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An intermittent lithium deposition model based on CuMnbimetallic MOF derivatives for composite lithium anode with ultrahigh areal capacity and current densities

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Nano Research 2024年第4期17卷 2763-2769页

作者： tao Wei Yanyan zhou Cheng Sun Xingtong Guo Shoudong Xu Daifen Chen Yongfu Tang School of Energy and Power Jiangsu University of Science and TechnologyZhenjiang 212003China State Key Laboratory of Metastable Materials Science and Technology(MMST) Hebei Key Laboratory of Applied ChemistryYanshan UniversityQinhuangdao 066004China College of Materials Science and Engineering Taiyuan University of TechnologyTaiyuan 030024China

Recently,three-dimensional(3D)conductive frameworks have been chosen as the host for composite lithium(Li)metal anode because of their exceptional electrical conductivity and remarkable thermal and electrochemical ***,Li tends to accumulate on the top of the 3D frameworks with homogenous lithiophilicity and Li dendrite still *** work firstly designed a bimetallic metal-organic framework(MOF)(CuMn-MOF)derived Cu_(2)O and Mn_(3)O_(4) nanoparticles decorated carbon cloth(CC)substrates(CC@Cu_(2)O/Mn_(3)O_(4))to fabricate a composite Li *** to the synergistic effects of lithiophilic Cu_(2)O and Mn_(3)O_(4),the CC@Cu_(2)O/Mn_(3)O_(4)@Li symmetrical cell can afford a prolonged cycling lifespan(1400 h)under an ultrahigh current density and areal capacity(6 mA·cm^(-2)/6 mAh·cm^(-2)).When coupled with the LiFePO_(4)(LFP)cathode,the LFP||CC@Cu_(2)O/Mn_(3)O_(4)@Li full cell demonstrated a superior performance of 89.7 mAh·g^(-1) even at an extremely high current density(10 C).Furthermore,it can also be matched well with LiNi_(0.5)Co^(0).2Mn_(0.3)O_(2)(NCM523)***,to explain the excellent performances of the CC@Cu_(2)O/Mn_(3)O_(4)@Li composite anode,an intermittent model was also *** study offers a novel model that can enhance our comprehension of the Li deposition behavior and pave the way to attain stable and safe Li metal anodes by employing bimetallic MOF-derived materials to construct 3D frameworks.

关键词： lithium metal anode(LMA) three-dimensional(3D)frameworks intermittent lithiophilic model high areal capacity bimetallic metal-organic frameworks(MOFs)

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Two-Staged Method for Ice Channel Identification Based on Image Segmentation and Corner Point Regression

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China Ocean Engineering 2024年第2期38卷 313-325页

作者： DONG Wen-bo zhou Li DING Shi-feng WANG Ai-ming CAI Jin-yan School of Naval Architecture and Ocean Engineering Jiangsu University of Science and TechnologyZhenjiang 212003China School of Naval Architecture Ocean and Civil EngineeringShanghai Jiao Tong UniversityShanghai 200240China

Identification of the ice channel is the basic technology for developing intelligent ships in ice-covered waters,which is important to ensure the safety and economy of *** the Arctic,merchant ships with low ice class often navigate in channels opened up by *** in the ice channel often depends on good maneuverability skills and abundant experience from the captain to a large *** ship may get stuck if steered into ice fields off the *** this circumstance,it is very important to study how to identify the boundary lines of ice channels with a reliable *** this paper,a two-staged ice channel identification method is developed based on image segmentation and corner point *** first stage employs the image segmentation method to extract channel *** the second stage,an intelligent corner regression network is proposed to extract the channel boundary lines from the channel region.A non-intelligent angle-based filtering and clustering method is proposed and compared with corner point regression *** training and evaluation of the segmentation method and corner regression network are carried out on the synthetic and real ice channel *** evaluation results show that the accuracy of the method using the corner point regression network in the second stage is achieved as high as 73.33%on the synthetic ice channel dataset and 70.66%on the real ice channel dataset,and the processing speed can reach up to 14.58frames per second.

关键词： ice channel ship navigation identification image segmentation corner point regression

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A hybrid spatial-temporal deep learning prediction model of industrial methanol-to-olefins process

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Frontiers of Chemical Science and Engineering 2024年第4期18卷 73-85页

作者： Jibin zhou Xue Li Duiping Liu Feng Wang tao Zhang Mao Ye Zhongmin Liu National Engineering Research Center of Lower-Carbon Catalysis Technology Dalian Institute of Chemical PhysicsChinese Academy of SciencesDalian 116023China Yulin Innovation Institute for Clean Energy Clean Energy Innovation Institute of Chinese Academy of SciencesYulin 719053China Xuelang Industrial Intelligence Technology Co. Ltd.Wuxi 214000China

Methanol-to-olefins,as a promising non-oil pathway for the synthesis of light olefins,has been successfully *** accurate prediction of process variables can yield significant benefits for advanced process control and *** challenge of this task is underscored by the failure of traditional methods in capturing the complex characteristics of industrial processes,such as high nonlinearities,dynamics,and data distribution shift caused by diverse operating *** this paper,we propose a novel hybrid spatial-temporal deep learning prediction model to address these ***,a unique data normalization technique called reversible instance normalization is employed to solve the problem of different data ***,convolutional neural network integrated with the self-attention mechanism are utilized to extract the temporal ***,a multi-graph convolutional network is leveraged to model the spatial ***,the extracted temporal and spatial features are fused as input into a fully connected neural network to complete the ***,the outputs are denormalized to obtain the ultimate *** monitoring results of the dynamic trends of process variables in an actual industrial methanol-to-olefins process demonstrate that our model not only achieves superior prediction performance but also can reveal complex spatial-temporal relationships using the learned attention matrices and adjacency matrices,making the model more ***,this model is deployed onto an end-to-end Industrial Internet Platform,which achieves effective practical results.

关键词： methanol-to-olefins process variables prediction spatial-temporal self-attention mechanism graph convolutional network

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A data-driven model of drop size prediction based on artificial neural networks using small-scale data sets

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Chinese Journal of Chemical Engineering 2024年第2期66卷 71-83页

作者： bo Wang Han zhou Shan Jing Qiang Zheng Wenjie Lan Shaowei Li Institute of Nuclear and New Energy Technology Tsinghua UniversityBeijing 100084China State Key Laboratory of Heavy Oil Processing China University of Petroleum(Beijing)Beijing 102249China State Key Laboratory of Chemical Engineering Tsinghua UniversityBeijing 100084China

An artificial neural network(ANN)method is introduced to predict drop size in two kinds of pulsed columns with small-scale data *** training,the deviation between calculate and experimental results are 3.8%and 9.3%,*** ANN model,the influence of interfacial tension and pulsation intensity on the droplet diameter has been *** size gradually increases with the increase of interfacial tension,and decreases with the increase of pulse *** can be seen that the accuracy of ANN model in predicting droplet size outside the training set range is reach the same level as the accuracy of correlation obtained based on experiments within this *** two kinds of columns,the drop size prediction deviations of ANN model are 9.6%and 18.5%and the deviations in correlations are 11%and 15%.

关键词： Artificial neural network Drop size Solvent extraction Pulsed column Two-phase flow Hydrodynamics

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On Robust Cross-view Consistency in Self-supervised Monocular Depth Estimation

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Machine Intelligence Research 2024年第3期21卷 495-513页

作者： Haimei Zhao Jing Zhang Zhuo Chen bo Yuan Dacheng tao School of Computer Science University of SydneySydney2008Australia Shenzhen International Graduate School Tsinghua UniversityShenzhen518055China School of Information Technology&Electrical Engineering University of QueenslandBrisbane4072Australia

Remarkable progress has been made in self-supervised monocular depth estimation (SS-MDE) by exploring cross-view consistency, e.g., photometric consistency and 3D point cloud consistency. However, they are very vulnerable to illumination variance, occlusions, texture-less regions, as well as moving objects, making them not robust enough to deal with various scenes. To address this challenge, we study two kinds of robust cross-view consistency in this paper. Firstly, the spatial offset field between adjacent frames is obtained by reconstructing the reference frame from its neighbors via deformable alignment, which is used to align the temporal depth features via a depth feature alignment (DFA) loss. Secondly, the 3D point clouds of each reference frame and its nearby frames are calculated and transformed into voxel space, where the point density in each voxel is calculated and aligned via a voxel density alignment (VDA) loss. In this way, we exploit the temporal coherence in both depth feature space and 3D voxel space for SS-MDE, shifting the “point-to-point” alignment paradigm to the “region-to-region” one. Compared with the photometric consistency loss as well as the rigid point cloud alignment loss, the proposed DFA and VDA losses are more robust owing to the strong representation power of deep features as well as the high tolerance of voxel density to the aforementioned challenges. Experimental results on several outdoor benchmarks show that our method outperforms current state-of-the-art techniques. Extensive ablation study and analysis validate the effectiveness of the proposed losses, especially in challenging scenes. The code and models are available at https://***/sunnyHelen/RCVC-depth.

关键词： 3D vision depth estimation cross-view consistency self-supervised learning monocular perception

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Glutamicibacter nicotianae AT6:A new strain for the efficient biodegradation of tilmicosin

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Journal of Environmental Sciences 2024年第8期142卷 182-192页

作者： Huijuan Li Hao zhou Liling Fan Long Meng Yanyun Zhao Lanmei Zhao bo Wang College of Chemical and Biological Engineering Shandong University of Science and TechnologyQingdao 266590China College of Chemistry and Chemical Engineering Heze UniversityHeze 274015China College of Safety and Environmental Engineering Shandong University of Science and TechnologyQingdao 266590China

The degradation of tilmicosin(TLM),a semi-synthetic 16-membered macrolide antibiotic,has been receiving increasing ***,there are three tilmicosin degradation methods,and among them microbial degradation is considered the best due to its high efficiency,eco-friendliness,and low ***,we found a new strain,Glutamicibacter nicotianae ***6,capable of degrading high-concentration TLM at 100 mg/L with a 97%removal *** role of tryptone was as well investigated,and the results revealed that the loading of tryptone had a significant influence on TLM *** toxicity assessment indicated that strain AT6 could efficiently convert TLM into less-toxic *** on the identified intermediates,the degradation of TLM by AT6 processing through two distinct pathways was then proposed.

关键词： Tilmicosin Glutamicibacter nicotianae Biodegradation Degradation pathway Pharmaceutical wastewater

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Engineering classification recycling of spent lithium-ion batteries through pretreatment:a comprehensive review from laboratory to scale-up application

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Rare Metals 2024年第3期43卷 915-941页

作者： Shu-Xuan Yan You-zhou Jiang Xiang-Ping Chen Lu Yuan Ting-Ting Min Yu Cao Wan-Li Peng tao zhou Hunan Provincial Key Laboratory of Chemical Power Sources College of Chemistry and Chemical EngineeringCentral South UniversityChangsha 410083China College of Chemistry and Chemical Engineering Hunan Normal UniversityChangsha 410083China School of Public Policy and Management Tsinghua UniversityBeijing 100084China China Nonferrous Metal Industry's Foreign Engineering and Construction Co. LtdBeijing 100029China

The lithium-ion batteries(LIBs)have been widely equipped in electric/hybrid electric vehicles(EVs/HEVs)and the portable electronics due to their excellent electrochemical ***,a large number of retired LIBs that consist of toxic substances(e.g.,heavy metals,electrolytes)and valuable metals(e.g.,Li,Co)will inevitably flow into the waste stream,and their incineration or landfill treatment will cause severe risks to ecosystem and human *** sustainable and efficient treatment or recycling of valuable resources from spent LIBs should be fully recognized for environmental and resource *** one of the most important processes for spent LIBs recycling,the pretreatment is an indispensable step,which is directly related to the subsequent metal extraction and separation *** considerable progresses have been made regarding the pretreatment technologies,there are few summarized reports concerning critical processes of spent LIBs recycling,especially combination of currently available recycling technologies with industrialized applications during ***,comprehensive review of the current prevailing pretreatment technologies in laboratory to existing scale-up applications is quite necessary to reveal cutting-edge development in the field of *** this review,the current pretreatment technologies are systematically categorized and introduced,along with critical *** review focused on the various options for pretreatment processes itself,instead of general spent LIBs recycling technologies without the focused topics that have been sophisticatedly reviewed by previous ***,the deactivation,discharge,dismantling,separation,liberation of active material and electrolyte treatment have been summarized with the in-depth discussion of the technology development and current status of each ***,current states of industrial development are also reviewed and discussed for the development of

关键词： Spent lithium-ion batteries(LIBs) Recycling Pretreatment Valuable metals Cutting-edge technologies Industrialized application

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Towards Semi-supervised Classification of Abnormal Spectrum Signals Based on Deep Learning

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Chinese Journal of Electronics 2024年第3期33卷 721-731页

作者： tao JIANG Wanqing CHEN Hangping zhou Jinyang HE Peihan QI School of Cyber Engineering Xidian University State Key Laboratory of Integrate Service Networks Xidian University Guangzhou Institute of Technology Xidian University

In order to cope with the heavy labor cost challenge of the manual abnormal spectrum classification and improve the effectiveness of existing machine learning schemes for spectral datasets with interference-to-signal ratios,we proposes a semi-supervised classification of abnormal spectrum signals(SSC-ASS),aimed at addressing some of the challenges in abnormal spectrum signal(ASS) classification tasks.A significant advantage of SSC-ASS is that it does not require manual labeling of every abnormal data,but instead achieves high-precision classification of ASSs using only a small number of labeled ***,the method can to some extent avoid the introduction of erroneous information resulting from the complex and variable nature of abnormal signals,thereby improving classification ***,SSC-ASS uses a memory AutoEncoder module to efficiently extract features from abnormal spectrum signals by learning from the reconstruction ***,SSC-ASS combines convolutional neural network and the K-means using a DeepCluster framework to fully utilize the unlabeled ***,SSC-ASS also utilizes pre-training,category mean memory module and replaces pseudo-labels to further improve the classification accuracy of *** we verify the classification effectiveness of SSC-ASS on synthetic spectrum datasets and real on-air spectrum dataset.

关键词： Deep learning Costs Memory management Memory modules Manuals Feature extraction Labeling

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Enhancing thermoelectric performance in P-type Mg_(3)Sb_(2)-based Zintls through optimization of band gap structure and nanostructuring

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Journal of Materials Science & Technology 2024年第3期170卷 25-32页

作者： Yi-bo Zhang Ji-Sheng Liang Chengyan Liu Qi zhou Zhe Xu Hong-bo Chen Fu-cong Li Ying Peng Lei Miao Guangxi Key Laboratory of Information Material Engineering Research Center of Electronic Information Materials and DevicesMinistry of EducationSchool of Material Science and EngineeringGuilin University of Electronic TechnologyGuilin 541004China Guangxi Key Laboratory for Relativity Astrophysics Center on Nanoenergy ResearchGuangxi Key Laboratory of Processing for Nonferrous Metal and Featured MaterialsSchool of Physical Science&TechnologyGuangxi UniversityNanning 530004China Guangxi Key Laboratory of Precision Navigation Technology and Application Guilin University of Electronic TechnologySchool of Information and CommunicationGuilin 541004China

P-type Mg_(3)Sb_(2)-based Zintls have attracted considerable interest in the thermoelectric(TE)field due to their environmental friendliness and low ***,compared to their n-type counterparts,they show relatively low TE performance,limiting their application in TE *** this work,we simultaneously introduce Bi alloying at Sb sites and Ag doping at Mg sites into the Mg_(3)Sb_(2)to coopera-tively optimize the electrical and thermal properties for the first time,acquiring the highest ZT value of∼0.85 at 723 K and a high average ZT of 0.39 in the temperature range of 323-723 K in sample Mg_(2.94)Ag_(0.06)Sb_(1.9)Bi_(0.1).The first-principle calculations show that the codoping of Ag and Bi can shift the Fermi level into the valence band and narrow the band gap,resulting in the increased carrier concentration from 3.50×10^(17)cm^(-3)in the reference Mg 3 Sb 0.9 Bi 0.1 to∼7.88×10^(19)cm^(-3)in sample Mg 2.94 Ag 0.06 Sb 0.9 Bi *** a result,a remarkable power factor of∼778.9μW m^(-1)K^(-2)at 723 K is achieved in sample Mg 2.94 Ag 0.06 Sb 0.9 Bi ***,a low lattice thermal conductivity of∼0.48 W m^(-1)K^(-1)at 723 K is also obtained with the help of phonon scattering at the distorted lattice,point defects,and nano-precipitates in sample Mg 2.94 Ag 0.06 Sb 0.9 Bi *** synergistic effect of using the multi-element co-doping/-alloying to optimize electrical properties in Mg_(3)Sb_(2)holds promise for further improving the TE performance of Zintl phase materials or even others.

关键词： Thermoelectric materials Band engineering Nanostructuring P-type Mg_(3)Sb_(2) Ag and Bi Co-doping

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Estimating wheat spike-leaf composite indicator(SLI)dynamics by coupling spectral indices and machine learning

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The Crop Journal 2024年第3期12卷 927-937页

作者： Haiyu tao Ruiheng zhou Yining Tang Wanyu Li Xia Yao tao Cheng Yan Zhu Weixing Cao Yongchao Tian National Engineering and Technology Center for Information Agriculture Key Laboratory of Crop System Analysis and Decision MakingMinistry of Agriculture and Rural AffairsJiangsu Key Laboratory for Information AgricultureNanjing Agricultural UniversityNanjing 210095JiangsuChina

The contribution of spike photosynthesis to grain yield(GY)has been overlooked in the accurate spectral prediction of ***,it’s essential to construct and estimate a yield-related phenotypic trait considering spike *** on field and spectral reflectance data from 19 wheat cultivars under two nitrogen fertilization conditions in two years,our objectives were to(i)construct a yield-related phenotypic trait(spike–leaf composite indicator,SLI)accounting for the contribution of the spike to photosynthesis,(ii)develop a novel spectral index(enhanced triangle vegetation index,ETVI3)sensitive to SLI,and(iii)establish and evaluate SLI estimation models by integrating spectral indices and machine learning *** results showed that SLI was sensitive to nitrogen fertilizer and wheat cultivar variation as well as a better predictor of yield than the leaf area ***3 maintained a strong correlation with SLI throughout the growth stage,whereas the correlations of other spectral indices with SLI were poor after spike *** spectral indices and machine learning algorithms improved the estimation accuracy of SLI,with the most accurate estimates of SLI showing coefficient of determination,root mean square error(RMSE),and relative RMSE values of 0.71,0.047,and 26.93%,*** results provide new insights into the role of fruiting organs for the accurate spectral prediction of *** high-throughput SLI estimation approach can be applied for wheat yield prediction at whole growth stages and may be assisted with agronomical practices and variety selection.

关键词： Wheat spike photosynthesis Yield-related phenotypic trait Spectral indices Machine learning Estimation

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