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

Xiyin Ye, Tao Yu

Frequency combs are a spectrum of equally spaced frequency components with very high time-frequency accuracy, which have been widely used in the optical and microwave frequency ranges. We propose the realization of a frequency comb operating at the terahertz regime in terms of the nonlinear dynamics of electric-polarization waves, or ferrons as their quanta, in the ferroelectric materials. The efficiency of the frequency comb of the electric-polarization waves is exactly proportional to the static electric polarization carried by the ferron modes, which thereby offers new opportunities for the direct observation and application of the intrinsic properties of ferrons.

Mrudula Prasad, Benedikt Prifling, Matthias Neumann et al.

The conductive additive and binder domain (CBD) is an essential component of lithium-ion battery electrodes. It enhances the electrical connectivity and mechanical stability within the solid electrode matrix. The CBD aggregate exhibits inner porosity that significantly impacts ion transport within the electrode. Thus, the spatial distribution of CBD and its morphology play a critical role for ion transport pathways within the electrode. In order to quantify the extent of this influence, we employ high-resolution focused ion beam/scanning electron microscopy (FIB-SEM) imaging and isolate regions with just solid CBD and pore. This enables us to quantitatively correlate the CBD morphology with physical transport parameters and present a function that describes the relationship between CBD porosity and its ionic conductivity. Through our work, we provide insights into the CBD microstructure for use in future continuum-scale models.

Xin Cao, Chunxiao Mei, Zhiyong Song et al.

In order to solve the problem of difficulty in learning semantic pattern representations between user dynamic interest sequences using path based and knowledge graph based entity embedding methods, the author proposes research on spatiotemporal data modeling and prediction algorithms in intelligent management systems. The author first makes a preliminary analysis of the wireless network data (mainly the data of cellular mobile networks) obtained by Internet service providers, reveals that the data of adjacent base stations have temporal and spatial correlations, then establishes a hybrid deep learning model for spatio-temporal prediction, and proposes a new spatial model training algorithm. Finally, experiments were conducted using wireless network datasets to evaluate the performance of the model. The experimental results show that based on data analysis, it can be seen that the prediction of the system has effectively improved by 99 %. Conclusion: The spatiotemporal data modeling and prediction algorithm proposed by the author in the intelligent management system significantly improves prediction accuracy.

Qian Liu, Kaito Kanahashi, Keiichiro Matsuki et al.

Jianhong Wu, Dongmin Qian, Ruishan Wang et al.

Abstract Perovskite light‐emitting diodes (LEDs) have achieved their highest efficiency with an n‐i‐p device structure, utilizing n‐type ZnO as the electron transport layer. The exceptional device efficiency is highly dependent on the interfacial reaction between ZnO and perovskite intermediates, which promotes the formation of high‐quality perovskite films. However, achieving green and blue perovskite LEDs with this n‐i‐p device structure remains a challenge, which hinders the fabrication of full‐color perovskite LED arrays with a consistent device structure. This challenge stems from the vigorous interfacial reaction between ZnO and bromine/chlorine‐based perovskites compared to iodine‐based perovskites during the crystallization process. Here, n‐i‐p perovskite LEDs with enhanced device performance on a relatively stable n‐type SnO2 layer are demonstrated. The near‐infrared perovskite LEDs based on SnO2 reach a peak external quantum efficiency of 21.2%. More importantly, this approach enables the realization of full‐color n‐i‐p perovskite LEDs, offering significant potential for streamlining the manufacturing process of full‐color displays.

Guanjie Wang, Jingjing Hu, Jian Zhou et al.

With ChatGPT starting a storm of transformative applications worldwide, the advent of large language models (LLMs) has revolutionized the paradigm of scientific research, shifting from data-driven methods to AI-driven science. While LLMs have demonstrated significant promise in many fields of science, the development of material knowledge-guided, domain-specific LLMs remains challenges. In this review, the key milestones of LLMs are discussed, and guidelines for building LLMs are provided, including determining objectives, designing model architectures, data curation, and establishing training and evaluation frameworks. Furthermore, methodologies for creating domain-specific models through fine-tuning, retrieval-augmented generation, prompt engineering, and AI agents are explored. Additionally, the applications of LLMs in materials science are investigated, ranging from structured information extraction and property prediction to autonomous laboratories and robotics. Finally, challenges such as resource demands, dataset quality, benchmarking, hallucination mitigation, and AI safety are reported alongside emerging opportunities, positioning LLMs as a pivotal tool in advancing materials discovery and innovation.

Jae‐Soon Yang, Min‐Ho Seo, Min‐Seung Jo et al.

Abstract Flexible pressure sensors have emerged as indispensable components in advancing wearable electronics, healthcare systems, and next‐generation human‐machine interfaces. To enable these applications, significant progress has been made in improving the sensitivity of flexible pressure sensors. However, achieving bending insensitivity—crucial for reliable pressure detection under dynamic and curved conditions—remains a critical challenge. In this study, a high‐performance flexible capacitive pressure sensor is presented that successfully integrates bending insensitivity with enhanced pressure sensitivity. By leveraging the percolation effect within a sub‐100 nm nanograting structure, the design of the pressure sensor is optimized through numerical analysis and finite element method (FEM) simulations. Fabricated using a nanoscale wet‐chemical digital etching process and nanoimprint lithography, the sensor features a sub‐100 nm valley nanograting structure. It exhibits an exceptional sensitivity of 0.05 kPa⁻¹, achieving capacitance changes 4.2 times greater than those of flat substrate designs. Furthermore, the sub‐100 nm nanostructured pressure sensor effectively reduces bending strain to 0.175 times that of flat substrates, ensuring stable performance even at a 2.5 mm radius of curvature. This highly reliable flexible pressure sensor array enables real‐time pressure mapping and human artery pulse monitoring, making it highly suitable for tactile and wearable sensing applications.

Zdzislaw Trzaska

Shilin Liu, Yijing Ding, Xin Wang et al.

Abstract Signal amplification is vitally important for sensing low‐dose X‐rays in medical diagnosis by amplifying the generated electric read‐out signal. However, the complexity of external amplification circuits hampers device miniaturization and portability, while integrating amplification functionality directly into sensors or detectors remains a significant and formidable challenge. In this work, a direct high‐gain X‐ray detector with facile electron drift speed manipulation is reported in perovskite single‐crystal film (SCF). By employing laser‐assisted nucleation,   high‐quality MAPbBr3 SCF is fabricated with precise control of thickness from ≈20 to ≈500 µm and the area up to 3 by 2 cm, while the architecture of ITO/MAPbBr3/Au is constructed to form the Schottky junction in opposite polarity. With the assistance of applied bias, the space electric field over MAPbBr3 SCF can be tunable, which ensures the manipulation of charge carrier drift speed to form recirculation for internal gain. The resultant photodetector exhibits an ultrahigh sensitivity of 1.44 × 105 µC Gy−1 cm−2 with a gain of 5.14 × 105, an ultralow detection limit of 39.8 nGy s−1, and the X‐ray array imaging is achieved at a low dose rate of 5 µGy s−1. These results confirm the advance of high‐gain detectors in constructing sensing arrays for practical safe medical diagnosis.

Biplov Paneru, Bishwash Paneru, Sanjog Chhetri Sapkota et al.

The healthcare industry has blossomed into one of the most pivotal and technologically advanced sectors in the past decade. Individuals grapple with the peril of untimely demise from diverse ailments as patients suffer from delayed treatment. The paramount objective is to forge a dependable patient care system utilizing the Internet of Things (IoT), enabling physicians to monitor patients' well-being within medical facilities or even the confines of their homes. The system aids in tracking the patient's SpO2 level, body temperature, pulse rate (beats per minute), room temperature, and humidity, then trains the data with machine learning algorithms for the patient and finally monitors it through the Blynk IoT system. The cloud-stored data can be harnessed to ascertain and supervise one's health and predict forthcoming perils. This study unveils an efficacious decision-making model custom-tailored for Internet of Things (IoT) ventures, and the proposed trained algorithm satiates these requirements, offering efficiency and precision, rendering it appropriate for numerous IoT applications. Finally, the Shapley Additive Explanations (SHAP) is used here for finding out the most influential parameters, and Explainable AI (XAI) is utilized with the help of SHAP values for enhanced information on affecting parameters. The SVC model's hyperparameter is properly adjusted, yielding a testing accuracy of 98.83 % and a training accuracy of 98.71 %. On cross-validation, the lightweight Sklearn model achieved a mean accuracy of almost 99 %. And with a SHAP weightage magnitude of 1.38, 0.91, and 0.44 for Class ‘Good’, Class ‘Poor’, and Class ‘Bad’, respectively, patients SpO2 level is the most significant feature.

Liuting Shang, Sheng Lu, Yichen Zhang et al.

Datapath synthesis is a crucial step in synthesis flow and aims at globally minimizing an area by identifying shareable logic structures. This paper introduces a novel Directed Acyclic Graph (DAG)-based datapath synthesis method based on graph isomorphism and gate reconfiguration. Unlike algorithms that identify common specification logic, our approach simplifies the problem by focusing on searching for common topology. Leveraging the concept of gate reconfiguration, our algorithm extends the applicability of DAG-based datapath synthesis by transforming a topology-equivalent network into a specification-equivalent network. Experimental results demonstrate up to 23.6% improvement when optimizing the adder–subtractor circuit, a scenario not addressed by existing DAG-based datapath synthesis algorithms.

Zhi Zhang, Yongqiang Sun

Energy meter billing is an important part in the Energy Management System and Feed-in tariff schemes become more popular in many countries nowadays. However traditional techniques are complicated for getting the tariff values. Therefore, a novel IoT based technique namely Smart TARiff (STAR) calculator has been proposed for calculating the Feed-in tariff for residents which in turn improves the efficiency of the feed-in tariff scheme for renewable resources. The power generated from the solar panel has been stored in batteries. The sensors such as current and voltage sensors are used to measure the voltage and electricity supplied and fuzzy system will calculate the tariff and the calculated tariff will be send to the user via the internet. The experimental findings reveal that the energy generated by the suggested system is three times that of the system without intelligent functionalities. The Proposed approach improves the overall accuracy of the proposed Star, Chip based IoT Electrical Meter, and EMS is 99.03 %, 91.36 %, and 99.03 respectively.

Md Sazid Bin Sadeque, Mahmudur Rahman, Md Mehdi Hasan et al.

Abstract Triboelectric nanogenerators (TENGs) utilize the synergetic effect of triboelectrification and electrostatic induction to guide electrons through an external circuit, enabling low‐frequency mechanical and biomechanical energy harvesting and self‐powered sensing. Integrating 2D material with a high specific surface area into flexible ferroelectric polymers such as polyvinylidene difluoride (PVDF) has proven to be an efficient strategy to improve the performance of TENG devices. Scalable fabrication of graphene‐integrated PVDF nanocomposite fiber using thermal drawing process is demonstrated for the first time in this study. The open‐circuit voltage and short‐circuit current show 1.41 times and 1.48 times improvement with the integration of 5% graphene in the PVDF fibers, respectively. The TENG fabric shows a maximum power output of 32.14 µW at a matching load of 7 MΩ and a power density of 53.57 mW m−2. The fibers exhibit excellent stability in harsh environmental conditions such as alkaline medium, high/low temperature, multi‐washing cycle, and long‐time usage.

Yuncheng Zhang, Dingxin Xu, Kenichi Okada

This article examines the research area of digital phase-locked loops (DPLLs), a critical component in modern electronic systems, from wireless communication devices to RADAR systems and digital processors. As the demands for higher integration levels in electronic systems increase, DPLLs have become a key point for research and development. Implemented in scaled digital CMOS process, DPLLs offer potential advantages over traditional analog designs and have explored the boundaries of phaselocked loop (PLL) design. This article delves into several key directions of DPLL research: improvements in PLL performance through digital methods, the automation of PLL design using commercial electronic design automation (EDA) tools, and innovative approaches for using low-frequency references in wireless applications. Specifically, it covers the DPLL architectures using time-to-digital and digital-to-time converters, as well as bang–bang phase detectors, fully synthesizable DPLLs, and the integration of oversampling techniques that enable the use of a 32-kHz reference to avoid using bulky higher-frequency reference sources. This review outlines current achievements of DPLLs research in these directions.

Tiago V. C. Antão, Jose L. Lado, Adolfo O. Fumega

Twisted magnetic van der Waals materials provide a flexible platform to engineer new forms of unconventional magnetism. Here we demonstrate the emergence of electrically tunable topological moiré magnetism in twisted bilayers of the spin-spiral multiferroic NiI$_2$. We establish a rich phase diagram featuring uniform spiral phases, a variety of $kπ$-skyrmion lattices, and nematic spin textures ordered at the moiré scale. The emergence of these phases is driven by the local stacking and the resulting modulated frustration in the spin spiral stemming from the moiré pattern. Notably, when the spin-spiral wavelength is commensurate with the moiré length scale by an integer $k$, multi-walled skyrmions become pinned to the moiré pattern. We show that the strong magnetoelectric coupling displayed by the moiré multiferroic allows the electric control of the $kπ$-skyrmion lattices by an out-of-plane electric field, which couples to the moiré-induced electric polarization. While adiabatic changes in the electric field preserve the topology of the spin configurations, abrupt variations can trigger transitions between different skyrmion lattice ground states. Our results establish a highly tunable platform for skyrmionics based on twisted van der Waals multiferroics, potentially enabling a new generation of ultrathin topologically-protected spintronic devices.

Alice K, Deepa N, Devi T et al.

Glaucoma is an eye disease that damages the optic nerve which connects the eye to the brain. When the fluid pressure inside the eye (intraocular pressure) increases, the optic nerve get impaired and has doubled the chance for diabetic patients resulting in irreversible loss of vision if not detected in early stages. In developing countries, due to the scarcity of ophthalmic experts and lab facilities, the needs for eye disease detecting automation system are increased without saying. The field of artificial intelligence is providing many solution's especially in health care domain. The proposed work generate models for recognizing the presence of glaucoma based on open access public dataset of retinal fundus images using machine learning algorithms with the help of image feature descriptors. It classifies the given retinal fundus image as normal or abnormal in two stages. Firstly it extracts image features using appropriate filters and then it is trained through tree based ensemble classifier to classify the given input image and then the same is tested to get the better accuracy performance. The above two steps are iterated by varying over the three effective filters like edge histogram, fuzzy color and texture histogram and pyramid histogram of gradients. The proposed experiment based on this approach reveals that the use of Edge histogram filter in combination with fuzzy color and texture histogram with Random forest classifier yields maximum accuracy of 80.43% and AUC 0.884. The results obtained by applying multi filters is better than that obtained by applying single filter.

Jiawei Wu, Wenling Jiao, Yongshi Guo et al.

Abstract Over the last decade, the rapid development of wearable electronics has generated renewed interest in textiles. The integration of advanced nanotechnology and microelectronics with well‐established textile production processes has resulted in textile electronics, that are lightweight, flexible, breathable, and conformable, which broadens the applications of electronic products. The hierarchical textile structure, ranging from a single fiber to twisted yarns and various fabrics, is suitable for constructing multifunctional flexible devices. In particular, yarn, which bridges between fiber and fabric, is advantageous owing to its easy integration into wearable formats via weaving, knitting, or braiding. However, because of the dearth of effective interdisciplinary communication between researchers of electronic and textile engineering, fabricating yarn‐based devices with superior mechanical properties and versatile electronic functionality is difficult. Therefore, this review provides an overview of yarn‐based electronics, followed by a systematical summary of recent progress in yarns with respect to material and device design, multifunctional integration, and applications in wearable devices, including sensors, actuators, stealth, batteries, and nanogenerators. Furthermore, the major challenges and future developments in this field are discussed.

Tanushree Ghosh, Suchita Kandpal, Chanchal Rani et al.

Abstract Search for a versatile flexible all‐organic liquid electrolyte‐less solid–state electrochromic device continues. In this quest, a polyaniline‐Viologen‐(PANI‐EV)‐based electrochromic device has been reported and designed based on the complementary redox behavior and easy processibility. Electrochromic electrodes are first individually characterized before the device fabrication, to check their compatibility. The polyaniline electrode is electrodeposited and characterized using scanning electron microscopy and Raman techniques followed by bias dependent absorbance measurements to understand its color switching capabilities. In situ kinematics are performed on the finished solid–state device and electrochromic performance have been measured. An improved electrochromic performance can be observed as evident from <1 s switching time while switching with a color contrast of ≈75% and good cycle life. The device displays switching in visible, as well as IR and NIR regions with an application of bias as low as 1.5 V. In addition to the multiple wavelength switching, a device has also been fabricated on a plastic electrode to demonstrate all‐organic flexible liquid electrolyte less solid–state versatile electrochromic device.

Jie Jiang, Ruth Pachter, Krishnamurthy Mahalingam et al.

Abstract Although understanding filament formation in oxide‐based memristive devices by theory has emerged, there are still fundamental unanswered questions. Importantly, for practical application of thin films the material in its amorphous state is to be considered, but mostly lacking so far, and details on sub‐stoichiometry are also scarce. To gain insight into the optical and electronic properties of sub‐stoichiometric amorphous tantalum oxide (TaOx), the electron energy loss spectrum (EELS) of model systems is characterized theoretically and electron transport characteristics are analyzed in detail. Calculated blue‐shifts by increasing sub‐stoichiometry explained the measurements, potentially suggesting estimation of oxygen vacancy concentrations through EEL spectra. Electron transport results based on TaOx material models validated by EELS measurements show that oxygen vacancy filamentary paths are initiated at low bias upon increasing sub‐stoichiometry yet noting an interplay with the local amorphous structure. Contact resistances at interfaces of the TaOx switching layer and a tantalum scavenging layer or titanium nitride electrode are quantified, indicating the possibility for either oxygen vacancy‐ or metal cluster‐based conduction mechanisms at the interface. The computational work, combined with experimental characterization for validation, provides a basis for investigating effects of sub‐stoichiometry on filament formation in TaOx thin film memristive devices.

Ying Li, Zhencai Zhu, Xiaoqiang Li et al.

As the most important auxiliary transportation equipment in coal mines, mining electric locomotives are mostly operated manually at present. However, due to the complex and ever-changing coal mine environment, electric locomotive safety accidents occur frequently these years. A mining electric locomotive control method that can adapt to different complex mining environments is needed. Reinforcement Learning (RL) is concerned with how artificial agents ought to take actions in an environment so as to maximize reward, which can help achieve automatic control of mining electric locomotive. In this paper, we present how to apply RL to the autonomous control of mining electric locomotives. To achieve more precise control, we further propose an improved epsilon-greedy (IEG) algorithm which can better balance the exploration and exploitation. To verify the effectiveness of this method, a co-simulation platform for autonomous control of mining electric locomotives is built which can complete closed-loop simulation of the vehicles. The simulation results show that this method ensures the locomotives following the front vehicle safely and responding promptly in the event of sudden obstacles on the road when the vehicle in complex and uncertain coal mine environments.