This paper presents a compact broadband circularly polarized (CP) filtering patch antenna with multi-element coupling structure. The antenna consists of two substrate layers. The upper substrate layer incorporates a main radiating patch with an etched rectangular slot, a pair of unequal-sized L-shaped parasitic stubs placed along the + 45° diagonal of the main patch, and surrounding microstrip patches. The lower substrate features a ground plane etched with an asymmetric U-shaped slot on its top surface and a microstrip feedline on the bottom. The signal enters the port, propagates along the microstrip feedline, and couples to the main radiating patch through the asymmetric U-shaped slot, generating CP radiation. Further coupling between the main patch and the surrounding microstrip patches produces two additional CP modes, enabling wideband CP operation. Without the need for additional filtering circuits, the antenna achieves bandpass filtering characteristics by introducing two controllable radiation nulls near 2.0 GHz and its second harmonic at 4.0 GHz through the unequal L-shaped parasitic stubs. A third radiation null near 3.6 GHz is generated by the rectangular slot etched on the main patch. The proposed design maintains a compact profile (<inline-formula> <tex-math notation="LaTeX">$0.54 \lambda_0 \times 0.54 \lambda_0 \times 0.07 \lambda_0$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$\lambda _{0}$ </tex-math></inline-formula> corresponds to the lowest operating frequency) while realizing integrated wideband CP and filtering performance. The measured results demonstrate an impedance bandwidth of 23.1% (2.64–3.33 GHz), an axial ratio (AR) bandwidth of 11% (2.8–3.125 GHz) and a stable in-band gain of approximately 7 dB. The out-of-band suppression levels reach 17 dB and 24 dB in the lower and upper stopbands, respectively, confirming good CP filtering performance.
Korawege N. C. Jayasena, Battur Batkhishig, Babak Nahid-Mobarakeh
et al.
Bearing faults are a critical concern in electrical machines, particularly permanent magnet synchronous motors (PMSMs), commonly used in electric vehicles. Early and accurate classification of bearing fault severity is essential for predictive maintenance, as it enhances cost-effectiveness, ensures safety, and extends product life. Although vibration-based monitoring offers rich diagnostic information, it remains costly and requires excess modifications. In contrast, current-based non-invasive techniques offer advantages in cost and integration but face challenges with accuracy due to operational complexities. This study presents six distinct artificial neural networks (ANNs)-based cascaded classification schemes for bearing fault severity classification. Discrete wavelet transform (DWT) with Symlet (Sym) is used for multi-resolution feature extraction in currents, combined with motor speed data to generate multi-channel features. These features are fed into an ANN-based level I algorithm using various fusion techniques, offering a more interpretable algorithmic framework. One approach employs a multi-input ANN for level I, integrated with an ANN-based level II for refined severity classification. This two-level cascaded approach achieves an accuracy over 99% on the Paderborn University dataset in various operational scenarios. The model is trained and analyzed using MATLAB. The proposed cascaded algorithms outperform single-stage models, and enhanced signal preprocessing improves accuracy and noise resilience. Additionally, the proposed risk-based performance indicator offers insights into maintenance strategies, while an optimum algorithm selection framework identifies an algorithm by considering a trade-off between computational complexity and accuracy.
Changing the topography of electrodes by ultrafast laser ablation has shown great potential in enhancing electrochemical performance in lithium-ion batteries. The generation of microstructured channels within the electrodes creates shorter pathways for lithium-ion diffusion and mitigates strain from volume expansion during electrochemical cycling. The topography modification enables faster charging, improved rate capability, and the potential to combine high-power and high-energy properties. In this study, we present a preliminary exploration of this approach for sodium-ion battery technology, focusing on the impact of laser-generated channels on hard carbon electrodes in sodium-metal half-cells. The performance was analyzed by employing different conditions, including different electrolytes, separators, and electrodes with varying compaction degrees. To identify key factors contributing to rate capability improvements, we conducted a comparative analysis of laser-structured and unstructured electrodes using methods including scanning electron microscopy, laser-induced breakdown spectroscopy, and electrochemical cycling. Despite being based on a limited sample size, the data reveal promising trends and serve as a basis for further optimization. Our findings suggest that laser structuring can enhance rate capability, particularly under conditions of limited electrolyte wetting or increased electrode density. This highlights the potential of laser structuring to optimize electrode design for next-generation sodium-ion batteries and other post-lithium technologies.
Production of electric energy or power. Powerplants. Central stations, Industrial electrochemistry
Abstract Machine learning (ML) offers considerable promise for the design of new molecules and materials. In real-world applications, the design problem is often domain-specific, and suffers from insufficient data, particularly labeled data, for ML training. In this study, we report a data-efficient, deep-learning framework for molecular discovery that integrates a coarse-grained functional-group representation with a self-attention mechanism to capture intricate chemical interactions. Our approach exploits group-contribution concepts to create a graph-based intermediate representation of molecules, serving as a low-dimensional embedding that substantially reduces the data demands typically required for training. Using a self-attention mechanism to learn the subtle but highly relevant chemical context of functional groups, the method proposed here consistently outperforms existing approaches for predictions of multiple thermophysical properties. In a case study focused on adhesive polymer monomers, we train on a limited dataset comprising only 6,000 unlabeled and 600 labeled monomers. The resulting chemistry prediction model achieves over 92% accuracy in forecasting properties directly from SMILES strings, exceeding the performance of current state-of-the-art techniques. Furthermore, the latent molecular embedding is invertible, enabling the design pipeline to automatically generate new monomers from the learned chemical subspace. We illustrate this functionality by targeting several properties, including high and low glass transition temperatures (Tg), and demonstrate that our model can identify new candidates with values that surpass those in the training set. The ease with which the proposed framework navigates both chemical diversity and data scarcity offers a promising route to accelerate and broaden the search for functional materials.
Materials of engineering and construction. Mechanics of materials, Computer software
Raagya Arora, Ariel R. Barr, Daniel T. Larson
et al.
Two-dimensional (2D) semiconductors are likely to dominate next-generation electronics due to their advantages in compactness and low power consumption. However, challenges such as high contact resistance and inefficient doping hinder their applicability. Here, we investigate workfunction-mediated charge transfer (modulation doping) as a pathway for achieving high-performance p-type 2D transistors. Focusing on type-III band alignment, we explore the doping capabilities of 27 candidate materials, including transition metal oxides, oxyhalides, and α-RuCl3, on channel materials such as transition metal dichalcogenides (TMDs) and group-III nitrides. Our extensive first-principles density functional theory (DFT) reveal p-type doping capabilities of high electron affinity materials, including α-RuCl3, MoO3, and V2O5. We predict significant reductions in contact resistance and enhanced channel mobility through efficient hole transfer without introducing detrimental defects. We analyze transistor geometries and identify promising material combinations beyond the current focus on WSe2 doping, suggesting new avenues for hBN, AlN, GaN, and MoS2. This comprehensive investigation provides a roadmap for developing high-performance p-type monolayer transistors toward the realization of 2D electronics.
Arka Bandyopadhyay, Nesta Benno Joseph, Awadhesh Narayan
This review presents recent breakthroughs in the realm of nonlinear Hall effects, emphasizing central theoretical foundations and recent experimental progress. We elucidate the quantum origin of the second-order Hall response, focusing on the Berry curvature dipole, which may arise in inversion symmetry broken systems. The theoretical framework also reveals the impact of disorder scattering effects on the nonlinear response. We further discuss the possibility of obtaining nonlinear Hall responses beyond the second order. We examine symmetry-based indicators essential for the manifestation of nonlinear Hall effects in time-reversal symmetric crystals, setting the stage for a detailed exploration of theoretical models and candidate materials predicted to exhibit sizable and tunable Berry curvature dipole. We summarize groundbreaking experimental reports on measuring both intrinsic and extrinsic nonlinear Hall effects across diverse material classes. Finally, we highlight some of the other intriguing nonlinear effects, including nonlinear planar Hall, nonlinear anomalous Hall, and nonlinear spin and valley Hall effects. We conclude with an outlook on pivotal open questions and challenges, marking the trajectory of this rapidly evolving field.
The increasing prominence of concepts such as Smart Production and Industrial Internet of Things (IIoT) within the context of Industry 4.0 has introduced a new set of requirements for the engineering of industrial systems, including support for dynamic environments, timeliness guarantees, support for heterogeneity, interoperability and reliability. These requirements are further exacerbated at the network level by the notable rise in the number and variety of devices involved. To stay competitive in this ever-changing industrial landscape while boosting productivity, it is vital to meet those requirements, combining established protocols with emerging technologies. Software-Defined Networking (SDN) is the forefront traffic management paradigm that offers flexibility for complex industrial networks, enabling efficient resource allocation and dynamic reconfiguration. Message Queuing Telemetry Transport (MQTT) is a low-overhead protocol of the application layer that is gaining popularity in the scope of the IoT and IIoT. However, its Quality-of-Service (QoS) policies do not support timeliness requirements. This article presents a framework that seamlessly integrates SDN and MQTT, enhancing network management flexibility while satisfying real-time requirements found in industrial environments. It leverages the User Properties of MQTTv5 to allow specifying real-time requirements. MQTT traffic is intercepted by a Network Manager that extracts real-time information and instructs an SDN controller to deploy corresponding network reservations. MQTT traffic across multiple edge networks is propagated by selected brokers using multicasting. Extensive experiments validate the proposed approach, demonstrating its superiority over MQTT and Direct Multicast-MQTT (DM-MQTT) DM-MQTT in latency reduction. A response time analysis, validated experimentally, emphasizes robust performance across metrics.
In contrast to cumbersome benchtop spectrometers, integrated on-chip spectrometers are well-suited for portable applications in health monitoring and environmental sensing. In this paper, we have developed an on-chip spectrometer with a programmable silicon photonic filter by simply using parallel cascaded micro-ring resonators (MRs). By altering the transmission spectrum of the filter, multiple and diverse sampling of the input spectrum is achieved. Then, combined with an artificial neural network (ANN) model, the incident spectrum is reconstructed from the sampled signals. Each MR is coupled to adjacent ones, and the phase shifts within each MR can be independently tuned. Through dynamic programming of the phases of these MRs, sampling functions featuring diverse characteristics are obtained based on a single programmable filter with an adjustable number of sampling channels. This eliminates the need for a filter array, significantly reducing the area of the on-chip reconstructive spectrometer. The simulation results demonstrate that the proposed design can achieve the reconstruction of continuous and sparse spectra within the wavelength range of 1450 nm to 1650 nm, with a tunable resolution ranging from 2 nm to 0.2 nm, depending on the number of sampling states employed. This benefit arises from the programmable nature of the device. The device holds tremendous potential for applications in wearable optical sensing, portable spectrometry, and other related scenarios.
. The widespread introduction of information technologies into all spheres of society, the crea-tion of a significant amount of confidential and critical data in digital form leads to an increase in the priority of information security tasks everywhere, including in the energy sector, which relates to the critical infrastructure of any state. The purpose of the work is to develop the men-tioned approach to ensure the possibility of increasing the efficiency of information security methods based on it. The goal was achieved through a detailed study of disturbances in the val-ues of formal parameters that uniquely determine the matrix that is assigned to the information security system under conditions of active attacks (disturbances) on the system. Singular num-bers and singular vectors of the matrix are considered as such parameters. The most important result of the work is the substantiation of the existence and establishment of interconnected re-gions of stabilization of disturbances of singular numbers and singular vectors of the system ma-trix, while the region of stabilization of singular numbers corresponds to the region of monoto-nous decrease in their disturbances with increasing numbers, while the stabilization of singular vectors corresponds to the region in which their disturbances are comparable with 90 degrees. It is shown that the stabilization process is determined by the mathematical properties of the pa-rameters under consideration. The significance of the obtained result lies in the possibility of using it to improve various information security systems that were built or studied using a gen-eral approach to analyzing their state, both theoretically and practically. The work provides ex-amples of such use.
Electrical engineering. Electronics. Nuclear engineering, Production of electric energy or power. Powerplants. Central stations
Two-level DC/AC inverter topologies are widely used for low voltage and high voltage applications in power systems and industrial areas. Space Vector Modulation (SVM) is a popular Pulse-Width Modulation technique used for controlling the inverters and providing the efficient energy conversion from DC sources. However, applications of SVM-based studies are limited in the Power Electronics Laboratory (PEL) due to the vital risks associated with high voltage applications, and it is not easily learned through mathematical analysis and visual learning without implementation by undergraduate students. A simulation and experimental setup of an SVM-controlled two-level, three-phase inverter was presented in this study for undergraduate students to learn its basics in the PEL. Several programs were used to simulate the inverter in the classroom environment and to design a power circuit and microcontroller-based printed circuit board of the inverter for PEL experiments. The two case studies were given. In the case results, the output voltage waveforms of simulation and experimental inverters were compared to show the validation of simulation results. With this study, the students’ experience is enhanced in electronic circuit design, programming, coordination with hardware and software development activities, self-learning, and teamwork. Additionally, practical applications increase undergraduate students’ interest in Power Electronics Courses and reinforce their knowledge from lecture and laboratory studies.
Two-dimensional (2D) materials have garnered significant attention in recent years due to their atomically thin structure and unique electronic and optoelectronic properties. To harness their full potential for applications in next-generation electronics and photonics, precise control over the dielectric environment surrounding the 2D material is critical. The lack of nucleation sites on 2D surfaces to form thin, uniform dielectric layers often leads to interfacial defects that degrade the device performance, posing a major roadblock in the realization of 2D-based devices. Here, we demonstrate a wafer-scale, low-temperature process (< 250 °C) using atomic layer deposition (ALD) for the synthesis of uniform, conformal amorphous boron nitride (aBN) thin films. ALD deposition temperatures between 125 and 250 °C result in stoichiometric films with high oxidative stability, yielding a dielectric strength of 8.2 MV/cm. Utilizing a seed-free ALD approach, we form uniform aBN dielectric layers on 2D surfaces and fabricate multiple quantum well structures of aBN/MoS2 and aBN-encapsulated double-gated monolayer (ML) MoS2 field-effect transistors to evaluate the impact of aBN dielectric environment on MoS2 optoelectronic and electronic properties. Our work in scalable aBN dielectric integration paves a way towards realizing the theoretical performance of 2D materials for next-generation electronics.
The emerging field of quantum sensors and electronics for fundamental physics is introduced, emphasising the role of thin-film superconducting devices. Although the next generation of ground-based and space-based experiments requires the development of advanced technology across the whole of the electromagnetic spectrum, this article focuses on ultra-low-noise techniques for radio to far-infrared wavelengths, where existing devices fall short of theoretical limits. Passive circuits, detectors and amplifiers are described from classical and quantum perspectives, and the sensitivities of detector-based and amplifier-based instruments discussed. Advances will be achieved through refinements in existing technology, but innovation is essential. The needed developments go beyond engineering and relate to theoretical studies that bring together concepts from quantum information theory, quantum field theory, classical circuit theory, and device physics. This article has been written to introduce graduate-level scientists to quantum sensor physics, rather than as a formal review.
Marco Turchetti, Yujia Yang, Mina R. Bionta
et al.
Recent advancements in nanofabrication have enabled the creation of vacuum electronic devices with nanoscale free space gaps. These nanoelectronic devices promise the benefits of cold-field emission and transport through free-space, such as high nonlinearity and relative insensitivity to temperature and ionizing radiation, all the while drastically reducing the footprint, increasing the operating bandwidth and reducing the power consumption of each device. Furthermore, planarized vacuum nanoelectronics could easily be integrated at scale similar to typical micro and nanoscale semiconductor electronics. However, the interplay between different electron emission mechanisms from these devices are not well understood, and inconsistencies with pure Fowler-Nordheim emission have been noted by others. In this work, we systematically study the current-voltage characteristics of planar vacuum nano-diodes having few-nanometer radii of curvature and free-space gaps between the emitter and collector. By investigating the current-voltage characteristics of nearly identical diodes fabricated from two different materials and under various environmental conditions, such as temperature and atmospheric pressure, we were able to clearly isolate three distinct emission regimes within a single device: Schottky, Fowler-Nordheim, and saturation. Our work will enable robust and accurate modeling of vacuum nanoelectronics which will be critical for future applications requiring high-speed and low-power electronics capable of operation in extreme conditions.
Gauthier Roussilhe, Thibault Pirson, Mathieu Xhonneux
et al.
Taiwan plans to rapidly increase its industrial production capacity of electronic components while concurrently setting policies for its ecological transition. Given that the island is responsible for the manufacturing of a significant part of worldwide electronics components, the sustainability of the Taiwanese electronics industry is therefore of critical interest. In this paper, we survey the environmental footprint of 16 Taiwanese electronic components manufacturers (ECM) using corporate sustainability responsibility reports (CSR). Based on data from 2015 to 2020, this study finds out that our sample of 16 manufacturers increased its greenhouse gases (GHG) emissions by 7.5\% per year, its final energy and electricity consumption by 8.8\% and 8.9\%, and the water usage by 6.1\%. We show that the volume of manufactured electronic components and the environmental footprints compiled in this study are strongly correlated, which suggests that relative efficiency gains are not sufficient to curb the environmental footprint at the national scale. Given the critical nature of electronics industry for Taiwan's geopolitics and economics, the observed increase of energy consumption and the slow renewable energy roll-out, these industrial activities could create a carbon lock-in, blocking the Taiwanese government from achieving its carbon reduction goals and its sustainability policies. Besides, the European Union, the USA or even China aim at developing an industrial ecosystem targeting sub-10nm CMOS technology nodes similar to Taiwan. This study thus provides important insights regarding the environmental implications associated with such a technology roadmap. All data and calculation models used in this study are provided as supplementary material.
Stefano Pecorario, Alberto D. Scaccabarozzi, Daniele Fazzi
et al.
Solution-processed, large-area, and flexible electronics largely relies on the excellent electronic properties of sp$^2$-hybridized carbon molecules, either in the form of $π$-conjugated small molecules and polymers or graphene and carbon nanotubes. Carbon with sp-hybridization, the foundation of the elusive allotrope carbyne, offers vast opportunities for functionalized molecules in the form of linear carbon atomic wires (CAWs), with intriguing and even superior predicted electronic properties. While CAWs represent a vibrant field of research, to date, they have only been applied sparingly to molecular devices. The recent observation of the field-effect in microcrystalline cumulenes suggests their potential applications in solution-processed thin-film transistors but concerns surrounding the stability and electronic performance have precluded developments in this direction. In the present study, ideal field-effect characteristics are demonstrated for solution-processed thin films of tetraphenyl[3]cumulene, the shortest semiconducting CAW. Films are deposited through a scalable, large-area, meniscus-coating technique, providing transistors with hole mobilities in excess of 0.1 cm$^2$ V$^{-1}$ s$^{-1}$, as well as promising operational stability under dark conditions. These results offer a solid foundation for the exploitation of a vast class of molecular semiconductors for organic electronics based on sp-hybridized carbon systems and create a previously unexplored paradigm.
Paul C. Lou, Anand Katailiha, Ravindra G. Bhardwaj
et al.
In metal/degenerately doped silicon bilayer structure, the interfacial flexoelectric effect due to strain gradient leads to charge carrier transfer from metal layer to the silicon layer. This excess charge carrier concentration is called flexoelectronic doping or flexoelectronic charge transfer, which gives rise to an electronically polarized (order of magnitude larger than ferroelectric materials) silicon layer. In the transport measurements, the charge carrier concentration in silicon is found to increase by two orders of magnitude due to flexoelectronic doping, which changes the Fermi level and the Hall response. The flexoelectronic charge accumulation modifies the electron-electron and the electron phonon coupling, which gives rise to Mott metal-insulator transition and magnetism of phonons, respectively. The coexistence of flexoelectronic polarization and magnetism gives rise to a new class of materials called electronic multiferroics. By controlling the flexoelectronic doping, material behavior can potentially be engineered for quantum, spintronics and electronics applications in semiconductor materials.
saeedeh zebhi, Seyed Mohammad Taghi AlModaressi, Vahid Abootalebi
In this paper, two different methods of human activity recognition based on video signals are introduced. The first method explores the effectiveness of combining feature descriptors obtained by local descriptors and artificial neural network classifier. It is used in the traditional approach and the local descriptors extract interest points or local patches from the videos, and the feature vectors are later constructed based on the intrests, and eventually feature vectors are used as the input of a two-layer feed-forward artificial neural network (ANN). Experimental results show that using the HOG3D descriptor with ANN gives the best performance. On the other hand, deep learning architectures have attracted much consideration for automatic feature extraction in the last years, so an improved 3D convolutional neural network architecture is also designed as the second method. They are implemented and compared with state-of-the-art approaches on two data sets. The results exhibit that method 1 is superior when the shortage of sample data is the main restriction. It respectively achieves recognition accuracies of 97.8% and 99.8% for the Weizmann and KTH action data sets. In addition, method 2 is considerable for its automatic features extraction, and achieves an acceptable result with lots of original training data. As a result, it gets recognition accuracy of 92% for the KTH data set while this value is drastically reduced for the Weizmann data set.
Despite any doubts about driving safety, many stroke drivers drive again due to the absence of valid screening tools. The on-road test is considered a formal assessment, but there are safety issues in testing directly on stroke patients who are not fully capable of driving. A driving simulator is a promising tool since it provides meaningful information for identifying hazards to driving safety across different driver populations and driving conditions. Using the advantages of a driving simulator, we propose a Driving Performance Assessment System for Stroke drivers (Driving-PASS). Driving-PASS is designed not only to pre-screen invalid stroke drivers before the on-road test but also to provide problematic driving items for the use in driving rehabilitation. To design assessment classifiers, i.e., the core engine of Driving-PASS, we collect driving data from a total of twenty-seven participants in thirteen driving scenarios. Thereafter, we get subjective assessment results from ten driving evaluators in eleven assessment items. By using driving data and subjective assessment results, we construct eleven assessment classifiers for ten driving ability items and one driving suitability item. We addressed the technical challenges such as handcrafted features and imbalanced dataset by a feature extraction method using pre-trained CNN models and a resampling method. Through comprehensive performance evaluation, we build eleven accurate assessment classifiers in Driving-PASS by carefully selecting deep features in each assessment item. We envision that Driving-PASS can be used as a pre-screening tool for evaluating stroke drivers and will ultimately improve road safety.