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Stability and energetics of two-dimensional surface crystals in liquid AuSi thin films and nanoscale droplets

Physical Review Materials 2021年第7期5卷 074003-074003页

作者： Hailong Wang Mengyuan Wang Alireza Shahabi Alain Karma Moneesh Upmanyu CAS Key Laboratory of Mechanical Behavior and Design of Materials Department of Modern Mechanics CAS Center for Excellence in Complex System Mechanics University of Science and Technology of China Hefei Anhui 230027 China Group for Simulation and Theory of Atomic-Scale Material Phenomena (stAMP) Department of Mechanical and Industrial Engineering Northeastern University Boston Massachusetts 02115 USA Department of Physics Northeastern University Boston Massachusetts 02115 USA

We employ atomic-scale computational frameworks to study the surface crystallization in AuSi films and droplets as a function of composition, temperature, and size. Above the melting point, the surfaces of both thin films and droplets undergo a first-order transition from a two-dimensional (2D) Au2Si crystalline phase to a laterally disordered yet stratified layer. The thin film surfaces exhibit an effective surface tension that increases with temperature and decreases with Si concentration, while for droplets in the size range 10–30 nm, the bulk Laplace pressure alters the surface segregation as it occurs with respect to a strained bulk. Above the transition, the size effect on the surface tension due to the stratified surface layer is small, while the crystalline surface layer below the transition is strained and composed of 2D crystallites separated by extended grain boundary scars that lead to large fluctuations in its energetics. As a specific application, all-atom simulations of AuSi droplets on Si(111) substrate subject to Si surface flux show that the supersaturation dependent surface tension destabilizes the contact line via formation of a precursor wetting film on the solid-vapor interface and has ramifications for size selection during droplet-catalyzed routes for nanowire growth. Our study sheds light on the interplay between stability and energetics of surfaces in this unique class of binary alloys and offers pathways for exploiting their surface structure for varied applications such as catalytic nanocrystal growth, dealloying, and polymer crystallization.

关键词： Crystal forms Growth Interface & surface thermodynamics Nanocrystals Structural phase transition 2-dimensional systems Surfaces Molecular dynamics

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Prediction of Direct Normal Irradiation Based on CNN-LSTM Model

Prediction of Direct Normal Irradiation Based on CNN-LSTM Mo...

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2020年第五届多媒体系统和信号处理国际会议

作者： Chengying Zhang Zhen Yan Chao Ma Xiaoli Xu Key Laboratory of Space Utilization Technology and Engineering Center for Space Utilization Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing Key Laboratory of Measurement and Control of Mechanical and Electrical System Beijing Information Science and Technology University

Due to the intermittency and instability of power output of solar power stations(SPS), the safety and stationarity of the electric grid are facing considerable challenges. So, it is essential to predict the power output of SPS accurately. To date, as the most important meteorological factor determining the power output of SPS, the prediction of direct normal irradiation(DNI) has always been a hotspot, however at the same time a challenging problem. In recent years, with the improvement of hardware performance, deep learning methods have witnessed rapid development in the field of forecasting of renewable energies. In this paper, a hybrid method combining convolutional neural network(CNN) and longshort term memory(LSTM) neural network is proposed, the former can be effectively used for extraction of spatial feature, while the latter is used for extraction of temporal feature. As a case study, the proposed method is used for prediction of DNI of four typical days, i.e. spring equinox, summer solstice, autumn equinox, and winter solstice, based on the meteorological conditions of Zhangbei. The results show that deep learning methods can be effectively used for prediction of DNI, however, the proposed method in this paper achieves higher prediction accuracy than that of the compared methods.

关键词： Solar power station Direct normal irradiation prediction Deep learning Convolutional neural network Long-short term memory Convolutional-LSTM network

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Constructive Interference Precoding Empowered NOMA-ISAC Design

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IEEE Transactions on Wireless Communications 2025年

作者： Wang, Wei Dong, Chao Zhao, Nan Wu, Qihui Niyato, Dusit Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Nanjing210023 China Dalian University of Technology School of Information and Communication Engineering Dalian116024 China Nanyang Technological University College of Computing and Data Science 639798 Singapore

Non-orthogonal multiple access (NOMA) can help integrated sensing and communication (ISAC) to accommodate more users and well manage interference. In this paper, we first propose a NOMA-ISAC scheme, in which a multiantenna base station (BS) transmits ISAC signal to detect a radar user (RU), and provide wireless service to the RU and the communication user (CU) simultaneously. The inter-user interference can be mitigated by the successive interference cancellation (SIC). We further investigate the trade-off between minimizing the beampattern matching error and maximizing the CU’s achievable signal-to-noise ratio (SNR), and propose a penalty-based semi-definite relaxation (SDR) method to solve this non-convex problem. Then, to mitigate the instantaneous NOMA-ISAC beampattern shaking and enhance its stability, we utilize constructive interference precoding (CIP) to assist the NOMA-ISAC beampattern design. Introducing CIP can convert the interference from RU into the beneficial signal to CU and the complex SIC can be avoided. Then, the corresponding trade-off can be transformed into a convex problem by the Taylor-series approximation, and an iterative algorithm is proposed to solve it. Moreover, the Manopt toolbox assisted initialization is utilized to accelerate its convergence speed. simulation results verify that the proposed CIP-NOMA-ISAC scheme can effectively enhance the stability of instantaneous NOMA-ISAC beampattern over limited time slots, and provide higher SNR for CU. © 2002-2012 IEEE.

关键词： Taylor series

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Prediction of Direct Normal Irradiance Based on Ensemble Deep Learning Models

Prediction of Direct Normal Irradiance Based on Ensemble Dee...

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International Conference on Electronics technology (ICET)

作者： Chengying Zhang Zhen Yan Chao Ma Xiaoli Xu Key Laboratory of Space Utilization Technology and Engineering Center for Space Utilization Chinese Academy of Sciences Beijing China Beijing Key Laboratory of Measurement and Control of Mechanical and Electrical System Beijing Information Science and Technology University Beijing China

ISBN: (数字)9781728162836

ISBN: (纸本)9781728162843

Due to the intermittency and instability of solar power plant output, the key components are often in danger of being scrapped. Therefore, it is necessary to accurately predict the output of solar power plants. At present, the solar radiation intensity is considered to be one of the most important factors affecting the output of solar power plants. In essentially, the solar radiation intensity is a general concept, which is composed of several parameters. Among these parameters, direct normal Irradiance (DNI) prediction has become a hot and difficult research topic. In recent years, with the continuous improvement of hardware performance, a deep learning model has become one of the best methods to solve most time series prediction problems. In this paper, an ensemble deep learning model is proposed. This model integrates the convolutional neural network (CNN) and long short-term memory (LSTM) neural network. The former is responsible for extracting spatial features from data, while the latter is responsible for extracting temporal features. As a case study, the proposed method will predict the DNI of four typical days (spring equinox, summer solstice, autumn equinox and winter solstice) and its seven days before and after which is based on the meteorological data of Zhangbei. The results show that the deep learning model can be effectively applied to DNI prediction. However, the method proposed in this paper has a higher prediction accuracy compared with other models.

关键词： direct normal irradiance prediction deep learning convolutional neural network long-short term memory ensemble learning framework Solar power plants DNI Machine Learning winter solstice Power Plants Equinox irradiance Continuous improvement Radiation flux density intermittency meteorological data Output

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Distributed Coordinated Emergency Frequency Control Strategy for Renewables-Rich Power systems Based on Multi-Agent Reinforcement Learning

SSRN

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SSRN 2025年

作者： Sun, Zhenglong Han, Junpeng Chen, Weihan Zhang, Rui Liu, Yong Levron, Yoash Belikov, Juri Department of Electrical Engineering Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin China The Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering Technion—Israel Institute of Technology Haifa3200003 Israel Department of Software Science Tallinn University of Technology Akadeemia Tee 15a Tallinn12618 Estonia

With the rapid growth of renewable energy in the power system and the implementation of large-capacity LCC-HVDC projects, the frequency support capability of the grid has been reduced. Simultaneously, the renewables-rich grid faces the risk of high-power shocks due to LCC-HVDC blocking. Existing centralized, single-measure approaches and the model-based emergency frequency control (EFC) methods are facing tremendous challenges. To fill this gap, this paper proposes a frequency emergency control method for renewables-rich grids that coordinates LCC-HVDC emergency power support and load shedding. The coordination of the two stabilization measures is modeled as a decentralized partially observable Markov decision process, and a multi-agent reinforcement learning (MADRL) approach is employed to optimize the power of measures for each stabilization control substation. An MADRL algorithm that integrates the system frequency response model is developed to overcome the problem of inaccurate evaluation of centralized action value in the MADRL algorithm used for EFC. This approach allows for the global optimization of the distributed coordination strategy through centralized training. The effectiveness and adaptability of the proposed method are demonstrated through simulations of the modified IEEE 39-bus system. © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Improved deep deterministic policy gradient algorithm based on prioritized sampling

Improved deep deterministic policy gradient algorithm based ...

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Chinese Intelligent systems Conference, CISC 2018

作者： Zhang, HaoYu Xiong, Kai Bai, Jie Science and Technology on Space Intelligent Control Laboratory Beijing Institute of Control Engineering Beijing100190 China Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and Applications Beijing Institute of Spacecraft System Engineering Beijing China

ISBN: (纸本)9789811322877

Deep reinforcement learning tends to have low sampling efficiency, and prioritized sampling algorithm can improve the sampling efficiency to a certain extent. The prioritized sampling algorithm can be used in deep deterministic policy gradient algorithm, and a small sample sorting method is proposed to solve the problem of high complexity of the common prioritized sampling algorithm. simulation experiments prove that the improved deep deterministic policy gradient algorithm improves the sampling efficiency and the training performance is better. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Reinforcement learning

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Sources and Emissions of Marine-Derived Halocarbons Over the Coastal and Open Ocean of the Northwest Pacific

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Journal of Geophysical Research: Atmospheres 2025年第11期130卷

作者： Shan-Shan Liu Yi-Ning Bai Jie Ni Zhen He Gui-Peng Yang Frontiers Science Center for Deep Ocean Multispheres and Earth System and Key Laboratory of Marine Chemistry Theory and Technology Ministry of Education Ocean University of China Qingdao China Key Laboratory of Marine Ecology and Environmental Sciences Institute of Oceanology Chinese Academy of Sciences Qingdao China College of Oceanography and Space Informatics China University of Petroleum Qingdao China Laboratory for Marine Ecology and Environmental Science Qingdao Marine Science and Technology Center Qingdao China Institute of Marine Chemistry Ocean University of China Qingdao China

The spatial variability and sources of atmospheric methyl halides (CH 3 Cl, CH 3 Br, and CH 3 I) over the northwest Pacific and its marginal seas—the East China Sea (ECS) and South China Sea (SCS)—were comprehensively characterized from 2017 to 2020. This study represents the first large-scale investigation of these compounds across coastal and open-ocean environments. Atmospheric CH 3 Cl and CH 3 Br concentrations were substantially elevated in the ECS and SCS, exceeding Northern Hemisphere background levels by 12%–28%, whereas concentration enhancements over the northwest Pacific were 3%–5%. Elevated CH 3 Cl and CH 3 Br concentrations were attributed to long-range terrestrial atmospheric transport from China, Korea, Japan, and the Philippines, as well as localized phytoplankton-driven production in nutrient-rich coastal waters. In contrast, CH 3 I concentrations were primarily controlled by photochemical processes mediated by dissolved organic carbon (DOC), with elevated levels being observed in the oligotrophic subtropical northwest Pacific (10°–30°N). Multiple regression analyses revealed that CH 3 Cl and CH 3 Br distributions were mainly influenced by terrestrial transport in coastal areas, whereas oceanic processes dominated their distributions in the open ocean. CH 3 I distributions primarily reflected oceanic photochemical production, with marginal terrestrial influence limited to the ECS. The production rates of CH 3 Br and CH 3 Cl in the ECS and SCS, respectively, were 2–3 times higher than those in the northwest Pacific, consistent with enhanced biological activity. Sea-to-air flux estimates demonstrated that the northwest Pacific and its marginal seas acted as net sources of all three methyl halides, with fluxes generally exceeding global averages by approximately 1.5–48 times. These findings underscore the importance of the study area to the atmospheric methyl halides budget. Terrestrial transport and localized bioproduction enhanced atmospheric CH 3 Cl

关键词： methyl halides China seas northwest Pacific source

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Effect of potential HONO sources on peroxyacetyl nitrate(PAN)formation in eastern China in winter

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Journal of Environmental sciences 2020年第8期32卷 81-87页

作者： Jingwei Zhang Yitian Guo Yu Qu Yong Chen Ruipeng Yu Chaoyang Xue Rui Yang Qiang Zhang Xingang Liu Yujing Mu Jing Wang Can Ye Haihan Zhao Qiangqiang Sun Ziwen Wang Junling An State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry(LAPC) Institute of Atmospheric Physics(IAP)Chinese Academy of SciencesBeijing 100029China College of Earth Science University of Chinese Academy of SciencesBeijing 100049China College of Resources and Environmental Sciences China Agricultural UniversityBeijing 100193China Research Center for Eco-Environmental Sciences Chinese Academy of SciencesBeijing 100085China Guangzhou Meteorological Observatory Guangzhou 511430China Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System ScienceTsinghua UniversityBeijingChina Collaborative Innovation Center for Regional Environmental Quality BeijingChina State Key Laboratory of Water Environment Simulation School of EnvironmentBeijing Normal UniversityBeijing 100875China Center for Excellence in Urban Atmospheric Environment Institute of Urban EnvironmentChinese Academy of SciencesXiamen 361021China College of Land Science and Technology China Agricultural UniversityBeijing 100193China Qinghai Climate Center Qinghai Meteorological BureauXiningQinghai 810001China

As an important secondary photochemical pollutant,peroxyacetyl nitrate(PAN)has been studied over decades,yet its simulations usually underestimate the corresponding observations,especially in polluted *** observations in north China found unusually high concentrations of PAN during wintertime heavy haze events,but the current model still cannot reproduce the observations,and researchers speculated that nitrous acid(HONO)played a key role in PAN *** the first time we systematically assessed the impact of potential HONO sources on PAN formation mechanisms in eastern China using the Weather Research and Forecasting/Chemistry(WRF-Chem)model in February of *** results showed that the potential HONO sources significantly improved the PAN simulations,remarkably accelerated the RO x(sum of hydroxyl,hydroperoxyl,and organic peroxy radicals)cycles,and resulted in 80%–150%enhancements of PAN near the ground in the coastal areas of eastern China and 10%–50%enhancements in the areas around 35–40°N within 3 km during a heavy haze *** direct precursors of PAN were aldehyde and methylglyoxal,and the primary precursors of PAN were alkenes with C>3,xylenes,propene and *** above results suggest that the potential HONO sources should be considered in regional and global chemical transport models when conducting PAN studies.

关键词： Nitrous acid(HONO) Peroxyacetyl nitrate(PAN) WRF-Chem model Winter haze RO x cycles

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Entire Chains simulation system for Optical Remote Sensing Imaging

Entire Chains Simulation System for Optical Remote Sensing I...

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2019 IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2019

作者： Zhao, Mengyuan Ma, Xiaoshan Meng, Xin Yang, Zhen Peng, Xiaodong Key Laboratory of Electronics and Information Technology for Space System National Space Science Center Chinese Academy of Sciences Beijing China Yuquan Road Shijingshan District Beijing China

ISBN: (纸本)9781728123455

The optical remote sensing imaging simulation plays vital roles in the improvement of imaging system design and the evaluation of image processing algorithms. By combing quantitative radiation and computer graphics, an entire chains simulation system is designed for optical remote sensing. Analyzing the imaging process, physical models for entire image chains are built, including BRDF model, radiative transfer model, imaging system model and platform model. Based on scene data measurement, physical properties correlation and scene management, a three-dimensional (3D) digital scene is constructed. The simulation software is developed on the basis of image chains models. A material spectral database and an atmospheric parameter database compose the database system together. The software is divided into three modules: visibility calculation module, radiance calculation module and imaging module. The simulation images for different solar zenith angles and observing zenith angles are obtained. The results prove the accuracy of the simulation models and software. © 2019 IEEE.

关键词： Optical remote sensing

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Large-Scale Spatiotemporal Deep Learning Predicting Residential Indoor Pm2.5 Concentration Using a Bayesian Neural Network Model

SSRN

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SSRN 2023年

作者： Dai, Hui Liu, Yumeng Wang, Jianghao Ren, Jun Gao, Yao Dong, Zhaomin Zhao, Bin Department of Building Science School of Architecture Tsinghua University Beijing100084 China Beijing Key Laboratory of Indoor Air Quality Evaluation and Control Tsinghua University Beijing100084 China School of Space and Environment Beihang University Beijing100191 China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China Sustainable Urbanization Lab Department of Urban Studies and Planning Massachusetts Institute of Technology CambridgeMA02139 United States Shenzhen Institute of Building Research Co. Ltd China

Indoor PM2.5 pollution is one of the leading causes of death and disease worldwide. As monitoring indoor PM2.5 concentrations on a large scale is challenging, it is urgent to assess population-level exposure and related health risks to develop an easy-to-use and generalized model to predict indoor PM2.5 concentrations and spatiotemporal variations at the global level. Existing machine learning models of indoor PM2.5 are prone to deliver single-point predictions, and their input strategies are not widely applicable. Here, we developed a Bayesian neural network (BNN) model for predicting the distribution of indoor daily average PM2.5 concentration based on multiple data sources available from nationwide comprehensive sensor-monitoring records in China. The BNN model showed good performance with a 10-fold cross-validation R2 of 0.70, mean-absolute-error of 9.45 μg/m3, root-mean-square error of 13.3 μg/m3, and 95% prediction interval coverage of 85%. Our modeled population-weighted annual indoor PM2.5 concentration for China in 2019 was 22.8 μg/m3, far exceeding the WHO standard. This model can be applied to predict indoor PM2.5 concentrations on a large spatiotemporal scale, making it valuable for assessing and managing indoor air pollution-related health risks. © 2023, The Authors. All rights reserved.

关键词： Health risks

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