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Applications of MXenes in human-like sensors and actuators

Nano research 2023年第4期16卷 5767-5795页

作者： Jinbo Pang Songang Peng Chongyang Hou Xiao Wang Ting Wang Yu Cao Weijia Zhou Ding Sun Kai Wang Mark H.Rümmeli Gianaurelio Cuniberti Hong Liu Institute for Advanced Interdisciplinary Research(iAIR) Collaborative Innovation Center of Technology and Equipment for Biological Diagnosis and Therapy in Universities of ShandongUniversity of JinanJinan 250022China Institute for Materials Science and Max Bergmann Center of Biomaterials Technische Universität DresdenDresden 01069Germany Center for Advancing Electronics Dresden Technische Universität DresdenDresden 01069Germany Dresden Center for Computational Materials Science Technische Universität DresdenDresden 01062Germany Dresden Center for Intelligent Materials(GCL DCIM) Technische Universität DresdenDresden 01062Germany High-Frequency High-Voltage Device and Integrated Circuits R&D Center Institute of MicroelectronicsChinese Academy of SciencesBeijing 100029China Key Laboratory of Microelectronic Devices&Integrated Technology Institute of MicroelectronicsChinese Academy of SciencesBeijing 100029China Shenzhen Key Laboratory of Nanobiomechanics Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China State Key Laboratory of Biobased Material and Green Papermaking Qilu University of TechnologyShandong Academy of SciencesJinan 250353China School of Bioengineering Qilu University of TechnologyShandong Academy of ScienceJinan 250353China Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology(Ministry of Education) Northeast Electric Power UniversityJilin 132012China School of Electrical Engineering Northeast Electric Power UniversityJilin 132012China School of Electrical Engineering Weihai Innovation Research InstituteQingdao UniversityQingdao 266000China School of Electrical and Computer Engineering Jilin Jianzhu UniversityChangchun 130118China Institute for Complex Materials Leibniz Institute for Solid State and Materials Research Dresden(IFW Dresden)20 Helmholtz StrasseDresden 01069Germany College of Energy Soochow Institute for Energy and Materials Innovations Soochow UniversitySuzhou 215006China Key L

Human beings perceive the world through the senses of sight,hearing,smell,taste,touch,space,and *** first five senses are prerequisites for people to *** sensing organs upload information to the nervous systems,including the brain,for interpreting the surrounding ***,the brain sends commands to muscles reflexively to react to stimuli,including light,gas,chemicals,sound,and ***,as an emerging two-dimensional material,has been intensively adopted in the applications of various sensors and *** this review,we update the sensors to mimic five primary senses and actuators for stimulating muscles,which employ MXene-based film,membrane,and composite with other functional ***,a brief introduction is delivered for the structure,properties,and synthesis methods of ***,we feed the readers the recent reports on the MXene-derived image sensors as artificial retinas,gas sensors,chemical biosensors,acoustic devices,and tactile sensors for electronic ***,the actuators of MXene-based composite are ***,future opportunities are given to MXene research based on the requirements of artificial intelligence and humanoid robot,which may induce prospects in accompanying healthcare and biomedical engineering applications.

关键词： MXenes sensors actuators gas sensors biosensors tactile sensors sound sensors artificial retina

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A Robust Mpc Approach with controller Tuning for Close Following Operation of Virtually Coupled Train Set

SSRN

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SSRN 2022年

作者： Luo, Xiaolin Tang, Tao Yin, Jiateng Liu, Hongjie School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University Beijing100044 China

Virtual coupling, as an emerging concept in railways, expects successive trains in a virtually coupled train set (VCTS) to maintain a short following distance. However, this close following operation is still difficult to be achieved given uncertain resistances and nonlinear safety constraints. To solve this problem, this paper proposes a robust model predictive control (RMPC) approach for the close following operation of VCTS while satisfying a nonlinear safety constraint with relative braking principle. First, we construct a robust positively invariant set that bounds the tracking errors caused by uncertain resistances. Further, a semi-definite program-based controller tuning algorithm is proposed to reduce the following distance in the premise of the tightened constraint for robustly satisfying the nonlinear safety constraint. Then, by mathematically examining the future trajectories of successive trains, we create a terminal constraint set to ensure the recursive feasibility of the proposed RMPC. This closed-loop property guarantees the satisfaction of the safety constraint in any situation, even in the case of sudden deceleration of VCTS. Finally, numerical experiments are conducted to evaluate the following distance with respect to heterogeneous trains and verify the effectiveness of our approach. Experimental results demonstrate that the expected close following operation can be achieved while robustly satisfying the nonlinear safety constraint with uncertain resistances. Moreover, our approach further reduces the following distance in a VCTS by over 5\%, compared with existing research. © 2022, The Authors. All rights reserved.

关键词： Model predictive control

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DOA Estimation for Coherent Multipath Signals with Covariance Matrix Reconstruction in Hybrid Massive Circular Array 1

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5th International Conference on Electrical engineering and Information Technologies for Rail Transportation, EITRT 2021

作者： Wu, Xinmeng Sun, Xiaofang Ding, Jianwen Lu, Xiaotong Zhong, Zhangdui Frontiers Science Center for Smart High-Speed Railway System Beijing Jiaotong University Beijing100044 China School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China State Key Lab of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China National Research Center of Railway Safety Assessment Beijing Jiaotong University Beijing100044 China Beijing Engineering Research Center of High-Speed Railway Broadband Mobile Communications Beijing Jiaotong University Beijing100044 China Key Laboratory of Railway Industry of Broadband Mobile Information Communications Beiing Jiaotong University Beijing100044 China China State Railway Group Co. Ltd Beijing China

ISBN: (数字)9789811699092

ISBN: (纸本)9789811699085

Hybrid massive multiple-input multiple-output (MIMO) is regarded as a promising and economic technique to improve the spectral efficiency. However, regarding to the direction of arrival (DOA) estimation, the spatial covariance matrix cannot be obtained by conventional sample average algorithm in hybrid massive MIMO systems. Besides, the performance of DOA estimation can be affected dramatically by the multipath especially in railway scenarios. To address these issues, this paper proposes a two-dimensional (2-D) DOA estimation method based on covariance matrix reconstruction for coherent signals in hybrid massive MIMO systems, which combine the large circular antenna array with one-dimensional digital processing. We first carry out the coherent preprocessing by dividing the massive uniform-circular-arrays (UCAs) into four subarrays. Moreover, each submatrix is reconstructed and the arithmetic average of the reconstructed covariance matrices is calculated. Then, multiple signal classification (MUSIC) algorithm is introduced for the DOA estimation. Finally, the numerical results demonstrate that the proposed approach achieves a profound precision over the existing ones. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Spectrum efficiency

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The Seasonal Prediction of the Exceptional Yangtze River Rainfall in Summer 2020

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Advances in Atmospheric Sciences 2021年第12期38卷 2055-2066页

作者： Chaofan LI Riyu LU Nick DUNSTONE Adam ASCAIFE Philip EBETT Fei ZHENG Center for Monsoon System Research Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China State Key Laboratory of Numerical Modelling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China University of the Chinese Academy of Sciences Beijing 100029China Met Office Hadley Centre FitzRoy RoadExeter EX13PBUK College of Engineering Mathematics and Physical SciencesUniversity of ExeterExeterDevon EX44QFUK International Center for Climate and Environment Science Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China

During June and July of 2020,the Yangtze River basin suffered from extreme mei-yu rainfall and catastrophic *** study explores the seasonal predictability and associated dynamical causes for this extreme Yangtze River rainfall event,based on forecasts from the Met Office GloSea5 operational forecast *** forecasts successfully predicted above-average rainfall over the Yangtze River basin,which arose from the successful reproduction of the anomalous western North Pacific subtropical high(WNPSH).Our results indicate that both the Indian Ocean warm sea surface temperature(SST)and local WNP SST gradient were responsible for the westward extension of the WNPSH,and the forecasts captured these tropical signals *** explore extratropical drivers but find a large model spread among the forecast members regarding the meridional displacements of the East Asian mid-latitude westerly jet(EAJ).The forecast members with an evident southward displacement of the EAJ favored more extreme Yangtze River ***,the forecast Yangtze River rainfall anomaly was weaker compared to that was observed and no member showed such strong *** observations,the EAJ displayed an evident acceleration in summer 2020,which could lead to a significant wind convergence in the lower troposphere around the Yangtze River basin,and favor more mei-yu *** model forecast failed to satisfactorily reproduce these *** difference implies that the observed enhancement of the EAJ intensity gave a large boost to the Yangtze River rainfall,hindering a better forecast of the intensity of the event and disaster mitigation.

关键词： seasonal forecast Yangtze River rainfall western North Pacific subtropical high westerly jet

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Enhanced carbonate weathering and CO2 release in a typical karst watershed (Southwest China): Evidence from hydrochemical and multi-isotopic data

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Science of the Total Environment 2024年 957卷 177372页

作者： Liu, Lei Lan, Jiacheng Ren, Kun Huang, Mingzhi Mao, Ruitao Zhang, Xianwen Yue, Kunqian School of Karst Science / State Engineering Technology Institute for Karst Desertiffcation control Guizhou Normal University Guiyang550001 China Institute of Karst Geology Chinese Academy of Geological Sciences Key Laboratory of Karst Dynamics Ministry of Natural Resources & Guangxi Guilin541004 China International Research Centre on Karst under the Auspices of UNESCO National Center for International Research on Karst Dynamic System and Global Change Guilin541004 China Pingguo Guangxi Karst Ecosystem National Observation and Research Station Pingguo531406 China

Carbon sinks formed by carbonate weathering are a major component of the carbon sink deficit and play a significant role in the carbon cycle. However, exogenous acids (HNO3 and H2SO4) are also involved in carbonate weathering, complicating the overestimation of the formation of carbon sinks and fluxes. In this study, we analyzed the Hongjiadu karst underground river basin in Guizhou using isotopes (δ13C-DIC, δ34S-SO4, δ18O-SO4, δ18O-H2O, and δ2H-H2O) and hydrochemical methods to investigate carbon cycling characteristics and its influencing factors, and quantitatively evaluate the effects of HNO3 + H2SO4 on CO2 sink fluxes in the basin. The following results were obtained: (1) The nitrate in the watershed mainly comes from fertilizers, and the sulfate mainly comes from local coal seams. (2) The relationship between isotopes (δ13C-DIC, δ34S-SO4, δ18O-SO4, δ18O-H2O, and δ2H-H2O) and hydrochemicals (Ca2+, Mg2+, NO3–, HCO3–, and SO42–) indicates that H2CO3, HNO3, and H2SO4 are involved in carbonate rock dissolution. Most of the samples in the watershed, particularly the acid mine drainage water, were affected by acid rain and sulfide oxidation, resulting in increased δ13C-DIC values. Another portion of the springs was affected by soil CO2, resulting in decreased δ13C-DIC values. (3) Quantitative calculations showed that exogenous acids enhanced the weathering rate of carbonate rocks in groundwater by 67.24 % (dry season) and 76.31 % (wet season) while decreasing the carbon sink flux in groundwater by 55.31 % (dry season) and 66.64 % (wet season). Rainfall enhances the weathering of carbonate rocks. In the process, CO2 is released, increasing the carbon source. Our study highlights the influence of anthropogenic, natural, and hydrological changes on karst carbon sinks in mixed-pollution karst basins. This information is important for future studies on the carbonate weathering processes in karst regions. © 2024 Elsevier B.V.

关键词： Groundwater pollution

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Switch Machine Fault Diagnosis Method Based on Kalman Filter and Support Vector Machines 4th

Switch Machine Fault Diagnosis Method Based on Kalman Filter...

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4th International Conference on Electrical and Information Technologies for Rail Transportation, EITRT 2019

作者： Li, Xiang Qin, Yong Wang, Zhipeng Kan, Jiayu Zhang, Xiaofeng State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China National Research Center of System Engineering of Railway Intelligent Transport Beijing100081 China National Research Center of System Engineering of Railway Intelligent Transport Signal & Communication Research Institute Beijing100044 China

ISBN: (纸本)9789811528651

Switch machines are used for operating railway turnout;its error can cause delays, increase operating costs and may even lead to train accidents. Therefore, the fault diagnosis technology for the switch machine has received more and more attention. This paper proposes a fault diagnosis method based on the action current of switch machine. Firstly, the Kalman filter is used to preprocess the collected action current to reduce the influence of the unavoidable error of the measurement. In addition, we can further improve the accuracy of fault diagnosis by extracting the characteristics of the action current curve, like the maximum, minimum and average value, etc. Finally, we use DAG-SVMs to intelligently diagnose switch failures. Experiments show that the accuracy of classification after Kalman filter preprocessing is better than that of direct classification. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Kalman filters

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A Priori Based Deep Unfolding Method for mmWave Channel Estimation in MIMO Radar Aided V2X Communications

A Priori Based Deep Unfolding Method for mmWave Channel Esti...

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IEEE International Conference on Communications (ICC)

作者： Jiapan Yang Xiao Gong Bo Ai Wei Chen State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University China Frontiers Science Center for Smart High-speed Railway System Beijing China Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications Beijing China School of Information Engineering Zhengzhou University Zhengzhou China Key Laboratory of Railway Industry of Broadband Mobile Information Communications Beijing China

Due to the inherent high-mobility features in the Vehicles-to-Everything (V2X) scenarios, accurate channel estimation is essential to ensure the quality of communication services. Recently, multiple-input multiple-output (MIMO) radar has shown the potential to aid channel estimation. In this paper, we consider the MIMO radar aided V2X communication systems and propose a prior information aided deep learning method for channel estimation. Specifically, we use the MIMO radar to measure the angle information of moving vehicles. Based on the estimated angles, we obtain the non-zero position information of sparse angle-frequency channel. Then, by formulating the channel estimation as solving a group row sparse recovery problem, we propose a new shrinkage function and derive a priori assisted deep unfolding method. Experimental results show that the proposed method achieves the highest channel estimation accuracy compared with existing compressive sensing algorithms and deep-learning-based baseline methods.

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Device-Edge Digital Semantic Communication with Trained Non-Linear Quantization

Device-Edge Digital Semantic Communication with Trained Non-...

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IEEE Conference on Vehicular Technology (VTC)

作者： Lei Guo Wei Chen Yuxuan Sun Bo Ai State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China Frontiers Science Center for Smart High-Speed Railway System School of Electronic and Information Engineering Beijing Jiaotong University Beijing China Key Laboratory of Railway Industry of Broadband Mobile Information Communications Beijing Engineering Research Center of High-Speed Railway Broadband Mobile Communications School of Information Engineering Zhengzhou University Zhengzhou China

Powered by deep learning, semantic communication is an intelligent communication paradigm, aiming to transmit useful information in the semantic domain. In most existing work, robust semantic features can be learned against wireless channel degradation, and directly transmitted in an analog fashion. However, analog semantic communication raises various challenges to the existing system from hardware/protocol to encryption issues. In this paper, we propose a novel non-linear quantization module to efficiently quantify semantic features. A sparse scaling vector is further incorporated to reduce the dimension of transmitted semantic features. Experimental results demonstrate that the proposed nonlinear quantization achieves better performance than the linear quantization method, and the performance of the digital system achieves better performance.

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Bandgap engineering of Resorcinol-Formaldehyde Resin for Efficient H2o2 Photosynthesis from Air and Water

SSRN

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SSRN 2024年

作者： Wang, Wuming Tang, Yuyang Ran, Jiaqi Teng, Wei Peng, Cheng Xu, Zuxin Chu, Wenhai State Key Laboratory of Pollution Control and Resources Reuse College of Environmental Science and Engineering Tongji University Shanghai200092 China Shanghai Institute of Pollution Control and Ecological Security Shanghai200092 China International Joint Research Center for Sustainable Urban Water System Tongji University Shanghai200092 China Key Laboratory of Interfacial Physics and Technology Shanghai Institute of Applied Physics Chinese Academy of Sciences Shanghai201800 China Tongji University China

On-site photosynthesis of hydrogen peroxide (PHP) from water and oxygen presents a promising vision for decentralized production technology of H2O2 at low concentration. Bandgap structure is crucial to photocatalyst for efficient H2O2 photosynthesis. Here we presented a sulfur-doping strategy to modulate the bandgap structure of resorcinol-formaldehyde (RF) resin to enhance the photocatalytic activity and stability. Results revealed the optimized bandgap structure of sulfur-doped RF enables an outstanding performance, with the solar-to-chemical conversion efficiency exceeding 0.6% after 24 h. Over sulfur-doped RF, oxygen can not only undergo a one-step two-electron reduction to produce H2O2, but an additional pathway, a two-step single-electron indirect reduction pathway, also occurs. Besides, PHP solution exhibited adequate disinfection efficiency in E. coli suspend and natural water, while its disinfection by-product (DBP) formation was negligible. This study provides valuable insights into designing efficient photocatalyst for practical applications, offering a sustainable and low-energy consumption technology for decentralized water disinfection. © 2024, The Authors. All rights reserved.

关键词： Photosynthesis

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Deep Learning Method for Path Loss Prediction in Mobile Communication systems 13

Deep Learning Method for Path Loss Prediction in Mobile Comm...

引用

13th International Symposium on Antennas, Propagation and EM Theory, ISAPE 2021

作者： Xu, Zhuocheng Cao, Huiyun Yin, Yue Zhang, Xiaohan Wu, Lina He, Danping Wang, Yuanbo Beijing Jiaotong University School of Electronic and Information Engineering Beijing China State Key Laboratory of Rail Traffic Control and Safety Beijing China Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications Beijing China Frontiers Science Center for Smart High-speed Railway System Beijing China Chn Energy Shuohuang Railway Development Company Ltd Hebei China

ISBN: (纸本)9781665418126

The study of wireless channel propagation characteristics is important to channel availability analysis and wireless network optimization. The existing deterministic channel models are greatly restricted by the high complexity. This paper proposes a deep learning (DL) method for path loss (PL) prediction in mobile communication systems, which uses satellite images and neural network (NN) to achieve efficient and reliable prediction. A Deep Neural Network (DNN) framework is established based on a Convolutional Neural Network (CNN) and a Back Propagation Neural Network (BPNN), where CNN extracts environmental features of propagation paths from satellite images and BPNN predicts PL according to the distance features and environmental features. The results show that DNN proposed in this paper can reduce the time cost with satisfying accuracy compared to the traditional Ray-tracing method with the lack of a detailed model of the scene and material parameters. The DL method for making a quick prediction of the PL in this paper can be used to optimize the mobile communication systems. © 2021 IEEE.

关键词： Deep neural networks

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