the design and realization of quasi-elliptical waveguide filters with reduced manufacturing complexity are discussed. The filters are based on TE mode cavities, which are loaded with TM mode stubs. It is shown that dual-, triple- and quadruple-resonance segments are obtained by using up to three stubs loaded on the broad side of a TE mode cavity. The structures obtained can either be used as a stand-alone filter or even as a building block suitable for the realization of higher order filters. The multi-resonance blocks reveal several advantages in the mm-wave area: The manufacturing complexity is easy to handle and comparable to simple all-pole filters, which is especially important at high frequencies. Therefore, three prototypes are manufactured as proof of concept in the D-band (110 GHz&#x2013;170 GHz). Moreover, the building blocks are able to produce <inline-formula><tex-math notation="LaTeX">$n-1$</tex-math></inline-formula> transmission zeros (TZs) with <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> being the number of resonances. Therefore, the blocks generate between one (dual-resonance) and up to three (quadruple-resonance) TZs. Advantageously, the filters can be cut in the E-plane in order to reduce the insertion loss and hence consist of only two components. Three examples are manufactured by high precision CNC milling and reveal good agreement to the simulation by obtaining unloaded Q-factors of up to 1000.

Muhammad Zubair Khan, Oleg E. Peil, Apoorva Sharma et al.

In the rapidly expanding field of two-dimensional materials, magnetic monolayers show great promise for the future applications in nanoelectronics, data storage, and sensing. The research in intrinsically magnetic two-dimensional materials mainly focuses on synthetic iodide and telluride based compounds, which inherently suffer from the lack of ambient stability. So far, naturally occurring layered magnetic materials have been vastly overlooked. These minerals offer a unique opportunity to explore air-stable complex layered systems with high concentration of local moment bearing ions. We demonstrate magnetic ordering in iron-rich two-dimensional phyllosilicates, focusing on mineral species of minnesotaite, annite, and biotite. These are naturally occurring van der Waals magnetic materials which integrate local moment baring ions of iron via magnesium/aluminium substitution in their octahedral sites. Due to self-inherent capping by silicate/aluminate tetrahedral groups, ultra-thin layers are air-stable. Chemical characterization, quantitative elemental analysis, and iron oxidation states were determined via Raman spectroscopy, wavelength disperse X-ray spectroscopy, X-ray absorption spectroscopy, and X-ray photoelectron spectroscopy. Superconducting quantum interference device magnetometry measurements were performed to examine the magnetic ordering. These layered materials exhibit paramagnetic or superparamagnetic characteristics at room temperature. At low temperature ferrimagnetic or antiferromagnetic ordering occurs, with the critical ordering temperature of 38.7 K for minnesotaite, 36.1 K for annite, and 4.9 K for biotite. In-field magnetic force microscopy on iron bearing phyllosilicates confirmed the paramagnetic response at room temperature, present down to monolayers.

Albert Fert, Ramamoorthy Ramesh, Vincent Garcia et al.

While early magnetic memory designs relied on magnetization switching by locally generated magnetic fields, key insights in condensed matter physics later suggested the possibility to do it electrically. In the 1990s, Slonczewzki and Berger formulated the concept of current-induced spin torques in magnetic multilayers through which a spin-polarized current may switch the magnetization of a ferromagnet. This discovery drove the development of spin-transfer-torque magnetic random-access memories (STT-MRAMs). More recent research unveiled spin-orbit-torques (SOTs) and will lead to a new generation of devices including SOT-MRAMs. Parallel to these advances, multiferroics and their magnetoelectric coupling experienced a renaissance, leading to novel device concepts for information and communication technology such as the MESO transistor. The story of the electrical control of magnetization is that of a dance between fundamental research (in spintronics, condensed matter physics, and materials science) and technology (MRAMs, MESO, microwave emitters, spin-diodes, skyrmion-based devices, components for neuromorphics, etc). This pas de deux led to major breakthroughs over the last decades (pure spin currents, magnetic skyrmions, spin-charge interconversion, etc). As a result, this field has propelled MRAMs into consumer electronics products but also fueled discoveries in adjacent research areas such as ferroelectrics or magnonics. Here, we cover recent advances in the control of magnetism by electric fields and by current-induced torques. We first review fundamental concepts in these two directions, then discuss their combination, and finally present various families of devices harnessing the electrical control of magnetic properties for various application fields. We conclude by giving perspectives in terms of both emerging fundamental physics concepts and new directions in materials science.

S. Bahrebar, R. Ambat

Development in electronics causes an increase in the user demand for reliable and robust performance in various climatic conditions, while miniaturization is occurring at printed circuit board assemblies (PCBAs). The small sized electric components consequently have smaller pitch distances that play an important role in their failure. The soldering process also introduces contamination during the manufacturing process, which together with applied voltage, and critical extrinsic factors such as humidity and temperature influence the failures of PCBAs. The pitch distance and contamination remained from the soldering process, climatic conditions containing humidity and temperature, and PCBA design and material properties, leading to water film formation and consequently surface insulation resistance (SIR) reduction on PCBA and making electrochemical failure mechanism. Predicting PCBA failure is an important topic especially for building reliable electronic devices when used in harsh environments. Generally, machine learning (ML) use for analytical purposes like predicting the outcome based on training input datasets without using explicit models. Compared to other methods, ML provides more profound insights with; remarkable accuracy and ease of interpretation, managing the big data with good speed, mapping the nonlinear relationships, performing well with messy data (outliers and missing values), and visualizing multiple and complex interactions. In this study, we have used five common ML algorithms included; k-nearest neighbours (k-NN), decision tree (DT), random forest (RF), support vector machines (SVM), and deep neural network (DNN) from the most common category of ML, which is called supervised learning group for both regression and classification analysis under various conditions combined of critical factors including; humidity, temperature, pitch distance, voltage, contamination type and levels on (printed circuit board assembly) PCBA failure. The regression analysis is utilized to predict leakage current, which can give us an insight into the initial situation before creating a failure state and maybe can avoid that. Moreover, the classification analysis is employed to predict the failure states, which can give us a good understanding of the critical conditions to increase climatic reliability and reduce the risk of electronics.