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Simulation and reconstruction of particle trajectories in the CEPC drift chamber

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Nuclear science and Techniques 2024年第8期35卷 9-22页

作者： Meng‑Yao Liu Wei‑dong Li Xing‑Tao Huang Yao Zhang Tao lin Ye Yuan Institute of Frontier and Interdisciplinary science Shandong UniversityQingdao 266237China Institute of High Energy physics Beijing 100049China university of Chinese Academy of sciences Beijing 100049China

The circular electron-positron collider(CEPC)is designed to precisely measure the properties of the Higgs boson,study electroweak interactions at the Z-boson peak,and search for new physics beyond the Standard *** a component of the 4th conceptual CEPC detector,the drift chamber facilitates the measurement of charged *** study implemented a Geant4-based simulation and track reconstruction for the drift *** the simulation,detector construction and response were implemented and added to the CEPC simulation *** development of track reconstruction involves track finding using the combinatorial Kalman filter method and track fitting using the tool of *** the simulated data,the tracking performance was *** results showed that both the reconstruction resolution and tracking efficiency satisfied the requirements of the CEPC experiment.

关键词： CEPC Drift chamber Detector simulation Track reconstruction

Deep learning CNN-APSO-LSSVM hybrid fusion model for feature optimization and gas-bearing prediction

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Petroleum science 2024年第4期21卷 2329-2344页

作者： Jiu-Qiang Yang Nian-Tian lin Kai Zhang Yan Cui Chao Fu dong Zhang college of Earth sciences and {2. Shandong University of Science and Technology Laboratory for Marine Mineral Resources Qingdao National Laboratory for Marine Science and Technology Transportation Institute of Inner Mongolia university Key Laboratory of Gas Hydrate Qingdao Institute of Marine Geology Ministry of Natural Resources

Conventional machine learning(CML) methods have been successfully applied for gas reservoir prediction. Their prediction accuracy largely depends on the quality of the sample data; therefore, feature optimization of the input samples is particularly important. Commonly used feature optimization methods increase the interpretability of gas reservoirs; however, their steps are cumbersome, and the selected features cannot sufficiently guide CML models to mine the intrinsic features of sample data efficiently. In contrast to CML methods, deep learning(DL) methods can directly extract the important features of targets from raw data. Therefore, this study proposes a feature optimization and gas-bearing prediction method based on a hybrid fusion model that combines a convolutional neural network(CNN)and an adaptive particle swarm optimization-least squares support vector machine(APSO-LSSVM). This model adopts an end-to-end algorithm structure to directly extract features from sensitive multicomponent seismic attributes, considerably simplifying the feature optimization. A CNN was used for feature optimization to highlight sensitive gas reservoir information. APSO-LSSVM was used to fully learn the relationship between the features extracted by the CNN to obtain the prediction results. The constructed hybrid fusion model improves gas-bearing prediction accuracy through two processes of feature optimization and intelligent prediction, giving full play to the advantages of DL and CML methods. The prediction results obtained are better than those of a single CNN model or APSO-LSSVM model. In the feature optimization process of multicomponent seismic attribute data, CNN has demonstrated better gas reservoir feature extraction capabilities than commonly used attribute optimization methods. In the prediction process, the APSO-LSSVM model can learn the gas reservoir characteristics better than the LSSVM model and has a higher prediction accuracy. The constructed CNN-APSO-LSSVM mode

关键词： Multicomponent seismic data Deep learning Adaptive particle swarm optimization Convolutional neural network Least squares support vector machine Feature optimization Gas-bearing distribution prediction

Roadmap for rechargeable batteries:present and beyond

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science china Chemistry 2024年第1期67卷 13-42页

作者： Sen Xin Xu Zhang lin wang Haijun Yu Xin Chang Yu-Ming Zhao Qinghai Meng Pan Xu Chen-Zi Zhao Jiahang Chen Huichao Lu Xirui Kong Jiulin wang Kai Chen Gang Huang Xinbo Zhang Yu Su Yao Xiao Shu-Lei Chou Shi{2. Zhang Zaiping Guo Aobing Du Guanglei Cui Gaojing Yang Qing Zhao Liubing {2. {2. Zhou Feiyu Kang Hu Hong Chunyi Zhi Zhizhang Yuan Xianfeng Li Yifei Mo Yizhou Zhu {2.fang Yu Xincheng Lei Jianxiong Zhao Jiayi wang {2. Su Yu-Guo Guo Qiang Zhang Jun Chen Li-Jun Wan CAS Key Laboratory of Molecular Nanostructure and Nanotechnology CAS Research/Education Center for Excellence in Molecular SciencesBeijing National Laboratory for Molecular Sciences(BNLMS)Institute of ChemistryChinese Academy of Sciences(CAS)Beijing 100190China university of Chinese Academy of sciences.Beijing 100049 China Institute of Advanced Battery Materials and Devices Faculty of Materials and ManufacturingBeijing University of TechnologyBeijing 100124China Key Laboratory of Advanced Functional Materials Ministry of EducationBeijing University of TechnologyBeijing 100124China Beijing Key Laboratory of Green Chemical Reaction engineering and Technology Department of Chemical EngineeringTsinghua UniversityBeijing 100084China State Key Laboratory of Chemistry and Utilization of Carbon-Based Energy Resources College of ChemistryXinjiang UniversityUrumqi 830046China department of Chemical engineering Shanghai Jiao Tong UniversityShanghai 200240China State Key Laboratory of Rare Earth Resource Utilization Changchun Institute of Applied ChemistryChangchun 130022China Institute for Carbon Neutralization College of Chemistry and Materials EngineeringWenzhou UniversityWenzhou 325035China School of Chemical engineering and Advanced Materials The University of AdelaideAdelaide 5000Australia qingdao Industrial Energy Storage Research Institute Qingdao Institute of Bioenergy and Bioprocess TechnologyChinese Academy of SciencesQingdao 266101China Frontiers science Center for New Organic Matter Key Laboratory of Advanced Energy Materials Chemistry(Ministry of Education)College of ChemistryNankai UniversityTianjin 300071China college of Chemistry and Materials science Jinan UniversityGuangzhou 511443China Tsinghua Shenzhen International Graduate School Tsinghua UniversityShenzhen 518055China department of Materials science and {2. City University of Hong KongHong Kong 999077China Division of Energy Storage Dalian National Laboratory for Clean Energy(DNL)Dalian Institute of Chem

Rechargeable batteries currently hold the largest share of the electrochemical energy storage market,and they play a major role in the sustainable energy transition and industrial decarbonization to respond to global climate *** to the increased popularity of consumer electronics and electric vehicles,lithium-ion batteries have quickly become the most successful rechargeable batteries in the past three decades,yet growing demands in diversified application scenarios call for new types of rechargeable *** efforts are made to developing the next-generation post-Li-ion rechargeable batteries,which include,but are not limited to solid-state batteries,lithium–sulfur batteries,sodium-/potassium-ion batteries,organic batteries,magnesium-/zinc-ion batteries,aqueous batteries and flow *** the great achievements,challenges persist in precise understandings about the electrochemical reaction and charge transfer process,and optimal design of key materials and interfaces in a *** roadmap tends to provide an overview about the current research progress,key challenges and future prospects of various types of rechargeable *** computational methods for materials development,and characterization techniques will also be discussed as they play an important role in battery research.

关键词： energy storage rechargeable batteries battery materials electrochemistry

Interaction Study of Guest with Host in Clathrate Hydrate

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Journal of Natural Gas Chemistry 2007年第4期16卷 423-427页

作者： shunle dong physics department{2. Ocean University of ChinaQingdao 266100ShandongChina

Lattice dynamical simulations of noble gas hydrate structures I and II have been performed. Potential energies were investigated to study the influence of guest species on the stability of the hydrate structure. Results show that when the diameter of inclusion molecules is between 3 A and 4.2.A, such as Ar and Kr, the critical role of the 512.cage in the stabilization of hydrates becomes effective. For Xe hydrates SI and SII, with the help of lattice dynamical calculations, the modes attributions are identified directly. We proposed the resonant effect of the fingerprint frequency at about 7 meV and 10 meV which arise from the coupling of Xe molecules in the 512.cage with the host lattice.

关键词： clathrate hydrate He Ne Ar Kr Xe lattice dynamical simulation

A New Method for Solving Nonlinear Partial Differential Equations Based on Liquid Time-Constant Networks

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Journal of Systems science & Complexity 2024年第2期37卷 480-493页

作者： SUN Jiuyun dong Huanhe FANG Yong college of Mathematics and Systems science Shandong University of Science and TechnologyQingdao 266590China

In this paper,physics-informed liquid networks(PILNs)are proposed based on liquid time-constant networks(LTC)for solving nonlinear partial differential equations(PDEs).In this approach,the network state is controlled via ordinary differential equations(ODEs).The significant advantage is that neurons controlled by ODEs are more expressive compared to simple activation *** addition,the PILNs use difference schemes instead of automatic differentiation to construct the residuals of PDEs,which avoid {2. loss in the neighborhood of sampling *** this method draws on both the traveling wave method and physics-informed neural networks(PINNs),it has a better physical ***,the KdV equation and the nonlinear Schr¨odinger equation are solved to test the generalization ability of the *** the best of the authors’knowledge,this is the first deep learning method that uses ODEs to simulate the numerical solutions of PDEs.

关键词： Nonlinear partial differential equations numerical solutions physics-informed liquid networks physics-informed neural networks

Physical informed memory networks for solving PDEs:implementation and applications

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Communications in Theoretical physics 2024年第2期76卷 51-61页

作者： Jiuyun Sun Huanhe dong Yong Fang college of Mathematics and Systems science Shandong University of Science and TechnologyQingdao 266590China

With the advent of physics informed neural networks(PINNs),deep learning has gained interest for solving nonlinear partial differential equations(PDEs)in recent *** this paper,physics informed memory networks(PIMNs)are proposed as a new approach to solving PDEs by using physical laws and dynamic behavior of *** the fully connected structure of the PINNs,the PIMNs construct the long-term dependence of the dynamics behavior with the help of the long short-term memory ***,the PDEs residuals are approximated using difference schemes in the form of convolution filter,which avoids {2. loss at the neighborhood of the sampling ***,the performance of the PIMNs is assessed by solving the Kd V equation and the nonlinear Schr?dinger equation,and the effects of difference schemes,boundary conditions,network structure and mesh size on the solutions are *** show that the PIMNs are insensitive to boundary conditions and have excellent solution accuracy even with only the initial conditions.

关键词： nonlinear partial differential equations physics informed memory networks physics informed neural networks numerical solution

α-particle preformation factors in heavy and superheavy nuclei

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Chinese physics C 2024年第4期48卷 146-158页

作者：骆松张冬萌亓林静陈勋初鹏程李小华 School of Nuclear science and Technology University of South ChinaHengyang 421001China School of Mathematics and physics Qingdao University of Science and TechnologyQingdao 266033China National Exemplary Base for International Sci&Tech.Collaboration of Energy and Nuclear Safety University of South ChinaHengyang 421001China Cooperative Innovation Center for Nuclear Fuel Cycle Technology&Equipment University of South ChinaHengyang 421001China Key Laboratory of Low Dimensional Quantum Structures and Quantum Control Hunan Normal UniversityChangsha 410081China

In this study,α-particle preformation factors in heavy and superheavy nuclei from ^(2.0)Th to ^(2.4)Og are *** combing experimental α decay energies and half-lives,the α-particle preformation factors P_(α) are extracted from the ratios between theoretical α decay half-lives calculated using the Two-Potential Approach (TPA)and experimental *** find that the α-particle preformation factors exhibit a noticeable odd-even staggering behavior,and unpaired nucleons inhibit α-particle ***,we find that both the α decay energy and mass number of parent nucleus exhibit considerable regularity with the extracted experimental α-particle preformation *** considering the major physical factors,we propose a local phenomenological formula with only five valid parameters for α-particle preformation factors P_(α).This analytic expression has a clear physical meaning as well as good *** an application,this analytic formula is extended to estimate the α-particle preformation factors and further predict the α decay half-lives for unknown even-even nuclei with Z=118 and 12..

关键词： αdecay half-lives preformation factors heavy and superheavy nuclei

Enhanced nitrite electroreduction to ammonia via interfacial dual-site adsorption

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Journal of Energy Chemistry 2024年第9期96卷 328-335页

作者： Xiaokang Chen Shengliang Zhai Yi Tan Le Su dong Zhai Wei-Qiao Deng Hao Wu Institute of Frontier Chemistry School of Chemistry and Chemical EngineeringShandong UniversityQingdao 266071ShandongChina college of physics and {2. engineering Shanxi Normal UniversityTaiyuan 030031ShanxiChina Suzhou Research Institute of shandong university Suzhou 215123JiangsuChina

The nitrite(NO_(2.^(−))to ammonia(NH3)electroreduction reaction(NO_(2.^(−)RR)would be impeded by sluggish proton-coupled electron transfer kinetics and competitive hydrogen evolution reaction(HER).A key to improving the NH_(3) selectivity is to facilitate adsorption and activation of NO_(2.^(−),which is generally undesirable in unitary *** this work,an efficient NO_(2.^(−)RR catalyst is constructed by cooperating Pd with In2.3,in which NO_(2.^(−)could adsorb on interfacial dual-site through“Pd–N–O–In”linkage,leading to strengthened NO_(2.^(−)adsorption and easier N=O bond cleavage than that on unitary Pd or ***,the Pd/In_(2.O_(3)composite exhibits moderate H^(*)adsorption,which may facilitate protonation kinetics while inhibiting competitive *** a result,it exhibits a fairly high NH_(3)yield rate of 62..76 mmol h^(−1)g^(−1)cat with a Faradaic efficiency(FE)of 95.72.,good selectivity of 91.96%,and cycling stability towards the NO_(2.^(−)RR,surpassing unitary In_(2.O_(3)and Pd/C ***,computed results indicate that NH_(3)production on Pd/In_(2.O_(3)follows the deoxidation to hydrogenation *** work highlights the significance of H^(*)and NO_(2.^(−)adsorption modulation and N=O activation in NO_(2.^(−)RR electrochemistry by creating synergy between a mediocre catalyst with an appropriate cooperator.

关键词： Dual-site adsorption Nitrite electroreduction Ammonia Pd Hydrogenation

Synergetic PVA degradation and H2.evolution in photocatalytic fuel cells using Ag@Fe_(2.O_(3) cathode

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Frontiers of Chemical science and engineering 2024年第1期18卷 85-93页

作者： Likun Sun Kesi Xiong Baoning Zhang Jinghong Fang Yingchao He Min wang Zhixing Gan Fanglin Du Qiong Sun Liyan Yu Lifeng {2. college of Materials science and {2. Qingdao University of Science and TechnologyQingdao 266042China Center for Future Optoelectronic Functional Materials School of Computer and Electronic InformationNanjing Normal UniversityNanjing 210023China department of physics Hamline UniversitySt.Paul 55104USA

In a dual-chamber photocatalytic fuel cell device,polyvinyl alcohol degradation and H2.evolution were concurrently *** setup involved commercial P2. as the photoanode and Ag@Fe_(2.O_(3) nanoparticles as the ***,the feasibility of a Fentonlike reaction in the cathode,utilizing Fe^(2.)ions and pumped O_(2.,was *** cathode materials,polyvinyl alcohol types,and pH values’effects were assessed on device *** tests highlighted photoinduced holes(h+)and OH·radicals as pivotal contributions to polyvinyl alcohol ***-term stability of the device was established through cycling experiments.

关键词： photocatalytic fuel cell PVA Ag@Fe_(2.O_(3) cathode materials Fenton-like reaction