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Grating pitch comparator traceable to the Cr atom transition frequency

Measurement: Journal of the International Measurement Confederation 2025年 242卷

作者： Lin, Zichao Deng, Xiao Wu, Yize Feng, Jingtong Zhu, Hongyu Yang, Yaao Yu, Jing Xue, Dongbai Wang, Jianbo Shi, Yushu Jin, Tao Zhang, Wentao Dun, Xiong Cheng, Xinbin Li, Tongbao MOE Key Laboratory of Advanced Micro-Structured Materials Shanghai Frontiers Science Center of Digital Optics Institute of Precision Optical Engineering School of Physics Science and Engineering Tongji University Shanghai200092 China National Institute of Metrology China Beijing100029 China College of metrology and measurement engineering China Jiliang University Hangzhou310018 China Guangxi Key Laboratory of Optoelectronic Information Processing School of Optoelectronic Engineering Guilin University of Electronic Technology Guilin541004 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

Calibration of the grating pitch typically requires metrological instruments or diffraction techniques. The traceability and stability demands for laser wavelength in these techniques present substantial challenges for on-site grating calibration. This paper introduces a novel traceability approach to grating pitch calibration using a grating pitch comparator that utilizes the transition frequency of the chromium. The chromium transition frequency is converted into a chromium grating through atom lithography, establishing a self-traceable length standard dCr = 212.7787 ± 0.0049 nm. Calibration of the test grating's pitch is achieved by comparing the phase shift caused by the simultaneous movement of the chromium grating and the test grating. By using the dCr, this approach combines the high-accuracy of fundamental physical constants with the environmental stability of grating interferometers, eliminating the need for measuring laser wavelength, air refraction, diffraction angles. It significantly enhances the precision and makes more straightforward and portable, crucial for facilitating traceable on-site grating measurements. © 2024

关键词： Comparators (optical)

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A self-adapting artificial SEI layer for carboxymethyl cellulose lithium coating for high performance zinc anode

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Journal of Alloys and Compounds 2025年 1030卷

作者： Gao, Song Yang, Yanqiu Jin, Mingyue Jia, Hongsheng Li, Angda Qi, Yali School of Mathematics and Computer Science Jilin Normal University Siping136000 China College of Engineering Jilin Normal University Siping136000 China Key Laboratory of Functional Materials Physics and Chemistry of the Ministry of Education Jilin Normal University Siping136000 China School of Materials Science and Engineering Sichuan University Chengdu610211 China School of Architectual Engineering Jilin Engineering Vocational College Siping136001 China

Zinc-ion battery is considered to be one of the most promising electrochemical energy storage systems in the field of energy storage due to its low cost and high safety. In addition, the zinc anode has a high energy density (>300 Wh kg−1) and a stable electrochemical window (1.0–2.0 V) due to its high theoretical specific capacity (820 mAh g−1) and moderate redox potential (-0.76 V vs. SHE). However, the development of zinc ion batteries with metal zinc anodes is facing major challenges due to the hydrogen evolution, dendrite formation, a large number of side reactions, low Coulomb efficiency and unstable cycle performance of metal zinc anodes. Here, we propose a modification method of artificial sei layer carboxymethyl cellulose lithium (CMC-Li). The modified zinc anode has high ion migration number, high Young 's modulus (464 MPa) and good zinc affinity, which can adapt to the volume change of zinc anode. The symmetrical battery with CMC-Li@Zn as the negative electrode exhibits more than 900 hours of operational stability at a current density of 2 mA cm−2. The experimental results show that the full cell with V2O5//CMC-Li@Zn structure can maintain a high coulombic efficiency of 99.75 % after 1000 cycles at a current density of 1 A g−1. This finding fully confirms that the modified zinc anode has significant advantages and application prospects in improving the comprehensive performance of zinc ion batteries. © 2025 Elsevier B.V.

关键词： Battery storage

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Efficient terahertz radiation generation from air plasma with mid- and near-infrared three-color femtosecond pulses

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Optics Express 2025年第11期33卷 22610-22631页

作者： Bintao Fu Zhiwen Zhang Zefu Liu Kang Wang Liwei Song Wenkai Li Vasily A Kostin Alexer A Silaev Longzhen Zhuang Chenhui Lu Yi Liu School of Optical Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai 200093 China State Key Laboratory of Ultra-Intense Laser Science and Technology Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Sciences Shanghai 201800 China Gaponov-Grekhov Institute of Applied Physics of the Russian Academy of Sciences Nizhny Novgorod 603950 Russia Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod 603022 Russia School of Mechanical and Automotive Engineering Shanghai University of Engineering Science Shanghai 201620 China

We report on the generation of the terahertz (THz) radiation from air plasma driven by a three-color femtosecond field with the fundamental pulse in the near- (1700 nm and 800 nm) and mid-infrared (3900 nm) ranges. With an inline optical setup for three-color pulse generation and relative phase control, we observed that the THz pulse energy can be enhanced up to 6 times compared with the conventional two-color excitation scheme, dependent on the incident fundamental pulse energy. The polarization of the enhanced THz pulse was found to be elliptical, with the second and third harmonics components of the three-color pump also being elliptically polarized. With the 3.9 µm driving pulses, the maximum THz pulse energy reaches 39.4 µJ with a conversion efficiency of about 1%. Based on the plasma current model, we analytically and numerically reproduced the THz energy enhancement, as well as its dependence on incident pulse energy. The THz enhancement can be attributed to the two additional ionization wave mixing channels introduced with the addition of the third harmonic.

关键词： Femtosecond lasers Femtosecond pulses Nonlinear optical crystals Optical fields Sum frequency generation Ultrafast lasers

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Deep Learning-Powered Edge Analytics for IoT-based Sensor Networks

Deep Learning-Powered Edge Analytics for IoT-based Sensor Ne...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： M. Saravana Karthikeyan Ponnusamy Subramani M. Panneer Selvam M. L. Sworna Kokila R. Vijayalakshmi P. Sundaravadivel Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Physics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electrical and Electronics Engineering Sona College of Technology Salem India Department of Computing Technologies Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India Department of Artificial Intelligence and Machine Learning Saveetha Engineering College Chennai India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The rapid expansion of loT-based sensor networks has necessitated the development of efficient edge analytics frameworks to process vast amounts of data in real time while minimizing computational overhead. Deep learning-powered edge computing has emerged as a transformative approach, enhancing data processing capabilities at the network edge while addressing constraints related to latency, bandwidth, and energy consumption. This study introduces a novel algorithm, Lightweight AI Model Compression for Energy-Efficient IoT Edge Devices (LiteEdgeAI), designed to optimize deep learning models for resource-constrained edge environments. The proposed method employs quantization, pruning, and knowledge distillation techniques to achieve significant reductions in model size and computational complexity while maintaining high inference accuracy. To validate the efficiency of LiteEdgeAI, a comparative simulation analysis was conducted against existing state-of-the-art algorithms using key performance metrics such prediction accuracy, energy efficiency and computational complexity. Experimental results demonstrated that LiteEdgeAI outperformed baseline models. The findings highlight the potential of LiteEdgeAI in enabling scalable, low-power, and real-time edge intelligence for IoT-based sensor networks.

关键词： Deep learning Adaptation models Accuracy Computational modeling Predictive models Prediction algorithms Energy efficiency Real-time systems Internet of Things Computational complexity

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UAV-Mounted RIS Enabled Maritime Secure Sensing With Joint Beamforming and Trajectory Design

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IEEE Transactions on Vehicular Technology 2025年

作者： Hao, Xu Ma, Haoyu Wang, Wei Zeng, Feng Cumanan, Kanapathippillai Bjornson, Emil Nantong University School of Information Science and Technology Nantong226019 China University of York School of Physics Engineering and Technology YorkYO10 5DD United Kingdom KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm16440 Sweden

This paper considers a challenging maritime secure sensing system, in which, a legitimate shipborne radar L needs to detect in real-time the state of a collaborative unmanned aerial vehicle (UAV) in the presence of an illegitimate or unauthorized shipborne radar U. To address this challenge, we propose a novel UAV-mounted reconfigurable intelligent surface (RIS) assisted approach, where the RIS is mounted on the UAV for enhancing/suppressing its reflected signal towards the L/U to facilitate/prevent its detection of the UAV. Furthermore, the UAV target can also adjust its flight route to move closer to/away from the L/U to improve/decrease the sensing performance. Due to the mobility of both the shipborne radars and UAVs, the Doppler shift effects of the RIS-ship channel need to be considered. In such a setup, the maximization of the received power at the L is formulated into an optimization problem while suppressing the received power at the U below a certain threshold by jointly designing the reflecting phase shifts of RIS and the 3D flight trajectory of UAV. The non-convex optimization problem is decomposed into two subproblems and solved via an iterative algorithm. Simulation results are presented to corroborate the effectiveness and tremendous potential of UAV-mounted RISs in the maritime secure sensing. © 1967-2012 IEEE.

关键词： Drones

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Accurate multi-category student performance forecasting at early stages of online education using neural networks

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Scientific Reports 2025年第1期15卷 1-19页

作者： Junejo, Naveed Ur Rehman Nawaz, Muhammad Wasim Huang, Qingsheng Dong, Xiaoqing Wang, Chang Zheng, Gengzhong School of Physics and Electronic Engineering Hanshan Normal University Chaozhou 521041 China Department of Computer Science and Engineering Hanshan Normal University Chaozhou 521041 China Department of Computer Engineering The University of Lahore Punjab Lahore 54000 Pakistan School of Mathematics and Statistics Hanshan Normal University Chaozhou 521041 China

The ability to accurately predict and analyze student performance in online education, both at the outset and throughout the semester, is vital. Most of the published studies focus on binary classification (Fail or Pass) but there is still a significant research shortcoming in predicting performance of students across multiple categories. This study introduces a novel neural network-based approach capable of accurately predicting student performance and identifying vulnerable students at early stages of the online courses. The open university learning analytics (OULA) dataset is employed to develop and test the proposed model, which predicts outcomes in Distinction, Fail, Pass, and Withdrawn categories. The OULA dataset is preprocessed to extract features from demographic data, assessment data, and clickstream interactions within a virtual learning environment (VLE). Novel features engineering has been utilized to predict students’ performance across multiple categories at early stages of courses. Specially, students’ VLE interactions are aggregated by total clicks to represent daily engagement and assess online activity. Comparative simulations indicate that the proposed model significantly outperforms existing baseline models including artificial neural network long short-term memory (ANN-LSTM), random forest (RF) ‘gini’, RF ‘entropy’ and deep feed forward neural network (DFFNN) in terms of accuracy, precision, recall, and F1-score. The results indicate that the prediction accuracy of the proposed method is about more than the existing state-of-the-art methods. Furthermore, compared to existing methodologies, the model demonstrates superior predictive capability across temporal course progression, achieving superior accuracy even at the initial phase of course completion. © The Author(s) 2025.

关键词： Deep Learning Early-prediction Feature engineering Machine Learning Virtual Learning Environment

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Digital Quantum Simulations of the Non-Resonant Open Tavis–Cummings Model

arXiv

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arXiv 2025年

作者： Sims, Aidan N. Patel, Dhrumil Philip, Aby Rubin, Alex H. Bandyopadhyay, Rahul Radulaski, Marina Wilde, Mark M. School of Applied and Engineering Physics Cornell University IthacaNY14850 United States Department of Computer Science Cornell University IthacaNY14850 United States Department of Electrical and Computer Engineering University of California DavisCA95616 United States Department of Physics and Astronomy University of California DavisCA95616 United States Applied Quantum Algorithms Leiden University Leiden2333 CA Netherlands School of Electrical and Computer Engineering Cornell University IthacaNY14850 United States

The open Tavis–Cummings model consists of N quantum emitters interacting with a common cavity mode, accounts for losses and decoherence, and is frequently explored for quantum information processing and designing quantum devices. As N increases, it becomes harder to simulate the open Tavis–Cummings model using traditional methods. To address this problem, we implement two quantum algorithms for simulating the dynamics of this model in the inhomogenous, non-resonant regime, with up to three excitations in the cavity. We show that the implemented algorithms have gate complexities that scale polynomially, as O(N2) and O(N3). One of these algorithms is the sampling-based wave matrix Lindbladization algorithm, for which we propose two protocols to implement its system-independent fixed interaction, resolving key open questions of [Patel and Wilde, Open Sys. & Info. Dyn., 30:2350014 (2023)]. Furthermore, we benchmark our results against a classical differential equation solver and demonstrate the ability to simulate classically intractable systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Jaynes-Cummings model

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Dynamic characteristics of packed bed latent heat thermal storage tank to smooth solar energy fluctuations

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Applied Thermal engineering 2025年 273卷

作者： Mokhtar, Omar ELSihy, ELSaeed Saad Xu, Chao Du, Xiaoze Wu, Hongwei Ministry of Education Beijing102206 China Mechanical Power Engineering Department Faculty of Engineering Mattaria branch Helwan University Massaken El-Helmia P.O 11718 Cairo Egypt School of Energy and Power Engineering Lanzhou University of Technology Lanzhou730050 China School of Physics Engineering and Computer Science University of Hertfordshire HatfieldAL109AB United Kingdom

Thermal energy storage utilizing phase change materials (PCMs) is a crucial technology for solar energy applications. However, there is a lack of knowledge regarding investigating the influence of the solar flux intensity fluctuations during the day on the performance of water/PCM-packed bed thermocline storage systems, which typically necessitate a stable heat load output. In this context, the study examines two distinct flux intensities in separate regions of Egypt: Qena and Sinai. Subsequently, the research delves into the effects of the melting temperature of PCMs and the discharge flow rate on the performance under actual solar conditions, employing the flux data from Sinai. A comprehensive two-dimensional unsteady mathematical model is developed to effectively couple the transient temperatures of water and PCM spherical capsules within the bed and perform energy and exergy analyses. The results indicated that the system performance using Sinai's flux surpasses Qena's flux in terms of both charging duration and discharging capacity. Besides, Sinai's flux facilitates a more rapid charging of the storage tank than Qena's, demonstrating a 28 % enhancement in the charging rate and a 4 % improvement in charging power. The PCM with a lower melting temperature demonstrates the highest charging efficiency. In addition, the tank system employing RT55 has an exergy efficiency of 73.3 %, while the tank system utilizing RT65 attains an efficiency of 74.77 %. The exergy efficiency of the packed bed tank and the whole system demonstrates an inverse correlation with the discharge flow rate. A threefold increase in the discharge flow rate, from 0.3 to 0.9 m3/hr, shows a 5.3 % reduction in exergy efficiency for the tank and the whole system. © 2025 Elsevier Ltd

关键词： Latent heat

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Crystallization of the transdimensional electron liquid

arXiv

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arXiv 2025年

作者： Bondarev, Igor V. Boltasseva, Alexandra Khurgin, Jacob B. Shalaev, Vladimir M. Department of Mathematics & Physics North Carolina Central University DurhamNC27707 United States Elmore Family School of Electrical & Computer Engineering Purdue Quantum Science & Engineering Institute Birck Nanotechnology Center West LafayetteIN47907 United States School of Materials Engineering Purdue University West LafayetteIN47907 United States Department of Electrical & Computer Engineering Whiting School of Engineering Johns Hopkins University BaltimoreMD21218 United States

By calculating the critical densities, temperatures and phase diagrams, we show that metallic transdimensional materials are unique in that by simply reducing their thickness one can Wigner crystallize their free electrons at room temperature. When it occurs, the electrons get pinned on a two-dimensional periodic triangular lattice to form an electron super-solid (electron solid inside of a crystalline material). Such a solid melts and gets frozen back with raising and lowering the temperature, respectively, and its resistivity behaves opposite to the free electron gas model predictions. © 2025, CC BY.

关键词： Crystalline materials

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Predicting Catalytic Properties of Transition Metal Sulfides Using Fine-Tuned Large Language Models: A High-Accuracy Approach for Band Gap and Predicted Stable Estimation

SSRN

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SSRN 2025年

作者： Zhao, Zimo Hu, Lin Wang, Honghui College of Computer Science and Cyber Security Demonstrative Software School Chengdu University of Technology Sichuan Chengdu610059 China College of Physics and Electronics Engineering Sichuan Normal University Sichuan Chengdu610101 China

This study presents a fine-tuned Large Language Model approach for predicting band gap and predicted stable of transition metal sulfides. By constructing a domain-specific knowledge base and using a strategically curated dataset of 554 compounds, the fine-tuned GPT-3.5-turbo model demonstrated significant improvements through nine iterations, with band gap prediction R2 values increasing from 0.7564 to 0.9989, while stability classification attained F1>0.7751. This framework automates feature extraction from text data and adapts to tasks through transfer learning with minimal domain data. Scalable from hundreds to tens of thousands of samples, With comparable accuracy, the fine-tuned model outperforms the traditional model and GPT-3.5 and GPT-4.0 in other aspects such as data set expansion. By combining adaptive learning with continuous knowledge integration, it precisely models catalytic material structure-property relationships. This artificial intelligence solution accelerates discovery and optimization of transition metal sulfide catalysts while resolving challenges in automated material characterization and performance prediction. © 2025, The Authors. All rights reserved.

关键词： Prediction models

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