Composites Part C Open Access最新文献

{"title":"Failure modes and energy absorption in Glass Reinforced aluminum (GLARE) hybrid laminates subjected to three-point bending","authors":"Shreyas Anand,&nbsp;Nachiket Dighe,&nbsp;Pranshul Gupta,&nbsp;René Alderliesten,&nbsp;Saullo G.P. Castro","doi":"10.1016/j.jcomc.2025.100651","DOIUrl":"10.1016/j.jcomc.2025.100651","url":null,"abstract":"<div><div>This paper investigates 3-point bending failure of five different types of GLARE laminates (2A, 2B, 3, 4A and 4B). 73 configurations (419 specimens), with different stacking sequences and aluminum layer thicknesses are tested. Failure mechanisms, effect of stacking sequence, effect of aluminum rolling direction, effect of displacement rate and energy absorption are analyzed. Configurations with predominantly 0°glass fiber layers fail with delamination as the major failure mode, while configurations with predominantly 90°glass fiber layers fail with central cracking as the major failure mode. GLARE 3, with 1:1 ratio of 0°and 90°fibers, fail with an equal mix of delamination and central cracking. A semi-analytical framework that can be used to predict the force versus displacement curve for central cracking failure is proposed and validated.</div></div>","PeriodicalId":34525,"journal":{"name":"Composites Part C Open Access","volume":"18 ","pages":"Article 100651"},"PeriodicalIF":7.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266009","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":"Viscoelastic high-damping vibration attenuation of sandwich FG-GPLRC face sheets by incorporating full nonlinear effects","authors":"Hamidreza Rostami ,&nbsp;Sattar Jedari Salami","doi":"10.1016/j.jcomc.2025.100650","DOIUrl":"10.1016/j.jcomc.2025.100650","url":null,"abstract":"<div><div>This article deals with the study of geometrically and materially nonlinear free-damped vibration analysis of Sandwich beams incorporating flexible cores governed by various frequency-dependent viscoelastic models, surrounded with top and bottom face sheets reinforced through a functionally graded distribution of graphene platelets (GPLs) in large deformation. In fact, two types of nonlinearities are considered in the formulation: one arising from the nonlinear strain-displacement relationship, and the other due to the viscoelastic material behavior in the sandwich beam. To analyze the impact of including nonlinear terms in both geometric and material behavior—which has not been reported in the literature—the results are computed by adopting the geometrically nonlinear von Kármán assumptions for the core and the face sheets on one hand, and by employing a viscoelastic core material with complex frequency-dependent Young's/shear modulus that induces material nonlinearity on the other. Based on the Extended Higher-Order Sandwich Panel Theory (EHSAPT), a set of coupled nonlinear governing equations is derived using the Lagrangian technique. As a progressive step, this is the first time that a displacement control technique has been enhanced to simultaneously account for both geometric and material nonlinearities in order to obtain the vibrational characteristics of a system, making it valid for large vibration amplitudes and high damping. To validate the approach, the results obtained from EHSAPT are compared with available data in the literature. Additionally, the problem is also examined by applying Euler–Bernoulli and Timoshenko beam theories to the face sheets and core, respectively. The complex nonlinear eigenvalue problem is solved, and the natural frequencies and loss factors of the viscoelastically damped sandwich beam are calculated. Parametric studies are discussed in detail to investigate the effects of weight fraction, graphene platelet distribution pattern, core-to-face sheet thickness ratio, boundary conditions, viscoelastic core temperature, and vibration amplitude. The results provide valuable and practical insights, showing that considering appropriate ranges of geometry and material in large-amplitude nonlinear vibrations of frequency-dependent viscoelastic core sandwich beams leads to improved design and industrial optimization.</div></div>","PeriodicalId":34525,"journal":{"name":"Composites Part C Open Access","volume":"18 ","pages":"Article 100650"},"PeriodicalIF":7.0,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145099403","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":"Structure–property relationships in 3D-printed onyx-based composites reinforced with continuous fibers: role of temperature and fiber orientation","authors":"Vishista Kaushik,&nbsp;Suresh Kurra,&nbsp;Ramesh Adusumalli","doi":"10.1016/j.jcomc.2025.100649","DOIUrl":"10.1016/j.jcomc.2025.100649","url":null,"abstract":"<div><div>This study investigates the flexural performance of 3D-printed continuous fiber-reinforced composites, focusing on the influence of fiber types, orientation, and temperature. Using a carbon, glass, kevlar fiber- and Onyx matrix- filaments, specimens were fabricated as 24 or 30-layer composites. Three-point bending tests were conducted under different temperatures. The results reveal a significant influence of fiber type and orientation. Carbon fiber composite showed the highest strength of 281 MPa at 0° orientation and 127 MPa at 90° orientation at RT. At -20 °C, Carbon, Glass and Kevlar composites revealed flexural strength of 422, 308 and 188 MPa respectively (0°). Similarly, with an increase in temperature, a decrement in flexural properties can be observed in all the fiber types. The modulus for kevlar decreased from 8.29 to 5.71 to 4.15 GPa with an increase in temperature from -20 to 27 to 85 °C. Additionally, microscopic analysis highlights the failure mechanisms, including fiber pull-out, delamination, and matrix softening. Grey relation analysis used two mutually conflicted parameters (strength, cost) and reported the best and worst composite amongst 18 combinations considered. The findings provide valuable insights for optimizing the design of 3D-printed composites at different fiber orientations and temperatures enhancing their applicability in structural applications.</div></div>","PeriodicalId":34525,"journal":{"name":"Composites Part C Open Access","volume":"18 ","pages":"Article 100649"},"PeriodicalIF":7.0,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118296","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":"Estimating the creep rupture time of GFRP bars using machine learning","authors":"M.Talha Junaid ,&nbsp;Ahed Habib ,&nbsp;Mazen Shrif ,&nbsp;Samer Barakat","doi":"10.1016/j.jcomc.2025.100648","DOIUrl":"10.1016/j.jcomc.2025.100648","url":null,"abstract":"<div><div>Fiber-reinforced polymer (FRP) bars are increasingly utilized in civil structures due to their advantages in terms of corrosion resistance and a high strength-to-weight ratio. Current research on long-term durability, particularly under sustained loading (creep-rupture), has not yet fully explored the use of methods like machine learning to accurately predict the creep rupture time of FRP bars. This study seeks to address this gap by applying machine learning techniques to estimate the creep rupture time of glass fiber-reinforced polymer (GFRP) bars. The motivation for this research comes from the shortcomings of traditional models, which are often inadequate for capturing the complex nonlinear behavior of materials subjected to long-term stress. This research aims to evaluate the effectiveness of different machine learning models, including neural networks, support vector machines, and ensemble methods, in predicting the creep behavior of GFRP bars. Within the study context, a large dataset consisting of 435 experimental tests is collected from the literature. In the testing phase, the optimized neural network achieved an RMSE of 926.29 h and an R² of 0.99 on a heterogeneous dataset that also included bars tested under environmental conditioning reported in the source studies. Gaussian process regression and support vector machines also performed well, albeit with higher errors. Sensitivity analysis revealed that the level of sustained stress and bar diameter were the most critical factors for environmentally conditioned bars. Importantly, the predictors reflect standard design and material descriptors (diameter, fiber content, modulus, UTS, sustained stress) and, when reported, environmental conditioning, which together capture the primary sources of variability relevant to civil engineering practice. Overall, the findings suggest that machine learning, particularly through optimized neural networks, offers a powerful tool for predicting complex material behavior and improving the reliability of GFRP-reinforced structures. This study contributes to the field by highlighting the potential of machine learning to enhance the precision of long-term performance predictions for engineering materials, facilitating improved design and material selection in critical infrastructure.</div></div>","PeriodicalId":34525,"journal":{"name":"Composites Part C Open Access","volume":"18 ","pages":"Article 100648"},"PeriodicalIF":7.0,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145099402","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":"Material card’s identification by inverse method and multi-scale optimization approach through eigenmode analysis of unidirectional Elium®/flax laminated biocomposites","authors":"Ameny Ketata ,&nbsp;Zouhaier Jendli ,&nbsp;Mondher Haggui ,&nbsp;Abderrahim El Mahi ,&nbsp;Anas Bouguehca ,&nbsp;Mohamed Haddar","doi":"10.1016/j.jcomc.2025.100647","DOIUrl":"10.1016/j.jcomc.2025.100647","url":null,"abstract":"<div><div>This article presents a mixed finite element method (FEM) and experimental inverse identification approach for determining the ply-level elastic properties of unidirectional (UD) Elium®/flax composites. Using the global dynamic response of UD laminates, the intrinsic mechanical properties are identified. Material uncertainties are accounted for, and engineering constants are determined over a broad frequency range through a response surface methodology (RSM)-based sensitivity analysis and meta-modeling approach. A multi-objective optimization process based on a non-dominated sorting genetic algorithm (NSGA) is employed to minimize differences between experimental and numerical frequency responses. The sensitivity analysis reveals that the first seven vibration modes are primarily influenced by the longitudinal modulus (<em>E</em>₁) and the shear modulus (<em>G</em>₁₂), with <em>E</em>₁ having a dominant effect in UD configurations. The optimization process, conducted using HyperStudy™, demonstrates good agreement between the numerical and experimental frequencies. However, the use of a global error function reveals certain limitations, as it may fail to smooth out local deviations, making it challenging to precisely identify mismatches in individual vibration modes. In summary, these findings provide valuable insights into the dynamic behavior of Elium®/flax composites and offer a robust method for determining material properties card for future complex composite structures.</div></div>","PeriodicalId":34525,"journal":{"name":"Composites Part C Open Access","volume":"18 ","pages":"Article 100647"},"PeriodicalIF":7.0,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145099493","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":"Data-driven prediction of failure loads in damaged FRP composites under four-point flexure","authors":"James A. Quinn,&nbsp;Ourania Patsia,&nbsp;Gabrielis Cerniauskas,&nbsp;Dongmin Yang,&nbsp;Dilum Fernando,&nbsp;Edward D. McCarthy","doi":"10.1016/j.jcomc.2025.100646","DOIUrl":"10.1016/j.jcomc.2025.100646","url":null,"abstract":"<div><div>This study investigates the use of machine learning (ML) as a tool to make predictions of the criticality of delamination damage in fiber-reinforced polymer (FRP) composites subjected to four-point flexural loading. An extensive experimental campaign was conducted on polyester-glass FRP specimens. Most specimens were manufactured with a polytetrafluoroethylene film inserted at one interlaminar location to simulate delamination damage. Damage size, damage location through the laminate thickness, and the number of plies in the laminate, were each varied in the test matrix. The strength of damaged specimens was normalized against the strengths of corresponding pristine reference specimens to obtain a measure of damage criticality. Data augmentation techniques were subsequently utilized on the experimental data to synthetically generate a larger dataset for training, validating and testing the ML model. Output predictions of specific strength from the ML model proved very accurate for both the training dataset and the test dataset, meaning the ML model can accurately and near instantaneously predict the specific four-point flexure strength of new delamination damage cases. The method presented could be expanded to include new specimen characteristics and loading scenarios, or be combined with non-destructive testing techniques to enable data-backed, rapid decision making when delamination damage is detected in asset maintenance programs. The results highlight the effectiveness of data-driven methods for predicting the failure loads and apparent static strengths of damaged FRP composites and provide information on the most influential delamination features affecting the strength of FRP under flexure loads.</div></div>","PeriodicalId":34525,"journal":{"name":"Composites Part C Open Access","volume":"18 ","pages":"Article 100646"},"PeriodicalIF":7.0,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145099401","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":"Improving the accuracy of carbon nanotube yarn 3D printing using machine learning","authors":"Junro Sano,&nbsp;Ryosuke Matsuzaki","doi":"10.1016/j.jcomc.2025.100644","DOIUrl":"10.1016/j.jcomc.2025.100644","url":null,"abstract":"<div><div>To overcome the limitations of conventional continuous carbon fiber 3D printing in achieving precise curved printing and intricate shaping, a 3D printing technique based on carbon nanotube (CNT) yarn was proposed, offering finer and more accurate fabrication capabilities. However, the contributions of two critical features of CNT yarn—its fine diameter and yarn twist—to enhanced printability remain inadequately understood. This study explores the impact of these features on printing precision through a combination of experimental methods and machine learning approaches. The findings reveal that yarn twist plays a more significant role than diameter in reducing radius errors during single-layer circular printing. A predictive model developed in this study achieved an <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> value of 0.888 and reduced radius error magnitude by approximately 79.3% when feedback was incorporated into the printing process. These results highlight the potential of CNT yarn to advance the precision of 3D printing technologies.</div></div>","PeriodicalId":34525,"journal":{"name":"Composites Part C Open Access","volume":"18 ","pages":"Article 100644"},"PeriodicalIF":7.0,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145044679","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":"Deep learning and information fusion for structure property analysis in adhesive joints","authors":"Umut Bakhbergen ,&nbsp;Ahmed Maged ,&nbsp;Fethi Abbassi ,&nbsp;Reza Montazami ,&nbsp;Sherif Araby","doi":"10.1016/j.jcomc.2025.100645","DOIUrl":"10.1016/j.jcomc.2025.100645","url":null,"abstract":"<div><div>Interfacial adhesion is a pivotal factor in determining the overall strength and durability of composite structures across aerospace and automotive industries. Therefore, understanding the failure modes and crack propagation paths in interface-based composites underpins the service life of bulk structure. This study employs deep learning and information fusion techniques to automate structure-property analysis in adhesive joints. First, response surface methodology (RSM) is used to design experimental matrix for anodizing adherend surfaces (aluminium sheets); the control parameters are concentration, current and time. Surface topography is characterized by surface roughness and contact angle along with scanning electron microscopy (SEM) images. Interfacial strength of anodized aluminium-polyurethane (Al-PU) adhesive joints is measured, and fracture analysis is performed <em>via</em> SEM. Experimental results demonstrated that anodizing conditions – concentration 0.5 M H₂SO₄ concentration, 1.5 A current and 45 min anodizing duration– enhanced the interfacial shear strength by up to 920% compared to untreated joints. Second, a novel information fusion approach is employed; the model integrates features extracted from SEM images using ResNet with numerical data from the RSM’s matrix. The combined representation is fed into an XGBoost model which enables robust material property analysis and regression. Feature-importance analysis <em>via</em> XGBoost and Integrated Gradients provide valuable insights into how anodizing parameters and surface features affect joint strength. Through the combination of numerical data (anodizing conditions and surface topographical features) and surface and fracture image analysis, the model significantly reduced the mean absolute percentage error from 18.8% to 10.7%. The findings highlight the pivotal role of integrating quantitative and qualitative information of structural materials to develop a robust and an accurate machine learning model.</div></div>","PeriodicalId":34525,"journal":{"name":"Composites Part C Open Access","volume":"18 ","pages":"Article 100645"},"PeriodicalIF":7.0,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145044681","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":"Translaminar fracture limit curves for U-notched glass/epoxy composite laminates with different layup configurations subjected to mixed mode- I/II loading","authors":"E. Pirhadi ,&nbsp;A.R. Torabi ,&nbsp;Sahel Shahbaz ,&nbsp;M. Petrů ,&nbsp;S.S. R․ Koloor","doi":"10.1016/j.jcomc.2025.100642","DOIUrl":"10.1016/j.jcomc.2025.100642","url":null,"abstract":"<div><div>Characterization of FRP composite laminate subjected to mixed-mode loading is one of the challenging topics in fracture mechanics. In this study, numerous experiments are conducted on U-notched rectangular tension (UNRT) specimens of various tip radii made of E-glass/epoxy composite with various layup configurations for experimental measurement of the translaminar U-notch fracture toughness (TLUNFT) of the composite laminates under mixed mode-I/II loading conditions. Two fracture limit curves are developed based on a two-dimensional stress distribution around the notch for predicting the mixed mode TLUNFT taking advantage of the maximum tangential stress (MTS) and the mean stress (MS) criteria as well as the virtual isotropic material concept (VIMC). It is revealed that both two-dimensional new models, namely the translaminar U-notch maximum tangential stress (TLUN-MTS) and the translaminar U-notch mean stress (TLUN-MS) criteria, can well estimate the experimental results obtained from testing the UNRT specimens made of the unidirectional (<span><math><msub><mrow><mo>[</mo><mn>0</mn><mo>]</mo></mrow><mn>16</mn></msub></math></span>) and quasi-isotropic (<span><math><msub><mrow><mo>[</mo><mrow><mn>0</mn><mo>/</mo><mn>90</mn><mo>/</mo><mo>±</mo><mn>45</mn></mrow><mo>]</mo></mrow><mrow><mn>2</mn><mi>s</mi></mrow></msub></math></span>) E/glass epoxy composites. It should be underlined that this is the <em>first time</em> that some fracture limit curves have been developed by using the notch fracture mechanics (NFM) for estimating the TLUNFT of laminated composites subjected to mixed mode loading. These curves can be accurately, rapidly, and conveniently utilized to predict the last-ply-failure load of U-notched composite laminates subjected to in-plane loading conditions.</div></div>","PeriodicalId":34525,"journal":{"name":"Composites Part C Open Access","volume":"18 ","pages":"Article 100642"},"PeriodicalIF":7.0,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144921906","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":"Analysis of the structure and characteristics of bioglass–iron oxide composite layers on Ti-6Al-4V alloy via electrophoretic deposition","authors":"Zahra Sohani,&nbsp;Hamed Jamshidi Aval,&nbsp;Sayed Mahmood Rabiee","doi":"10.1016/j.jcomc.2025.100639","DOIUrl":"10.1016/j.jcomc.2025.100639","url":null,"abstract":"<div><div>This study investigates the structural and functional properties of bioglass–iron oxide (Fe₃O₄) composite layers deposited on Ti-6Al-4V substrates via electrophoretic deposition (EPD). Suspensions with varying Fe₃O₄ contents (10, 15, 25, and 50 wt %) were prepared to identify the optimal composition. SEM and elemental mapping revealed that the B90-F10 sample (90 % bioglass, 10 % Fe₃O₄) produced a more uniform and denser coating compared to other compositions, while minimizing porosity and crack formation. The Vickers microhardness of the B90-F10 coating reached 321.3 ± 3.4 HV, higher than that of the pure bioglass coating B100-F0 (295.1 ± 2.3 HV). Surface roughness measurements showed that B90-F10 had a lower average roughness (0.82 ± 0.41 µm) than B100-F0 (2.10 ± 0.46 µm), indicating a smoother, more compact surface. The mean coating thickness for B90-F10 was 148.32 ± 0.02 µm, slightly greater than B100-F0 (140.01 ± 0.01 µm). Contact angle tests confirmed improved hydrophilicity, with B90-F10 showing a reduced contact angle (22.56°) compared to the uncoated substrate (55.16°). Electrochemical tests revealed that although coatings slightly reduced corrosion resistance compared to bare alloy due to residual porosity, the addition of Fe₃O₄ significantly increased charge transfer resistance, indicating better barrier performance than pure bioglass coatings. In vitro bioactivity tests confirmed enhanced formation of hydroxyapatite layers, critical for osseointegration. These findings highlight the coatings’ capacity to augment implant performance by improving mechanical durability, surface characteristics, and bioactivity, thus offering a valuable functional enhancement beyond the untreated substrate.</div></div>","PeriodicalId":34525,"journal":{"name":"Composites Part C Open Access","volume":"18 ","pages":"Article 100639"},"PeriodicalIF":7.0,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887417","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}