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Influence of rare earth doping on hydrogen absorption properties of Zr_(7)V_(5)Fe alloy

Journal of rare Earths 2025年第1期43卷 218-226,I0008页

作者： Shuang Yang Zifeng Luo Guo Yang Lijun Lv Linhua Xu Haiyan Leng Xingbo Han Jianhui Zhu Wei Liu Pengfei Zhu Jing Xu School of Mathematics Physics and StatisticsShanghai University of Engineering ScienceShanghai201620China Shanghai Institute of Applied Physics Chinese Academy of SciencesShanghai201800China State Key Laboratory of Advanced Special Steels&Shanghai Key Laboratory of Advanced Ferrometallurgy&School of Materials Science and Engineering Shanghai UniversityShanghai200444China Instrumental Analysis and Research Center Shanghai UniversityShanghai200444China

As a representative of non-evaporative getter,Zr-V-Fe has gained widespread attention due to its advantages including low activation temperatures and rapid hydrogen absorption *** this study,we investigated the impact of La and Ce doping on the thermodynamic,kinetic,and activation properties of Zr_(7)V_(5)Fe alloy.X-ray diffraction analysis shows that rare earth doping causes a decrease in the cell volume of both the ZrV_(2) andα-Zr phases of Zr_(7)V_(5)Fe alloy,which results in an increase in the plateau pressure of the *** kinetic curves illustrate that rare earth doping leads to a coarseα-Zr phases and a larger particle size after activation,resulting in a decrease in the hydrogen absorption kinetic *** for the activation process,the rare earth doped alloys exhibit excellent activation with shorter incubation periods.X-ray photoelectron spectroscopy investigations reveal that Zr and V are initially in a highly oxidized *** the heating temperature increases,they undergo a transition from oxidation state to metal *** content of metal Zr in rare earth doped alloys is higher than that in undoped alloys at 250℃,primarily due to rare earth elements'affinity for oxygen.

关键词： rare earths Hydrogen absorption thermodynamics X-rayphotoelectron spectroscopy Activation

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Integration of metal-organic layers with quantum dots for artificial photosynthesis

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Science China Chemistry 2024年第11期67卷 3712-3718页

作者： Hong Yuan Min Zhang Ji-Hua deng Tong-Bu Lu di-Chang Zhong Institute for New Energy Materials and Low Carbon Technologies School of Materials Science and EngineeringSchool of Chemistry and Chemical EngineeringTianjin University of TechnologyTianjin 300384China

Metal-organic layers(MOLs), a type of new-emerging two-dimensional ultrathin metal-organic framework materials with large surface areas and highly exposed active sites, have shown promising applications in photocatalytic CO_(2) reduction. However, due to a lack of photosensitivity and photooxidation capability, photosensitizers and sacrificial reductants are usually necessary for MOLs-based photocatalytic CO_(2) reduction systems. In this article, by integration of MOLs and quantum dots(Qds), we constructed MOLs-based catalysts with multi-functions of photosensitivity, photoreduction and photooxidation, which thus can serve as photocatalysts for CO_(2) reduction with H_(2)O as an electron donor. Specifically, by an electrostatic self-assembly approach,nickel(Ⅱ)-based MOLs(Ni-MOLs) and CsPbBr_(3)Qds have been assembled, constructing valid Ⅱ-Scheme Ni-MOLs/CsPbBr_(3) heterojunctions with close Ni-MOLs/CsPbBr_(3)heterointerface. Such a close heterointerface shortens the charge transfer distance,thus effectively boosting the charge separation and transfer. As a result, upon illumination by visible light(λ ≥ 400 nm,100 m W cm^(-2)), the optimized photocatalyst shows high efficiency and stability in photochemical CO_(2) reduction in the absence of any photosensitizers and sacrificial reductants. The CO yield reaches as high as 124 μmol g^(-1)in 4 h, over 6 times higher than that achieved by CsPbBr_(3). Additionally, the selectivity reaches 100%. This work provides a new way to construct MOL-based catalysts for artificial photosynthesis.

关键词： metal-organic layers artificial photosynthesis CO_(2)reduction H_(2)O oxidation

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Trustworthy dNN partition for blockchain-enabled digital twin in wireless IIoT networks

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Science China(Information Sciences) 2024年第11期67卷 373-374页

作者： Xiumei dENG Jun LI Long SHI Kang WEI Ming dING Yumeng SHAO Wen CHEN Shi JIN Pillar of Information Systems Technology and Design Singapore University of Technology and Design National Mobile Communications Research Laboratory Southeast University School of Electronic and Optical Engineering Nanjing University of Science and Technology Department of Computing Hong Kong Polytechnic University Data61 CSIRO Department of Electronics Engineering Shanghai Jiao Tong University

The integration of artificial intelligence(AI) and digital twin(dT) technology has revolutionized the industrial Internet of Things(IIoT), enabling advanced automation and intelligent manufacturing [1]. Through sophisticated interactions between physical entities and their virtual counterparts,AI-driven dTs facilitate performance monitoring, analysis,simulation, and optimization of physical assets, enabling predictive maintenance and informed decision-making [2].

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Effects of the extrusion parameters on microstructure,texture and room temperature mechanical properties of extruded Mg-2.49Nd-1.82Gd-0.2Zn-0.2Zr alloy

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International Journal of Minerals,Metallurgy and Materials 2025年第1期32卷 136-146页

作者： Chenjin Zhang Guangyu Yang Lei Xiao Zhiyong Kan Jing Guo Qiang Li Wanqi Jie State Key Laboratory of Solidification Processing Northwestern Polytechnical UniversityXi’an 710072China College of Materials Science and Engineering Fuzhou UniversityFuzhou 350108China College of Chemistry and Environmental Science Qujing Normal UniversityQujing 655011China

Microstructure,texture,and mechanical properties of the extruded Mg-2.49Nd-1.82Gd-0.2Zn-0.2Zr alloy were investigated at different extrusion temperatures(260 and 320℃),extrusion ratios(10:1,15:1,and 30:1),and extrusion speeds(3 and 6 mm/s).The experimental results exhibited that the grain sizes after extrusion were much finer than that of the homogenized alloy,and the second phase showed streamline distribution along the extrusion direction(Ed).With extrusion temperature increased from 260 to 320℃,the microstructure,texture,and mechanical properties of alloys changed *** dynamic recrystallization(drX)degree and grain sizes enhanced as the extrusion ratio increased from 10:1 to 30:1,and the strength gradually decreased but elongation(EL)*** the extrusion speed increased from 3 to 6 mm/s,the grain sizes and drX degree increased significantly,and the samples presented the typical<2111>-<1123>rare-earth(rE)*** alloy extruded at 260℃ with extrusion ratio of 10:1 and extrusion speed of 3 mm/s showed the tensile yield strength(TYS)of 213 MPa and EL of 30.6%.After quantitatively analyzing the contribution of strengthening mechanisms,it was found that the grain boundary strengthening and dislocation strengthening played major roles among strengthening *** results provide some guidelines for enlarging the industrial application of extruded Mg-rE alloy.

关键词： Mg-rare earth alloys extrusion temperature extrusion ratio extrusion speed strengthening mechanisms

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deep reinforcement Learning-Based Collaborative routing Algorithm for Clustered MANETs

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China Communications 2023年第3期20卷 185-200页

作者： Zexu Li Yong Li Wenbo Wang China Telecom Research Institute Beijing 102209China Key Laboratory of Universal Wireless Communications Beijing University of Posts and TelecommunicationsBeijing 100876China

Flexible adaptation to differentiated quality of service(QoS)is quite important for future 6G network with a variety of *** ad hoc networks(MANETs)are able to provide flexible communication services to users through self-configuration and rapid ***,the dynamic wireless environment,the limited resources,and complex QoS requirements have presented great challenges for network routing *** by the development of artificial intelligence,a deep reinforcement learning-based collaborative routing(drLCr)algorithm is *** routing policy and subchannel allocation are considered jointly,aiming at minimizing the end-to-end(E2E)delay and improving the network *** sufficient training by the cluster head node,the Q-network can be synchronized to each member node to select the next hop based on local ***,we improve the performance of training by considering historical observations,which can improve the adaptability of routing policies to dynamic *** results show that the proposed drLCr algorithm outperforms other algorithms in terms of resource utilization and E2E delay by optimizing network load to avoid *** addition,the effectiveness of the routing policy in a dynamic environment is verified.

关键词： artificial intelligence deep reinforcement learning collaborative routing MANETs 6G

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Characterization of Anisotropy in Additively Manufactured Materials through Instrumented Indentation Testing

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Chinese Journal of Mechanical Engineering 2025年第1期38卷 1-16页

作者： Zhuoshao Cai Zhiwei Yang Liang Meng Kaijie Lin Yuliang Hou Thaneshan Sapanathan Jihong Zhu Weihong Zhang State IJR Center of Aerospace Design and Additive Manufacturing Northwestern Polytechnical UniversityXi’an 710072China State-Owned Sida Machinery Manufacturing Company Xianyang 712203China Jiangsu Provincial Engineering Laboratory for Laser Additive Manufacturing of High-Performance Metallic Components College of MaterialsScience and TechnologyNanjing University of Aeronautics and AstronauticsNanjing 210016China School of Mechanical and Power Engineering Zhengzhou UniversityZhengzhou 450001China Curtin Corrosion Centre WA School of Mines-MineralsEnergy and Chemical EngineeringFaculty of Science and EngineeringCurtin UniversityPerthAustralia

The accurate characterization of anisotropy for additively manufactured materials is of vital importance for both highperformance structural design and printing processing *** avoid the repetitive and redundant tensile testing on specimens prepared along diverse directions,this study proposes an instrumented indentation-based inverse identification method for the efficient characterization of additively manufactured *** the present work,a 3d finite element model of indentation test is first established for the printed material,for which an anisotropic material constitutive model is *** have demonstrated that the indentation responses are information-rich,and material anisotropy along different directions can be interpreted by a single indentation ***,an inverse identification framework is built,in which an Euclidean error norm between simulated and experimental indentation responses is minimized via optimization algorithms such as the Globally Convergent Method of Moving Asymptotes(GCMMA).The developed method has been verified on diverse printed materials referring to either the indentation curve or the residual imprint,and the superiority of this latter over the former is confirmed by a better and faster convergence of inverse *** validations on 3d printed materials(including stainless steel 316L,aluminum alloy AlSi10Mg,and titanium alloy TC4)reveal that the developed method is both accurate and reliable when compared with material constitutive behaviors obtained from uni-axial tensile tests,regardless of the degree of anisotropy among different materials.

关键词： Additive manufacturing Anisotropy property Inverse identification Indentation test

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Nonlinear time-reversal interferometry with arbitrary quadratic collective-spin interaction

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Chinese Physics B 2024年第8期33卷 63-70页

作者：胡知遥李其贤张轩晨张贺宾黄龙刚刘永椿 State Key Laboratory of Low-Dimensional Quantum Physics Department of PhysicsTsinghua UniversityBeijing 100084China School of Physics Xi'an Jiaotong UniversityXi'an 710049China China Fire and Rescue Institute Beijing 102202China Frontier Science Center for Quantum Information Beijing 100084China

Atomic nonlinear interferometry has wide applications in quantum metrology and quantum information *** we propose a nonlinear time-reversal interferometry scheme with high robustness and metrological gain based on the spin squeezing generated by arbitrary quadratic collective-spin interaction,which could be described by the Lipkin–Meshkov–Glick(LMG)*** optimize the squeezing process,encoding process,and anti-squeezing process,finding that the two particular cases of the LMG model,one-axis twisting and two-axis twisting outperform in robustness and precision,***,we propose a Floquet driving method to realize equivalent time reverse in the atomic system,which leads to high performance in precision,robustness,and *** study sets a benchmark for achieving high precision and high robustness in atomic nonlinear interferometry.

关键词： time-reversal interferometry spin squeezing quantum metrology

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Fuzzy-Inverse-Model-Based Networked Tracking Control Frameworks of Time-Varying Signals

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1708-1710页

作者： Shiwen Tong dianwei Qian keya Yuan dexin Liu Yuan Li Jiancheng Zhang the College of Robotics Beijing Union UniversityBeijing 100101China the School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China

dear Editor,Tracking control in networked environment is a very challenging problem due to the contradiction of rapid response to the time-varying signal and the inevitable delay introduced by networks. This letter has proposed several fuzzy-inverse-model-based network tracking control frameworks which are helpful in handling the system with nonlinear dynamics and uncertainties.

关键词： contradiction letter inverse

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Combining a lung microfluidic chip exposure model with transcriptomic analysis to evaluate the inflammation in BEAS-2B cells exposed to cigarette smoke

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ANALYTICA CHIMICA ACTA 2024年 1287卷 342049页

作者： Li, Zezhi Li, Xiang Feng, Boyang Xue, Jingxian Zhao, Junwei Zhu, Qingqing Liu, Kejian Xie, Fuwei Xie, Jianping Beijing Technol & Business Univ Beijing 100048 Peoples R China Zhengzhou Tobacco Res Inst CNTC Key Lab Tobacco Chem 2 Fengyang St Zhengzhou 450001 Peoples R China Beijing Life Sci Acad Beijing 102209 Peoples R China

Background: Typically, in vitro studies on the exposure of complex gaseous substances are performed in multi-well plate experiments by trapping and redissolving them, which could introduce potential bias into the results due to the use of inadequate trapping methods. Therefore, a more effective method is to expose complex gaseous substances in gaseous form online, such as using microfluidic chips in experiments. To address these challenges, we introduce a methodology that integrates a self-designed bionic-lung chip with transcriptome analysis to assess the impact of cigarette smoke (CS) exposure on changes in BEAS-2B cells cultured on-chip. results: After the microfluidic chip underwent online gas exposure, total rNA was extracted via in situ cell lysis, and rNA-Seq transcriptome analysis was conducted. And the rNA-Seq findings revealed the significant involvement of the MAPK signaling pathway associated with the inflammatory response in the cellular effects induced by CS exposure. Moreover, the validation of inflammatory response-related biomarkers through in situ fluorescence corroborated the outcomes of the transcriptome analysis. Besides, the experiment involving the inhibition of inflammation by dEX on the microfluidic chip provided additional confirmation of the previous experimental findings. Significant: In this study, we present an analytical strategy that combines microfluidic-based CS in situ exposure method with rNA-Seq technology. This strategy offers an experimental scheme for in situ exposure to complex gases, transcriptome analysis, and in situ fluorescence detection. Through the integration of the comprehen-iveness of transcriptome analysis with the chip's direct and intuitive in situ fluorescence detection with the stability and reliability of rT-PCr and Western blot experiments, we have successfully addressed the challenges associated with in vitro risk assessment for online exposure to complex gaseous substances.

关键词： Microfluidic Transcriptomic analysis Cigarette smoke Inflammation

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An Effective Machine Learning Approach with Hyperparameter Tuning for Sentiment Analysis

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data Intelligence 2025年第1期7卷 70-94页

作者： Saima Kanwal Ali raza Chunyan Bai dawei Zhang Jing Wen dileep Kumar Engineering Research Centre of Optical Instrument and Systems Ministry of Education and Shanghai Key Lab of Modern Optical SystemUniversity of Shanghai for Science and TechnologyNo.516 Jun Gong RoadShanghai 200093China Department of Mathematics School of SciencesHebei University of TechnologyBeichen CampusTianjin 300401China Shanghai Publishing and Printing College Shanghai 200093China Zhangjiang Laboratory Shanghai 200120China Department of Electronic Engineering Faculty of EngineeringThe Islamia University of Bahawalpur63100PunjabPakistan

Sentiment analysis depends on individuals’comments and opinions on *** from social media platforms like Twitter,Quora,or Facebook poses challenges due to informal language,including acronyms,misspellings,and ambiguous ***,hyperparameters in machine learning models significantly impact *** address these issues,we propose advanced feature engineering techniques in Natural Language Processing(NLP)and hyperparameter optimization to enhance prediction accuracy and generalization *** study employs Naïve Bayes,Logistic regression(Lr),Multi-layer Perceptron(MLP),and Support Vector Machine(SVM)to classify sentiments in tweets about Elon Musk’s potential acquisition of *** dataset,consisting of 100,000 tweets,is fetched using the Twitter representational state transfer application programming interface(rEST API).We outline a sentiment analysis procedure to classify unstructured Twitter data,identify influential keywords,and categorize sentiments as Positive,Negative,or *** a hybrid Lexicon NLP approach,we extract contextually significant emotionally charged words and assign sentiment *** optimization via automated search methods ensures alignment with classifier performance *** achieved an impressive accuracy rate of 97%.Cross-validation minimizes random variations,providing a reliable assessment of the model’s generalization capabilities,and demonstrating the method’s accuracy in predicting sentiments with larger new unseen standard datasets,and varying sentiment.

关键词： Hybrid lexicon Logistic regression(Lr) Machine learning Natural language processing(NLP)

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