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Significant effect of ordered micro-domain on cell boundary phase distribution and demagnetization curve squareness of Sm_(2)Co_(17)-type magnet

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Rare Metals 2024年第4期43卷 1724-1735页

作者： Zhuang Liu Hai-Chen Wu Chao-Yue zhang Chao-Qun Zhu Guo-Xin Chen Huan-ming Lu Ren-jie Chen Aru Yan CISRI&NIMTE Joint Innovation Center for Rare Earth Permanent Magnets Ningbo Institute of Materials Technology and EngineeringChinese Academy of SciencesNingbo315201China University of Chinese Academy of Sciences Beijing100049China Analytical Center Ningbo Institute of Materials Technology and EngineeringChinese Academy of SciencesNingbo315201China

Cu-rich cell boundary phase is difficult to precipitate evenly,resulting in a generally poor demagnetization curve squareness for Fe-rich Sm_(2)Co_(17)-type magnet,which is a key factor limiting the further improvement of magnetic energy *** this study,we report that nanoscale strip-like ordered micro-domains distributed in1:7H disordered matrix phase of the solid solution precursor is a new factor significantly affecting the precipitation and distribution of the cell boundary *** strip-like and continuous micro-twin structure with twin boundaries neatly perpendicular to the C-axis is observed after sintering *** solution treatment,sequential and long strip-like micro-twins gradually transform into disordered state along the basal plane,forming narrow disordered 1:7H(TbCu_(7)-type structure)phase between the separated strip-like ordered *** disordering transformation takes place via broken down of the long strip-like ordered micro-domains,which is accomplished by narrowing along the width direction followed by reduction of the ***,a new model revealing the effect of the ordered micro-domains on the formation of the cell boundary phase is *** boundaries enriched in Cu have already existed in the precursor with long strip-like ordered ***,the Cu-rich cell boundary phase acting as strong pinning centers cannot be precipitated homogeneously and distributed continuously after aging,resulting in a poor demagnetization curve squareness of Sm_(2)Co_(17)-type *** results indicate that significant broken down of the nanoscale ordered micro-domains in solution precursor is the key factor improving the distribution of cell boundary phase in Sm_(2)Co_(17)-type magnets.

关键词： Sm_(2)Co_(17) magnets Solution precursor Ordered micro-domains Disordering transformation Cellular structure

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Deciphering and integrating invariants for neural operator learning with various physical mechanisms

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National Science Review 2024年第4期11卷 111-122页

作者： Rui zhang Qi Meng Zhi-ming Ma Academy of Mathematics and Systems Science Chinese Academy of Sciences(CAS) Microsoft Research

Neural operators have been explored as surrogate models for simulating physical systems to overcome the limitations of traditional partial differential equation(PDE) solvers. However, most existing operator learning methods assume that the data originate from a single physical mechanism, limiting their applicability and performance in more realistic scenarios. To this end, we propose the physical invariant attention neural operator(PIANO) to decipher and integrate the physical invariants for operator learning from the PDE series with various physical mechanisms. PIANO employs self-supervised learning to extract physical knowledge and attention mechanisms to integrate them into dynamic convolutional *** to existing techniques, PIANO can reduce the relative error by 13.6%–82.2% on PDE forecasting tasks across varying coefficients, forces or boundary conditions. Additionally, varied downstream tasks reveal that the PI embeddings deciphered by PIANO align well with the underlying invariants in the PDE systems, verifying the physical significance of PIANO.

关键词： neural operator PDE solver contrastive learning physical invariants

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Molecular insights into oil detachment from hydrophobic quartz surfaces in clay-hosted nanopores during steam-surfactant co-injection

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Petroleum Science 2024年第4期21卷 2457-2468页

作者： Ben-jie-ming Liu Xuan-Tong Lei Mohammadali Ahmadi zhangxin Chen Department of Chemical and Petroleum Engineering University of Calgary Eastern Institute of Technology

Thermal recovery techniques for producing oil sands have substantial environmental impacts. Surfactants can efficiently improve thermal bitumen recovery and reduce the required amount of steam. Such a technique requires solid knowledge about the interaction mechanism between surfactants, bitumen,water, and rock at the nanoscale level. In particular, oil sands ores have extremely complex mineralogy as they contain many clay minerals(montmorillonite, illite, kaolinite). In this study, molecular dynamics simulation is carried out to elucidate the unclear mechanisms of clay minerals contributing to the bitumen recovery under a steam-anionic surfactant co-injection process. We found that the clay content significantly influenced an oil detachment process from hydrophobic quartz surfaces. Results reveal that the presence of montmorillonite, illite, and the siloxane surface of kaolinite in nanopores can enhance the oil detachment process from the hydrophobic surfaces because surfactant molecules have a stronger tendency to interact with bitumen and quartz. Conversely, the gibbsite surfaces of kaolinite curb the oil detachment process. Through interaction energy analysis, the siloxane surfaces of kaolinite result in the most straightforward oil detachment process. In addition, we found that the clay type presented in nanopores affected the wettability of the quartz surfaces. The quartz surfaces associated with the gibbsite surfaces of kaolinite show the strongest hydrophilicity. By comparing previous experimental findings with the results of molecular dynamics(MD) simulations, we observed consistent wetting characteristics. This alignment serves to validate the reliability of the simulation outcomes. The outcome of this paper makes up for the lack of knowledge of a surfactant-assisted bitumen recovery process and provides insights for further in-situ bitumen production engineering designs.

关键词： Clay minerals Bitumen Contact angle Interaction energy Surfactant Molecular dynamics

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End-to-end data-driven modeling framework for automated and trustworthy short-term building energy load forecasting

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Building Simulation 2024年第8期17卷 1419-1437页

作者： Chaobo zhang jie Lu Jiahua Huang Yang Zhao Department of the Built Environment Eindhoven University of TechnologyEindhovenThe Netherlands Energy Efficient Cities Initiative Department of EngineeringUniversity of CambridgeCambridgeUK Institute of Refrigeration and Cryogenics Zhejiang UniversityHangzhouChina Institute of Green Building and Engineering Design Zhejiang Province Institute of Architectural Design and Research Co.Ltd.HangzhouChina Key Laboratory of Clean Energy and Carbon Neutrality of Zhejiang Province Jiaxing Research InstituteZhejiang UniversityJiaxingChina

Conventional automated machine learning(AutoML)technologies fall short in preprocessing low-quality raw data and adapting to varying indoor and outdoor environments,leading to accuracy reduction in forecasting short-term building energy ***,their predictions are not transparent because of their black box ***,the building field currently lacks an AutoML framework capable of data quality enhancement,environment self-adaptation,and model *** address this research gap,an improved AutoML-based end-to-end data-driven modeling framework is *** optimization is applied by this framework to find an optimal data preprocessing process for quality improvement of raw *** bridges the gap where conventional AutoML technologies cannot automatically handle missing data and outliers.A sliding window-based model retraining strategy is utilized to achieve environment self-adaptation,contributing to the accuracy enhancement of AutoML ***,a local interpretable model-agnostic explanations-based approach is developed to interpret predictions made by the improved *** overcomes the poor interpretability of conventional AutoML *** performance of the improved framework in forecasting one-hour ahead cooling loads is evaluated using two-year operational data from a real *** is discovered that the accuracy of the improved framework increases by 4.24%–8.79%compared with four conventional frameworks for buildings with not only high-quality but also low-quality operational ***,it is demonstrated that the developed model interpretation approach can effectively explain the predictions of the improved *** improved framework offers a novel perspective on creating accurate and reliable AutoML frameworks tailored to building energy load prediction tasks and other similar tasks.

关键词： building energy load forecasting end-to-end data-driven modeling automated machine learning Bayesian optimization model retraining model interpretation

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The influence of stress-dependent overpotential on dendrite growth in all-solid-state battery with cracks

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Science China(Technological Sciences) 2024年第8期67卷 2493-2504页

作者： zhang ZhenHua zhang Yong LIU Chang HOU Xu WANG jie Department of Engineering Mechanics School of Aeronautics and AstronauticsZhejiang UniversityHangzhou 310027China Zhejiang Laboratory Hangzhou 311100China Key Laboratory of Soft Machines and Smart Devices of Zhejiang Province Zhejiang UniversityHangzhou 310027China School of Aerospace Engineering and Applied Mechanics Tongji UniversityShanghai 200092China Applied Mechanics and Structure Safety Key Laboratory of Sichuan Province School of Mechanics and Aerospace EngineeringSouthwest Jiaotong UniversityChengdu 610031China Department of Industrial and Systems Engineering The Hong Kong Polytechnic UniversityHong Kong 999077China

Dendrite growth is one of the main challenges in maintaining the service life of all-solid-state lithium-ion *** stress has been reported to significantly affect dendrite *** this study,to explain the effect of mechanical stress on electrochemical reactions in all-solid-state batteries,a modified phase-field model for dendrite growth is proposed by considering the stress-dependent *** growth under different mechanical loadings in an all-solid-state battery is investigated using the proposed *** with previous experimental results,the current result shows that compressive stress inhibits dendrite *** the stress concentration at the tips of processing-induced microcracks,the effects of the number and distribution of microcracks on dendrite growth are *** results show that the stress-concentration field induced at the tips of cracks or voids can change the morphology of dendrites and decrease their growth *** study provides a new perspective for explaining Li dendrite growth under mechanical stress and offers inspiration for prolonging the service life of all-solid-state batteries based on defect and stress regulation,which may be further realized in experiments by filling solid electrolytes with different types of nanofillers.

关键词： Butler–Volmer equation stress-dependent overpotential microcracks all-solid-state battery phase-field simulation

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NeurstrucEnergy:A bi-directional GNN model for energy prediction of neural networks in IoT

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Digital Communications and Networks 2024年第2期10卷 439-449页

作者： Chaopeng Guo Zhaojin Zhong Zexin zhang jie Song Software Campus Northeastern UniversityShenyang110000LiaoningChina

A significant demand rises for energy-efficient deep neural networks to support power-limited embedding devices with successful deep learning applications in IoT and edge computing *** accurate energy prediction approach is critical to provide measurement and lead optimization ***,the current energy prediction approaches lack accuracy and generalization ability due to the lack of research on the neural network structure and the excessive reliance on customized training *** paper presents a novel energy prediction model,*** treats neural networks as directed graphs and applies a bi-directional graph neural network training on a randomly generated dataset to extract structural features for energy *** has advantages over linear approaches because the bi-directional graph neural network collects structural features from each layer's parents and *** results show that NeurstrucEnergy establishes state-of-the-art results with mean absolute percentage error of 2.60%.We also evaluate NeurstrucEnergy in a randomly generated dataset,achieving the mean absolute percentage error of 4.83%over 10 typical convolutional neural networks in recent years and 7 efficient convolutional neural networks created by neural architecture *** code is available at https://***/NEUSoftGreenAI/***.

关键词： Internet of things Neural network energy prediction Graph neural networks Graph structure embedding Multi-head attention

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Novel high-safety aeroengine performance predictive control method based on adaptive tracking weight

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Chinese Journal of Aeronautics 2024年第7期37卷 352-374页

作者： Qian CHEN Hanlin SHENG jie zhang Jiacheng LI College of Energy and Power Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China College of Automation Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China

Increasing attention has been attracted to the dynamic performance and safety of advanced performance predictive control systems of the next-generation *** latest research demonstrates that Subspace-based Improved Model Predictive Control(SIMPC)can overcome the difficulty in solving the predictive model in MPC/NMPC ***,applying constant design parameters cannot maintain consistent control effects in all ***,the designed system relies too much on sensor-measured data,and thus it is difficult to thoroughly validate the safety of the system because of its high *** means that any potential hardware/software faults will endanger the ***,this paper first presents a novel nonlinear mapping relationship to adaptively tune the tracking weight online with the change of Power Lever Angle(PLA)and real-time relative tracking ***,without introducing additional design parameters,an Adaptive Tracking Weight-based SIMPC(ATW-SIMPC)controller is designed to improve the control performance in all operating states ***,a Primary/Backup Hybrid Control(PBHC)strategy with the ATW-SIMPC controller as the primary system and the traditional speed(Nf)controller as the backup system is proposed to ensure *** designed affiliated switching controller and the real-time monitor therein can be used to realize reasonable and smooth switching between primary/backup systems,so as to avoid bump *** PBHC system switches to the Nf controller when the ATW-SIMPC controller is wrong because of potential hardware/software faults;otherwise,the ATW-SIMPC controller keeps acting on the *** main results prove that the ATW-SIMPC controller with the optimal nonlinear mapping relationship,compared with the existing SIMPC controller,uplifts the dynamic control performance by 32%and reduces overshoots to an allowable limit,resulting in a better control effect in full *** comparison results consistently i

关键词： Aeroengine Model predictive control Hybrid system Adaptive weight Safety

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Visible Light-Induced[3+2]Annulation Reaction of Alkenes with Vinyl Azides:Direct Synthesis of Functionalized Pyrroles

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Chinese Journal of Chemistry 2024年第2期42卷 151-156页

作者： ming Yang Xin-Yu Wang jie Wang Yu-Long Zhao Jilin Province Key Laboratory of Organic Functional Molecular Design&Synthesis Faculty of ChemistryNortheast Normal UniversityChangchunJilin 130024China

A photocatalytic[3+2]annulation of alkenes with vinyl azides was developed under irradiation by visible light in the presence of organic dye *** substrate scope and high functional group tolerance were demonstrated by more than 50 *** reaction provides a novel and efficient method for the synthesis of polyfunctionalized pyrroles under very mild metal-free conditions without other additives.

关键词： Photocatalysis Vinyl azides Alkenes Pyrroles Annulation Radical reactions

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A deep learning framework for suppressing prestack seismic random noise without noise-free labels

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Energy Geoscience 2024年第3期5卷 261-274页

作者： Han Wang jie zhang Petroleum Exploration and Production Research Institute SINOPECBeijing100083China Geophysical Research Institute School of Earth and Space SciencesUniversity of Science and Technology of ChinaHefeiAnhui230026China

Random noise attenuation is significant in seismic data *** deep learning-based denoising methods have been widely developed and applied in recent *** practice,it is often time-consuming and laborious to obtain noise-free data for supervised ***,we propose a novel deep learning framework to denoise prestack seismic data without clean labels,which trains a high-resolution residual neural network(SRResnet)with noisy data for input and the same valid data with different noise for *** valid signals in noisy sample pairs are spatially correlated and random noise is spatially independent and unpredictable,the model can learn the features of valid data while suppressing random *** data targets are generated by a simple conventional method without fine-tuning *** initial estimates allow signal or noise leakage as the network does not require clean *** Monte Carlo strategy is applied to select training patches for increasing valid patches and expanding training *** learning is used to improve the generalization of real data *** synthetic and real data tests perform better than the commonly used state-of-the-art denoising methods.

关键词： Data processing Denoising Signal processing Seismics Deep learning

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A Multi-Channel CMOS Analog Front-End Interface IC with 157.8 d B Current Detection Dynamic Range

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Chinese Journal of Electronics 2024年第2期33卷 393-402页

作者： Kunyu WANG Wenjing XU Chengbin zhang Yanjun YANG Man-Kay LAW Li ZHOU jie CHEN ming CHEN Institute of Microelectronics of the Chinese Academy of Science University of Chinese Academy of Sciences Zunyi Normal University Institute of Microelectronics and Dept.of ECE FST University of Macau

A high dynamic range and low-noise CMOS(complementary metal-oxide-semiconductor transistor)front-end interface integrated circuit(IC) with multi-channel detection is presented in this paper. Two different current detection channels, composed of a trans-impedance amplifier(TIA) and an integrator-differentiator TIA, are used to boost the current detection range. A capacitance-coupled instrument amplifier(CCIA) is also included to realize high precision voltage detection. A fourth-order sigma-delta modulator using a second-order loop filter and a second-order noise shaping integral quantizer is adopted to realize effective number of bits above 16 bits. The presented interface IC is implemented in 0.18-μm CMOS process with supply voltage of 3.3 V, and a proto-type electrochemical sensor platform with miniaturized sensor array is developed to verify the functionality of the interface IC. Measurement results indicate that the designed interface IC achieves 157.8 d B current detection dynamic range, and the measured input-referred current noise and voltage noise floor are 1.04 p A and 58.4 n V within 10 k Hz integration bandwidths,respectively.

关键词： Integrated circuits Sigma-delta modulation Semiconductor device measurement Voltage measurement Current measurement Noise Bandwidth

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