arXiv - PHYS - Materials Science最新文献

{"title":"Integral transform technique for determining stress intensity factor in wave propagation through functionally graded piezoelectric-viscoelastic structure","authors":"Diksha, Soniya Chaudhary, Pawan Kumar Sharma","doi":"arxiv-2409.05472","DOIUrl":"https://doi.org/arxiv-2409.05472","url":null,"abstract":"This study employs an integral transform approach for Love wave propagation\u0000in a rotating composite structure having an interfacial crack. The structure\u0000comprises an initially stressed functionally graded piezoelectric viscoelastic\u0000half-space bonded to a piezoelectric viscoelastic half-space. The study focuses\u0000on two material systems: Epoxy-BNKLBT paired with Epoxy-KNLNTS and Epoxy-BNKLBT\u0000paired with Epoxy-PZT7A. The viscoelastic materials are modeled to reflect\u0000their complex behavior under rotational and stress conditions. The Galilean\u0000transformation is applied to convert the Cartesian coordinates system into a\u0000moving reference frame aligned with the Love wave's propagation. Employing\u0000Bessel function properties, the system is converted into a set of double\u0000integral equations and subsequently reformulated into simultaneous Fredholm\u0000integral equations. Numerical solutions to these Fredholm integral equations\u0000are used to calculate the electric displacement intensity factor (EDIF) and\u0000stress intensity factor (SIF) near the interfacial crack. The key objective of\u0000this study is to visualize the impact of different material parameters, like\u0000piezoelectric constants, dielectric constants, initial stress, interface\u0000electric displacement, interface stress, and rotation, on SIF and EDIF. The\u0000investigations of this study will be helpful for advanced technologies like\u0000surface acoustic wave (SAW) sensors and piezoelectric actuators, as well as to\u0000enhance SAW bio-sensor sensitivity and stability for early cancer detection and\u0000biomedical implants.","PeriodicalId":501234,"journal":{"name":"arXiv - PHYS - Materials Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"p-(001)NiO/n-(0001)ZnO heterostructures grown by pulsed laser deposition technique","authors":"Bhabani Prasad Sahu, Amandeep Kaur, Simran Arora, Subhabrata Dhar","doi":"arxiv-2409.05003","DOIUrl":"https://doi.org/arxiv-2409.05003","url":null,"abstract":"NiO/ZnO heterostructures are grown on c-sapphire substrates using pulsed\u0000laser deposition (PLD) technique. X-ray diffraction study shows that the ZnO\u0000layer epitaxially grows along [0001]-direction on (0001)sapphire surface as\u0000expected. While, the epitaxial NiO film is found to be deposited along\u0000[001]-direction on the (0001)ZnO surface. Moreover, the presence of three\u0000(001)NiO domains laterally rotated by 30{deg} with respect to each other, has\u0000also been observed in our NiO films. The study reveals the continuous nature of\u0000the NiO film, which also possesses a very smooth surface morphology. In a sharp\u0000contrast, ZnO films are found to grow along [0001]-direction when deposited on\u0000(111)NiO layers. These films also show columnar morphology. (001)NiO/(0001)ZnO\u0000layers exhibit the rectifying current-voltage characteristics that suggests the\u0000existence of p-n junction in these devices. However, the behavior could not be\u0000observed in (0001)ZnO/(111)NiO heterojunctions. The reason could be the\u0000columnar morphology of the ZnO layer. Such a morphology can facilitate the\u0000propagation of the metal ions from the contact pads to the underlying NiO layer\u0000and suppress the p-n junction effect.","PeriodicalId":501234,"journal":{"name":"arXiv - PHYS - Materials Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning polycrystal plasticity using mesh-based subgraph geometric deep learning","authors":"Hanfeng Zhai","doi":"arxiv-2409.05169","DOIUrl":"https://doi.org/arxiv-2409.05169","url":null,"abstract":"Polycrystal plasticity in metals is characterized by nonlinear behavior and\u0000strain hardening, making numerical models computationally intensive. We employ\u0000Graph Neural Network (GNN) to surrogate polycrystal plasticity from finite\u0000element method (FEM) simulations. We present a novel message-passing GNN that\u0000encodes nodal strain and edge distances between FEM mesh cells, aggregates them\u0000to obtain embeddings, and combines the decoded embeddings with the nodal\u0000strains to predict stress tensors on graph nodes. We demonstrate training GNN\u0000based on subgraphs generated from FEM mesh-graphs, in which the mesh cells are\u0000converted to nodes and edges are created between adjacent cells. The GNN is\u0000trained on 72 graphs and tested on 18 graphs. We apply the trained GNN to\u0000periodic polycrystals and learn the stress-strain maps based on strain-gradient\u0000plasticity theory. The GNN is accurately trained based on FEM graphs, in which\u0000the $R^2$ for both training and testing sets are 0.993. The proposed GNN\u0000plasticity constitutive model speeds up more than 150 times compared with the\u0000benchmark FEM method on randomly selected test polycrystals. We also apply the\u0000trained GNN to 30 unseen FEM simulations and the GNN generalizes well with an\u0000overall $R^2$ of 0.992. Analysis of the von Mises stress distributions in\u0000polycrystals shows that the GNN model accurately learns the stress distribution\u0000with low error. By comparing the error distribution across training, testing,\u0000and unseen datasets, we can deduce that the proposed model does not overfit and\u0000generalizes well beyond the training data. This work is expected to pave the\u0000way for using graphs as surrogates in polycrystal plasticity modeling.","PeriodicalId":501234,"journal":{"name":"arXiv - PHYS - Materials Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lead-free room-temperature ferroelectric thermal conductivity switch using anisotropies in thermal conductivities","authors":"Lucile FégerGREMAN UMR7347, CNRS, University of Tours, INSA Centre Val de Loire, Tours, France, Carlos Escorihuela-SayaleroDepartament de Física, Universitat Politècnica de Catalunya, Campus Nord B4-B5, Barcelona, Spain, Jean-Michel RampnouxUniversité de Bordeaux, CNRS, LOMA, UMR 5798, Talence, France, Kyriaki KontouUniv Lyon, CNRS, INSA-Lyon, Université Claude Bernard Lyon 1, CETHIL UMR5008, Villeurbanne, France, Micka BahGREMAN UMR7347, CNRS, University of Tours, INSA Centre Val de Loire, Tours, France, Jorge Íñiguez-GonzálezMaterials Research and Technology Department, Luxembourg Institute of Science and TechnologyDepartment of Physics and Materials Science, University of Luxembourg, Belvaux, Luxembourg, Claudio CazorlaDepartament de Física, Universitat Politècnica de Catalunya, Campus Nord B4-B5, Barcelona, Spain, Isabelle Monot-LaffezGREMAN UMR7347, CNRS, University of Tours, INSA Centre Val de Loire, Tours, France, Sarah DouriUniv Lyon, CNRS, INSA-Lyon, Université Claude Bernard Lyon 1, CETHIL UMR5008, Villeurbanne, FranceLaboratoire National de Métrologie et d'Essais, Stéphane GraubyUniversité de Bordeaux, CNRS, LOMA, UMR 5798, Talence, France, Riccardo RuraliInstitut de Ciència de Materials de Barcelona, ICMAB-CSIC, Campus UAB, Bellaterra, Spain, Stefan DilhaireUniversité de Bordeaux, CNRS, LOMA, UMR 5798, Talence, France, Séverine GomèsUniv Lyon, CNRS, INSA-Lyon, Université Claude Bernard Lyon 1, CETHIL UMR5008, Villeurbanne, France, Guillaume F. NatafGREMAN UMR7347, CNRS, University of Tours, INSA Centre Val de Loire, Tours, France","doi":"arxiv-2409.05216","DOIUrl":"https://doi.org/arxiv-2409.05216","url":null,"abstract":"Materials with on-demand control of thermal conductivity are the\u0000prerequisites to build thermal conductivity switches, where the thermal\u0000conductivity can be turned ON and OFF. However, the ideal switch, while\u0000required to develop novel approaches to solid-state refrigeration, energy\u0000harvesting, and even phononic circuits, is still missing. It should consist of\u0000an active material only, be environment friendly, and operate near room\u0000temperature with a reversible, fast, and large switching ratio. Here, we first\u0000predict by ab initio electronic structure calculations that ferroelectric\u0000domains in barium titanate exhibit anisotropic thermal conductivities. We\u0000confirm this prediction by combining frequency-domain thermoreflectance and\u0000scanning thermal microscopy measurements on a single crystal of barium\u0000titanate. We then use this gained knowledge to propose a lead-free thermal\u0000conductivity switch without inactive material, operating reversibly with an\u0000electric field. At room temperature, we find a switching ratio of 1.6 $pm$\u00000.3, exceeding the performances of state-of-the-art materials suggested for\u0000thermal conductivity switches.","PeriodicalId":501234,"journal":{"name":"arXiv - PHYS - Materials Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142224530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ferro-Valleytricity with In-Plane Magnetization","authors":"Yibo Liu, Yangyang Feng, Ying Dai, Baibiao Huang, Yandong Ma","doi":"arxiv-2409.04739","DOIUrl":"https://doi.org/arxiv-2409.04739","url":null,"abstract":"Ferro-valleytricity, a fundamental phenomenon that manifests spontaneous\u0000valley polarization, is generally considered to occur in two-dimensional (2D)\u0000materials with out-of-plane magnetization. Here, we propose a mechanism to\u0000realize ferro-valleytricity in 2D materials with in-plane magnetization,\u0000wherein the physics correlates to non-collinear magnetism in triangular\u0000lattice. Our model analysis provides comprehensive ingredients that allows for\u0000in-plane ferro-valleytricity, revealing that mirror symmetry is required for\u0000remarkable valley polarization and time-reversal-mirror joint-symmetry should\u0000be excluded. Through modulating in-plane magnetization offset, the valley\u0000polarization could be reversed. Followed by first-principles, such mechanism is\u0000demonstrated in a multiferroic triangular lattice of single-layer W3Cl8. We\u0000further show that the reversal of valley polarization could also be driven by\u0000applying electric field that modulates ferroelectricity. Our findings greatly\u0000enrich the valley physics research and significantly extend the scope for\u0000material classes of ferro-valleytricity.","PeriodicalId":501234,"journal":{"name":"arXiv - PHYS - Materials Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Powder Diffraction Crystal Structure Determination Using Generative Models","authors":"Qi Li, Rui Jiao, Liming Wu, Tiannian Zhu, Wenbing Huang, Shifeng Jin, Yang Liu, Hongming Weng, Xiaolong Chen","doi":"arxiv-2409.04727","DOIUrl":"https://doi.org/arxiv-2409.04727","url":null,"abstract":"Accurate crystal structure determination is critical across all scientific\u0000disciplines involving crystalline materials. However, solving and refining\u0000inorganic crystal structures from powder X-ray diffraction (PXRD) data is\u0000traditionally a labor-intensive and time-consuming process that demands\u0000substantial expertise. In this work, we introduce PXRDGen, an end-to-end neural\u0000network that determines crystal structures by learning joint structural\u0000distributions from experimentally stable crystals and their PXRD, producing\u0000atomically accurate structures refined through PXRD data. PXRDGen integrates a\u0000pretrained XRD encoder, a diffusion/flow-based structure generator, and a\u0000Rietveld refinement module, enabling the solution of structures with\u0000unparalleled accuracy in a matter of seconds. Evaluation on MP-20 inorganic\u0000dataset reveals a remarkable matching rate of 82% (1 sample) and 96% (20\u0000samples) for valid compounds, with Root Mean Square Error (RMSE) approaching\u0000the precision limits of Rietveld refinement. PXRDGen effectively tackles key\u0000challenges in XRD, such as the precise localization of light atoms,\u0000differentiation of neighboring elements, and resolution of overlapping peaks.\u0000Overall, PXRDGen marks a significant advancement in the automated determination\u0000of crystal structures from powder diffraction data.","PeriodicalId":501234,"journal":{"name":"arXiv - PHYS - Materials Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A meta-generalized gradient approximation-based time-dependent and dielectric function dependent method for optical properties of solid materials","authors":"Hong Tang, Niraj Pangeni, Adrienn Ruzsinszky","doi":"arxiv-2409.04904","DOIUrl":"https://doi.org/arxiv-2409.04904","url":null,"abstract":"Accurate and efficient calculation of optical response properties of solid\u0000materials is still challenging. We present a meta-generalized gradient\u0000approximation (metaGGA) density functional based time-dependent and dielectric\u0000function dependent method for calculating optical absorption, exciton binding\u0000energy and intrinsic exciton lifetime for bulk solids and two-dimensional (2D)\u0000monolayer materials. This method uses advanced metaGGA functionals to describe\u0000the band structures, and a dielectric function mBSE (model Bethe-Salpeter\u0000equation) to capture the screening effect accurately and efficiently and the\u0000interaction between electrons and holes. The calculated optical absorption\u0000spectra of bulk Si, diamond, SiC, MgO, and monolayer MoS2 qualitatively agree\u0000with experimental results. The exciton binding energies of the first prominent\u0000peak in the optical absorption spectra of the direct band gap solids Ar, NaCl\u0000and MgO from mBSE qualitatively agree with those from standard GW-BSE. For\u0000monolayer MoS2, mBSE predicts quantitatively accurate binding energy for the\u0000first prominent peak, better than GW-BSE does. The calculated intrinsic exciton\u0000lifetimes for materials considered here show magnitudes of several nanoseconds\u0000for most bright excitons. The presented mtaGGA-mBSE method is established as a\u0000computationally efficient alternative for optical properties of materials with\u0000an overall qualitative accuracy.","PeriodicalId":501234,"journal":{"name":"arXiv - PHYS - Materials Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CrysAtom: Distributed Representation of Atoms for Crystal Property Prediction","authors":"Shrimon Mukherjee, Madhusudan Ghosh, Partha Basuchowdhuri","doi":"arxiv-2409.04737","DOIUrl":"https://doi.org/arxiv-2409.04737","url":null,"abstract":"Application of artificial intelligence (AI) has been ubiquitous in the growth\u0000of research in the areas of basic sciences. Frequent use of machine learning\u0000(ML) and deep learning (DL) based methodologies by researchers has resulted in\u0000significant advancements in the last decade. These techniques led to notable\u0000performance enhancements in different tasks such as protein structure\u0000prediction, drug-target binding affinity prediction, and molecular property\u0000prediction. In material science literature, it is well-known that crystalline\u0000materials exhibit topological structures. Such topological structures may be\u0000represented as graphs and utilization of graph neural network (GNN) based\u0000approaches could help encoding them into an augmented representation space.\u0000Primarily, such frameworks adopt supervised learning techniques targeted\u0000towards downstream property prediction tasks on the basis of electronic\u0000properties (formation energy, bandgap, total energy, etc.) and crystalline\u0000structures. Generally, such type of frameworks rely highly on the handcrafted\u0000atom feature representations along with the structural representations. In this\u0000paper, we propose an unsupervised framework namely, CrysAtom, using untagged\u0000crystal data to generate dense vector representation of atoms, which can be\u0000utilized in existing GNN-based property predictor models to accurately predict\u0000important properties of crystals. Empirical results show that our dense\u0000representation embeds chemical properties of atoms and enhance the performance\u0000of the baseline property predictor models significantly.","PeriodicalId":501234,"journal":{"name":"arXiv - PHYS - Materials Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Chiral damping of magnons","authors":"Dae-Yun Kim, Imane Berrai, T. S. Suraj, Yves Roussigne, Shuhan Yang, Mohamed Belmeguenai, Fanrui Hu, Guoyi Shi, Hui Ru Tan, Jifei Huang, Anjan Soumyanarayanan, Kyoung-Whan Kim, Salim Mourad Cherif, Hyunsoo Yang","doi":"arxiv-2409.04713","DOIUrl":"https://doi.org/arxiv-2409.04713","url":null,"abstract":"Chiral magnets have garnered significant interest due to the emergence of\u0000unique phenomena prohibited in inversion-symmetric magnets. While the\u0000equilibrium characteristics of chiral magnets have been extensively explored\u0000through the Dzyaloshinskii-Moriya interaction (DMI), non-equilibrium properties\u0000like magnetic damping have received comparatively less attention. We present\u0000the inaugural direct observation of chiral damping through Brillouin light\u0000scattering (BLS) spectroscopy. Employing BLS spectrum analysis, we\u0000independently deduce the Dzyaloshinskii-Moriya interaction (DMI) and chiral\u0000damping, extracting them from the frequency shift and linewidth of the spectrum\u0000peak, respectively. The resulting linewidths exhibit clear odd symmetry with\u0000respect to the magnon wave vector, unambiguously confirming the presence of\u0000chiral damping. Our study introduces a novel methodology for quantifying chiral\u0000damping, with potential ramifications on diverse nonequilibrium phenomena\u0000within chiral magnets.","PeriodicalId":501234,"journal":{"name":"arXiv - PHYS - Materials Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Theoretical spin transport analysis for a spin pseudovalve-type $mathrm{L}_j$/semiconductor/$mathrm{L}_j$ trilayer (with $mathrm{L}_j$ = ferromagnetic)","authors":"Julián A. Zúñiga, Arles V. Gil Rebaza, Diego F. Coral Coral","doi":"arxiv-2409.04635","DOIUrl":"https://doi.org/arxiv-2409.04635","url":null,"abstract":"In this work, a theoretical study of spin transport in a pseudovalve spin\u0000(PSV) heterostructure is conducted. For the semiconductor (SC), the conduction\u0000band at the $Gamma$ point of reciprocal space and spin-orbit coupling (SOC)\u0000are considered. For the ferromagnetic (FM) electrodes on the left ($l$) and\u0000right ($r$), the internal exchange energy ($Delta_j$, where $j =\u0000left(l,rright)$) and the magnetization normal vector ($mathbf{n}_j$) on the\u0000barrier plane are taken into account. An analytical expression for the\u0000transmission probability as a function of $mathbf{n}_j$ direction was obtained\u0000from the {em Schr\"odinger-Pauli} equations with the boundary conditions.\u0000Furthermore, the tunnel magnetoresistance (TMR) at T $approx$ 0 K was\u0000calculated, depending on the direction of the crystallographic axis favoring\u0000the magnetization ($theta_m$) of the FM and the thickness of the SC, using the\u0000{em Landauer-B\"{u}ttiker} formula for a single channel. It is observed that\u0000the TMR reaches its maximum value when the $mathbf{n}_l$ direction is parallel\u0000to $theta_m$. Applying this physico-mathematical model to the Fe/SC/Fe PSV,\u0000with SC as GaAs, GaSb, and InAs, it was found that the {em Dresselhaus} SOC\u0000does not significantly contribute to the TMR.","PeriodicalId":501234,"journal":{"name":"arXiv - PHYS - Materials Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}