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Study on Static Deflection Model of MEMS Capacitive Microwave power Sensors

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Chinese Journal of Electronics 2024年第5期33卷 1188-1195页

作者： ye jin Debo wang College of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology) Nanjing University of Posts and Telecommunications College of Integrated Circuit Science and Engineering Nanjing University of Posts and Telecommunications

In this paper, a static deflection model of MEMS cantilever beam is proposed, which can better study the force deformation of micro-electro-mechanical system(MEMS) cantilever beam and the output characteristics of capacitive microwave power sensor. The deflection curve is used to describe the deformation of the cantilever beam and then the overload power and sensitivity of this power sensor are derived. It is found that the overload power decreases with the beam length, and increases with the initial height of beam. The sensitivity increases with the beam length, and has a linear growth relationship with the measuring electrode width. A MEMS dual-channel microwave power sensor is designed, fabricated and measured. At a microwave signal frequency of 10 GHz, the sensitivity of the sensor is measured to be 0.11 V/W for the thermoelectric detection channel and 65.17 fF/W for the capacitive detection channel. The sensitivity calculated by the lumped model is 92.93 fF/W, that by the pivot model is 50.88 fF/W,and that by the deflection model proposed in this work is 75.21 fF/W. Therefore, the theoretical result of the static deflection model is more consistent with the measured result and has better accuracy than the traditional lumped model and pivot model.

关键词： Microwave measurement Micromechanical devices Microelectromechanical systems Sensitivity Voltage measurement Accuracy Deformation Micro-electro-mechanical system (MEMS) cantilever beam Static deflection model power sensor Overload power

A novel model of the carbon cycle in the Cambrian ocean

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Science China Earth Sciences 2024年第6期67卷 1759-1778页

作者： Huajian wang Shuichang ZHAng Xiaomei wang jin SU Yuntao ye Yuke LIU Key Laboratory of petroleum Geochemistry Research Institute of Petroleum Exploration and DevelopmentChina National Petroleum CorporationBeijing 100083China Key Laboratory of Orogenic Belts and Crustal Evolution School of Earth and Space SciencesPeking UniversityBeijing 100871China

The classic model of the carbon cycle suggests that the extensive burial of ^(12)C-enriched organic carbon leads to a positive carbon isotope(δ^(13)C)excursion(CIE),while massive oxidation of organic carbon results in a negative ***,global events such as the BAsal Cambrian Carbon isotope Excursion(BACE)and the Steptoean positive Carbon Isotope Excursion(SpICE)are global negative and positive δ^(13)C excursions,respectively,and they also exhibit significant organic carbon burial anomalies,displaying decoupling between carbon isotope anomalies and organic carbon *** on the analyses of the Cambrian carbon cycle and paleoceanographic evolution records from well Tadong2 in the Tarim Basin,we propose a novel model of the carbon cycle in the Cambrian ocean that incorporates oceanic dissolved organic carbon(DOC).Our findings are as follows.(1)The Cambrian ocean maintained substantial DOC reservoirs,which were regulated by ocean currents and paleo-redox conditions and exerted significant influence on the oceanic carbon cycle.(2)The oxidation of the oceanic DOC reservoirs during the early Cambrian led to the BACE and the Asian phenomenon of the Cambrian petroleum systems,while the expansion of the oceanic DOC reservoirs during the SpICE resulted in a global positive δ^(13)C excursion and the absence of significant organic carbon burial.(3)The deep-basin sedimentary environment in the eastern depression of the Tarim Basin may have fostered the development of organic-rich black shales during the Furongian Series,corresponding to organic carbon burial during the SpICE and representing potential prospects for ultra-deep oil and gas *** research should focus on the formation mechanism,reserve scale,and influencing factors of the oceanic DOC reservoirs,as well as their resource and environmental *** is expected that new breakthroughs will be made in the fields of Earth system science and oil and gas exploration.

关键词： Carbon isotope Organic carbon BACE SpICE Dissolved organic carbon Climate change Black shale

维普期刊数据库

Advances of machine learning in materials science: Ideas and techniques

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Frontiers of physics 2024年第1期19卷 155-194页

作者： Sue Sin Chong Yi Sheng ng Hui-Qiong wang jin-Cheng Zheng Department of New Energy Science and Engineering Xiamen University MalaysiaSepang 43900Malaysia Engineering Research Center of Micro-nano Optoelectronic Materials and Devices Ministry of EducationFujian Key Laboratory of Semiconductor Materials and ApplicationsCI Center for OSEDand Department of PhysicsXiamen UniversityXiamen 361005China

In this big data era, the use of large dataset in conjunction with machine learning (ML) has been increasingly popular in both industry and academia. In recent times, the field of materials science is also undergoing a big data revolution, with large database and repositories appearing everywhere. Traditionally, materials science is a trial-and-error field, in both the computational and experimental departments. With the advent of machine learning-based techniques, there has been a paradigm shift: materials can now be screened quickly using ML models and even generated based on materials with similar properties;ML has also quietly infiltrated many sub-disciplinary under materials science. However, ML remains relatively new to the field and is expanding its wing quickly. There are a plethora of readily-available big data architectures and abundance of ML models and software;The call to integrate all these elements in a comprehensive research procedure is becoming an important direction of material science research. In this review, we attempt to provide an introduction and reference of ML to materials scientists, covering as much as possible the commonly used methods and applications, and discussing the future possibilities.

关键词： machine learning materials science

progress in systematics and biogeography of Orchidaceae

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plant Diversity 2024年第4期46卷 425-434页

作者： Yajun wang Hanchen wang Chao ye Zhip.ng wang Chongbo Ma Dongliang Lin Xiaohua jin State Key Laboratory of plant Diversity and Specialty Crops Institute of Botanythe Chinese Academy of SciencesBeijing 100093China University of Chinese Academy of Sciences Beijing 100093China China National Botanical Garden Beijing 100093China

Orchidaceae are one of the largest families of angiosperms in terms of species *** the last decade,numerous studies have delved into reconstructing the phylogenetic framework of Orchidaceae,leveraging data from plastid,mitochondrial and nuclear *** studies have provided new insights into the systematics,diversification and biogeography of Orchidaceae,establishing a robust foundation for future ***,pronounced controversies persist regarding the precise placement of certain lineages within these phylogenetic *** address these discrepancies and deepen our understanding of the phylogenetic structure of Orchidaceae,we provide a comprehensive overview and analysis of phylogenetic studies focusing on contentious groups within Orchidaceae since 2015,delving into discussions on the underlying reasons for observed topological *** also provide a novel phylogenetic framework at the subtribal ***,we examine the tempo and mode underlying orchid species diversity from the perspective of historical biogeography,highlighting factors contributing to extensive ***,we delineate avenues for future research aimed at enhancing our understanding of Orchidaceae phylogeny and diversity.

关键词： Diversification Orchid phylogenetics phylogenetic conflicts Biogeography Taxonomy

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prediction of fracture initiation in cohesive soils based on data mining modelling and large-scale laboratory verification

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Underground Space 2024年第6期19卷 279-300页

作者： Weip.ng Luo Dajun Yuan Yannick Choy Hing ng Dalong jin p.ng Lu Teng wang Department of Geotechnical Engineering College of Civil EngineeringTongji UniversityShanghai 200092China Key Laboratory of the Ministry of Education for Urban Underground Engineering Beijing Jiaotong UniversityBeijing 100044China Department of Civil and Environmental Engineering National University of SingaporeSingapore 119077Singapore

Many empirical and analytical methods have been proposed to predict fracturing pressure in cohesive *** of them take into account three to four specific influencing factors and rely on the assumption of a failure *** this study,a novel data-mining approach based on the XGBoost algorithm is investigated for predicting fracture initiation in cohesive *** has the advantage of handling multiple influencing factors simultaneously,without pre-determining a failure mode.A dataset of 416 samples consisting of 14 distinct features was herein collected from past studies,and used for develop.ng a regressor and a classifier model for fracturing pressure prediction and failure mode classification *** results show that the intrinsic characteristics of the soil govern the failure mode while the fracturing pressure is more sensitive to the stress *** XGBoost-based model was also tested against conventional approaches,as well as a similar machine learning algorithm namely random forest ***,several large-scale triaxial fracturing tests and an in-situ case study were carried out to further verify the generalization ability and applicability of the proposed data mining approach,and the results indicate a superior performance of the XGBoost model.

关键词： Fracturing pressure Failure mode Cohesive soil Data-mining Large-scale tests

Deep learning for joint channel estimation and feedback in massive MIMO systems

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Digital Communications and Networks 2024年第1期10卷 83-93页

作者： Jiajia Guo Tong Chen Shi jin Geoffrey ye Li Xin wang Xiaolin Hou National Mobile Communications Research Laboratory Southeast UniversityNanjing210096China Department of Electrical and Electronic Engineering Imperial College LondonLondonSW72AZUK DOCOMO Beijing Communications Laboratories Co. LtdBeijingChina

The great potentials of massive Multiple-Input Multiple-Output(MIMO)in Frequency Division Duplex(FDD)mode can be fully exploited when the downlink Channel State Information(CSI)is available at base ***,the accurate CsI is difficult to obtain due to the large amount of feedback overhead caused by massive *** this paper,we propose a deep learning based joint channel estimation and feedback framework,which comprehensively realizes the estimation,compression,and reconstruction of downlink channels in FDD massive MIMO *** networks are constructed to perform estimation and feedback explicitly and *** explicit network adopts a multi-Signal-to-Noise-Ratios(SNRs)technique to obtain a single trained channel estimation subnet that works well with different SNRs and employs a deep residual network to reconstruct the channels,while the implicit network directly compresses pilots and sends them back to reduce network *** module is also designed to generate data-bearing *** results show that the two proposed networks exhibit excellent performance of reconstruction and are robust to different environments and quantization errors.

关键词： Channel estimation CSI feedback Deep learning Massive MIMO FDD

Investigation of the block toppling evolution of a layered model slope by centrifuge test and discrete element modeling

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Journal of Rock Mechanics and Geotechnical Engineering 2024年第1期16卷 112-122页

作者： Leilei jin Hongkai Dong Fei ye Yufeng Wei Jianfeng Liu Changkui wang State Key Laboratory of Hydraulic and Mountain River Engineering College of Water Resource and HydropowerSichuan UniversityChengdu610065China State Key Laboratory of Geohazard prevention and Geoenvironment protection Chengdu University of TechnologyChengdu610059China

primary toppling usually occurs in layered rock slopes with large anti-dip *** this paper,the block toppling evolution was explored using a large-scale centrifuge *** block column in the layered model slope was made of cement *** artificial cracks perpendicular to the block column were *** gages,displacement gages,and high-speed camera measurements were employed to monitor the deformation and failure processes of the model *** centrifuge test results show that the block toppling evolution can be divided into seven stages,*** compression,formation of major tensile crack,reverse bending of the block column,closure of major tensile crack,strong bending of the block column,formation of failure zone,and complete *** toppling is characterized by sudden large deformation and occurs in *** wedge-shaped cracks in the model incline towards the *** observations show that block toppling is mainly caused by bending failure rather than by shear *** tensile strength also plays a key factor in the evolution of block *** simulation results from discrete element method(DEM)is in line with the testing *** stress exists at the backside of rock column during toppling *** concentration results in the fragmented rock column and its degree is the most significant at the slope toe.

关键词： Block toppling Centrifuge Anti-dip slope Failure mechanism Discrete element method

A method for determining the complex refractive index dispersion of absorbing materials without thickness information

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Optoelectronics Letters 2024年第11期20卷 676-680页

作者： DEng Zhichao MEI Jianchun wang jin ye Qing TIAN Jianguo Key Laboratory of Weak-Light Nonlinear photonics Ministry of Education School of Physics and TEDA Applied Physics Nankai University

The complex refractive index dispersion(CRID) of absorbing materials is very important in many fields, especially in printing industry and medical research. However, due to their strong absorbing, CRID determination i... 详细信息

关键词： A

Anchoring Ru clusters to highly defective N-doped carbon nanotubes via a thermal-shock strategy for stable industrial hydrogen evolution

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Nano Research 2024年第6期17卷 5261-5269页

作者： Zhiming Li Xinyu Li Haiqing Ma Chenliang ye Hongan Yu Long Nie Meng Zheng jin wang College of Materials Science and Engineering Shenzhen UniversityShenzhen 518060China School of Chemistry and Materials Science Anhui Normal UniversityWuhu 241002China Department of power Engineering North China Electric Power UniversityBaoding 071003China

Non-pt or low-pt catalysts capable for stable generation of hydrogen via water electrolysis at an industrial level of current density are highly *** of strong metal-support connection is beneficial to improve the performance stability of *** we employed highly defective N-doped carbon nanotubes(d-N-CNT)as the support to achieve uniform and firm anchoring of Ru clusters(~1.9 nm)via a thermal-shock *** as-prepared Ru/d-N-CNT catalyst shows excellent catalytic activity for hydrogen evolution reaction(HER)in alkaline media and requires an overpotential(ƞ)of 12 mV at 10 mA·cm^(−2)and 116 mV at 200 mA·cm^(−2)with a Ru loading of 0.025 mg·cm^(−2).Impressively,Ru/d-N-CNT presents robust stability for HER at both low current density(stable for at least 1000 h at 10 mA·cm^(−2))and the industrial level of current density(stable for at least 100 h at 1000 mA·cm^(−2)),remarkably outperforming commercial pt/C and Ru/*** highly defective nature of the N-CNT support endowed the as-prepared Ru/d-N-CNT catalyst with strong metal-support adhesion that efficiently suppressed agglomeration as well as obscission of Ru ***,the rich defects increased the surface energy of the N-CNT support and resulted in improved hydrophilicity as evidenced by the liquid contact angle measurement and the bubble evolution process,which also played an important role in stabilizing the HER performance especially at large current density.

关键词： thermal-shock method hydrogen evolution reaction large-current-density Ru cluster