To address the poor mechanical properties of polydimethylsiloxane(PDMS)and enhance the understanding of the reinforcement mechanisms of aerogel network structures in rubber matrices,this study reinforced PDMS using an...To address the poor mechanical properties of polydimethylsiloxane(PDMS)and enhance the understanding of the reinforcement mechanisms of aerogel network structures in rubber matrices,this study reinforced PDMS using an ordered interconnected three-dimensional montmorillonite(MMT)aerogel network.The average pore diameter of the aerogels was successfully reduced from 11.53μm to 2.51μm by adjusting the ratio of poly(vinyl alcohol)(PVA)to MMT via directional freezing.Changes in the aerogel network were observed in field emission scanning electron microscope(FESEM)images.After vacuum impregnation,the aerogel network structure of the composites was observed using FESEM.Tensile tests indicated that as the pore diameter decreased,the elongation at break of the composites first increased to a peak of329.61%before decreasing,while the tensile strength and Young's modulus continuously increased to their maximum values of 6.29 MPa and24.67 MPa,respectively.Meanwhile,FESEM images of the tensile cracks and fracture surfaces showed that with a reduction in aerogel pore diameter,the degrees of crack deflection and interfacial debonding increased,presenting a rougher fracture surface.These phenomena enable the composites to dissipate substantial energy during tension,thus effectively improving the mechanical strength of the composites.The present work elucidates the bearing of ordered three-dimensional aerogel network structures on the performance of rubber matrices and provides crucial theoretical insights and technical guidance for the creation and optimization of high-performance PDMS-based composites.展开更多
Multilayer complex dynamical networks,characterized by the intricate topological connections and diverse hierarchical structures,present significant challenges in determining complete structural configurations due to ...Multilayer complex dynamical networks,characterized by the intricate topological connections and diverse hierarchical structures,present significant challenges in determining complete structural configurations due to the unique functional attributes and interaction patterns inherent to different layers.This paper addresses the critical question of whether structural information from a known layer can be used to reconstruct the unknown intralayer structure of a target layer within general weighted output-coupling multilayer networks.Building upon the generalized synchronization principle,we propose an innovative reconstruction method that incorporates two essential components in the design of structure observers,the cross-layer coupling modulator and the structural divergence term.A key advantage of the proposed reconstruction method lies in its flexibility to freely designate both the unknown target layer and the known reference layer from the general weighted output-coupling multilayer network.The reduced dependency on full-state observability enables more deployment in engineering applications with partial measurements.Numerical simulations are conducted to validate the effectiveness of the proposed structure reconstruction method.展开更多
An efficient data-driven numerical framework is developed for transient heat conduction analysis in thin-walled structures.The proposed approach integrates spectral time discretization with neural network approximatio...An efficient data-driven numerical framework is developed for transient heat conduction analysis in thin-walled structures.The proposed approach integrates spectral time discretization with neural network approximation,forming a spectral-integrated neural network(SINN)scheme tailored for problems characterized by long-time evolution.Temporal derivatives are treated through a spectral integration strategy based on orthogonal polynomial expansions,which significantly alleviates stability constraints associated with conventional time-marching schemes.A fully connected neural network is employed to approximate the temperature-related variables,while governing equa-tions and boundary conditions are enforced through a physics-informed loss formulation.Numerical investigations demonstrate that the proposed method maintains high accuracy even when large time steps are adopted,where standard numerical solvers often suffer from instability or excessive computational cost.Moreover,the framework exhibits strong robustness for ultrathin configurations with extreme aspect ratios,achieving relative errors on the order of 10−5 or lower.These results indicate that the SINN framework provides a reliable and efficient alternative for transient thermal analysis of thin-walled structures under challenging computational conditions.展开更多
In recent years,research investigations have focused on the substantial freshwater storage in the Beaufort Gyre(BG)region due to climate change.Despite active mesoscale eddies in the area,a notable gap in understandin...In recent years,research investigations have focused on the substantial freshwater storage in the Beaufort Gyre(BG)region due to climate change.Despite active mesoscale eddies in the area,a notable gap in understanding the three-dimensional structure and induced transport has been observed.This study concentrates on the Canada Basin in the western Arctic Ocean,specifically examining a subsurface anticyclonic eddy(SAE)sampled by a Mooring A in the BG region.Hybrid Coordinate Ocean Model(HYCOM)analysis data reveal its lifecycle from February 15 to March 15,2017,marked by initiation,development,maturity,decay,and termination stages.This work extends the finding of SAE passing through Mooring A by examining its overall effects,spatiotemporal variations,and swirl transport.SAE generation through baroclinic instability,which contributes to the westward tilt of the vertical axis,is also confirmed in this study.Swirl transport induced by SAE is predominantly eastward and downward due to its trajectory and background flow.SAE temporarily weakens stratification and extends the subsurface depth but demonstrates transient effects.Moreover,SAE transports upper-layer freshwater,Pacific Winter Water,and Atlantic Water downward,emphasizing its potential influence on freshwater redistribution in the Canadian Basin.This research provides valuable insights into mesoscale eddy dynamics,revealing their role in modulating the upper water mass in the BG region.展开更多
A zinc complex, [Zn(iso)_2(H_2O)_4](iso=C_6H_4NO_2^-), was synthesized and characterized by elemental analysis, thermal analysis and IR spectrum studies. The crystal structure of the complex was determined by X-ray di...A zinc complex, [Zn(iso)_2(H_2O)_4](iso=C_6H_4NO_2^-), was synthesized and characterized by elemental analysis, thermal analysis and IR spectrum studies. The crystal structure of the complex was determined by X-ray diffraction. The crystal crystallizes in the triclinic system, molecular formula ZnC12H16N2O8, Mr=381.64, space group P with a = 6.338(1), b =6.919(1), c=9.277(1), α=96.28(1), β=104.91(1), γ=112.85(1)°, V=352.12(9)?3, Z=1, Dc=1.80g?cm-3 and F(000)=196, μ =1.791mm-1. The crystal structure was solved by direct methods for final R=0.0204 and Rw=0.0542 for 1258 observed reflections with [Fo>4σ(Fo)]. The crystal structure reveals that zinc ion is trans-octahedral with two pyridyl nitrogens and two aque oxygens at the equational positions and two aqua oxygens at the axial positions. The complex forms a three-dimensional network through intermolecular hydrogen bonds.展开更多
Reticulated polyurethane was chosen as the preceramic material for preparing the porous preform using the replication process. The immersing and sintering processes were each performed twice for fabricating a high-por...Reticulated polyurethane was chosen as the preceramic material for preparing the porous preform using the replication process. The immersing and sintering processes were each performed twice for fabricating a high-porosity and super-strong skeleton. The aluminum magnesium matrix composites reinforced with three-dimensional network structure were prepared using the infiltration technique by pressure assisting and vacuum driving. Light interfacial reactions have played a profitable role in most of the ceramic-metal systems. The metal matrix composites interpenetrated with the ceramic phase have a higher wear resistance than the metal matrix phase. The volume fraction of ceramic reinforcement has a significant effect on the abrasive wear, and the wear rate can be decreased with the increase of the volume fraction of reinforcement.展开更多
To solve the volume expansion and poor electrical conductivity of germanium-based anode materials,Ge/rGO/CNTs nanocomposites with three-dimensional network structure are fabricated through the dispersion of polyethyle...To solve the volume expansion and poor electrical conductivity of germanium-based anode materials,Ge/rGO/CNTs nanocomposites with three-dimensional network structure are fabricated through the dispersion of polyethylene-polypropylene glycol(F127)and reduction of hydrogen.An interesting phenomenon is discovered that F127 can break GeO_(2)polycrystalline microparticles into 100 nm nanoparticles by only physical interaction,which promotes the uniform dispersion of GeO_(2)in a carbon network structure composed of graphene(rGO)and carbon nanotubes(CNTs).As evaluated as anode material of Lithium-ion batteries,Ge/rGO/CNTs nanocomposites exhibit excellent lithium storage performance.The initial specific capacity is high to 1549.7 mAh/g at 0.2 A/g,and the reversible capacity still retains972.4 mAh/g after 100 cycles.The improved lithium storage performance is attributed to that Ge nanoparticles can effectively slow down the volume expansion during charge and discharge processes,and threedimensional carbon networks can improve electrical conductivity and accelerate lithium-ion transfer of anode materials.展开更多
Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data co...Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data collection process,resulting in temporal misalignment or displacement.Due to these factors,the node representations carry substantial noise,which reduces the adaptability of the multivariate coupled network structure and subsequently degrades anomaly detection performance.Accordingly,this study proposes a novel multivariate anomaly detection model grounded in graph structure learning.Firstly,a recommendation strategy is employed to identify strongly coupled variable pairs,which are then used to construct a recommendation-driven multivariate coupling network.Secondly,a multi-channel graph encoding layer is used to dynamically optimize the structural properties of the multivariate coupling network,while a multi-head attention mechanism enhances the spatial characteristics of the multivariate data.Finally,unsupervised anomaly detection is conducted using a dynamic threshold selection algorithm.Experimental results demonstrate that effectively integrating the structural and spatial features of multivariate data significantly mitigates anomalies caused by temporal dependency misalignment.展开更多
Alkali-free SiO_(2)-Al_(2)O_(3)-CaO-MgO with different SiO_(2)/Al_(2)O_(3)mass ratios was prepared by conventional melt quenching method.The glass network structure,thermodynamic properties and elastic modulus changes...Alkali-free SiO_(2)-Al_(2)O_(3)-CaO-MgO with different SiO_(2)/Al_(2)O_(3)mass ratios was prepared by conventional melt quenching method.The glass network structure,thermodynamic properties and elastic modulus changes with SiO_(2)and Al_(2)O_(3)ratios were investigated using various techniques.It is found that when SiO_(2)is replaced by Al_(2)O_(3),the Q^(4) to Q^(3) transition of silicon-oxygen network decreases while the aluminum-oxygen network increases,which result in the transformation of Si-O-Si bonds to Si-O-Al bonds and an increase in glass network connectivity even though the intermolecular bond strength decreases.The glass transition temperature(T_(g))increases continuously,while the thermal expansion coefficient increases and high-temperature viscosity first decreases and then increases.Meanwhile,the elastic modulus values increase from 93 to 102 GPa.This indicates that the elastic modulus is mainly affected by packing factor and dissociation energy,and elements with higher packing factors and dissociation energies supplant those with lower values,resulting in increased rigidity within the glass.展开更多
This research centers on structural health monitoring of bridges,a critical transportation infrastructure.Owing to the cumulative action of heavy vehicle loads,environmental variations,and material aging,bridge compon...This research centers on structural health monitoring of bridges,a critical transportation infrastructure.Owing to the cumulative action of heavy vehicle loads,environmental variations,and material aging,bridge components are prone to cracks and other defects,severely compromising structural safety and service life.Traditional inspection methods relying on manual visual assessment or vehicle-mounted sensors suffer from low efficiency,strong subjectivity,and high costs,while conventional image processing techniques and early deep learning models(e.g.,UNet,Faster R-CNN)still performinadequately in complex environments(e.g.,varying illumination,noise,false cracks)due to poor perception of fine cracks andmulti-scale features,limiting practical application.To address these challenges,this paper proposes CACNN-Net(CBAM-Augmented CNN),a novel dual-encoder architecture that innovatively couples a CNN for local detail extraction with a CBAM-Transformer for global context modeling.A key contribution is the dedicated Feature FusionModule(FFM),which strategically integratesmulti-scale features and focuses attention on crack regions while suppressing irrelevant noise.Experiments on bridge crack datasets demonstrate that CACNNNet achieves a precision of 77.6%,a recall of 79.4%,and an mIoU of 62.7%.These results significantly outperform several typical models(e.g.,UNet-ResNet34,Deeplabv3),confirming their superior accuracy and robust generalization,providing a high-precision automated solution for bridge crack detection and a novel network design paradigm for structural surface defect identification in complex scenarios,while future research may integrate physical features like depth information to advance intelligent infrastructure maintenance and digital twin management.展开更多
Physics-informed neural networks(PINNs)have emerged as a promising class of scientific machine learning techniques that integrate governing physical laws into neural network training.Their ability to enforce different...Physics-informed neural networks(PINNs)have emerged as a promising class of scientific machine learning techniques that integrate governing physical laws into neural network training.Their ability to enforce differential equations,constitutive relations,and boundary conditions within the loss function provides a physically grounded alternative to traditional data-driven models,particularly for solid and structural mechanics,where data are often limited or noisy.This review offers a comprehensive assessment of recent developments in PINNs,combining bibliometric analysis,theoretical foundations,application-oriented insights,and methodological innovations.A biblio-metric survey indicates a rapid increase in publications on PINNs since 2018,with prominent research clusters focused on numerical methods,structural analysis,and forecasting.Building upon this trend,the review consolidates advance-ments across five principal application domains,including forward structural analysis,inverse modeling and parameter identification,structural and topology optimization,assessment of structural integrity,and manufacturing processes.These applications are propelled by substantial methodological advancements,encompassing rigorous enforcement of boundary conditions,modified loss functions,adaptive training,domain decomposition strategies,multi-fidelity and transfer learning approaches,as well as hybrid finite element–PINN integration.These advances address recurring challenges in solid mechanics,such as high-order governing equations,material heterogeneity,complex geometries,localized phenomena,and limited experimental data.Despite remaining challenges in computational cost,scalability,and experimental validation,PINNs are increasingly evolving into specialized,physics-aware tools for practical solid and structural mechanics applications.展开更多
This article aims to develop a head pursuit （HP） guidance law for three-dimensional hypervelocity interception, so that the effect of the perturbation induced by seeker detection can be reduced. On the basis of a no...This article aims to develop a head pursuit （HP） guidance law for three-dimensional hypervelocity interception, so that the effect of the perturbation induced by seeker detection can be reduced. On the basis of a novel HP three-dimensional guidance model, a nonlinear variable structure guidance law is presented by using Lyapunov stability theory. The guidance law positions the interceptor ahead of the target on its tlight trajectory, and the speed of the interceptor is required to be lower than that of the target, A numerical example of maneuvering ballistic target interception verifies the rightness of the guidance model and the effectiveness of the proposed method.展开更多
Microseismic/acoustic emission(MS/AE)source localization method is crucial for predicting and controlling of potentially dangerous sources of complex structures.However,the locating errors induced by both the irregula...Microseismic/acoustic emission(MS/AE)source localization method is crucial for predicting and controlling of potentially dangerous sources of complex structures.However,the locating errors induced by both the irregular structure and pre-measured velocity are poorly understood in existing methods.To meet the high-accuracy locating requirements in complex three-dimensional hole-containing structures,a velocity-free MS/AE source location method is developed in this paper.It avoids manual repetitive training by using equidistant grid points to search the path,which introduces A*search algorithm and uses grid points to accommodate complex structures with irregular holes.It also takes advantage of the velocity-free source location method.To verify the validity of the proposed method,lead-breaking tests were performed on a cubic concrete test specimen with a size of 10 cm10 cm10 cm.It was cut out into a cylindrical empty space with a size of/6cm10 cm.Based on the arrivals,the classical Geiger method and the proposed method are used to locate lead-breaking sources.Results show that the locating error of the proposed method is 1.20 cm,which is less than 2.02 cm of the Geiger method.Hence,the proposed method can effectively locate sources in the complex three-dimensional structure with holes and achieve higher precision requirements.展开更多
The performance of polymer networks is directly determined by their structure.Understanding the network structure offers insights into optimizing material performance,such as elasticity,toughness,and swelling behavior...The performance of polymer networks is directly determined by their structure.Understanding the network structure offers insights into optimizing material performance,such as elasticity,toughness,and swelling behavior.Herein,in this study we introduce the Dijkstra algorithm from graph theory to characterize polymer networks based on star-shaped multi-armed precursors by employing coarse-grained molecular dynamics simulations coupled with stochastic reaction model.Our research focuses on the structure characteristics of the generated networks,including the number and size of loops,as well as network dispersity characterized by loops.Tracking the number of loops during network generation allows for the identification of the gel point.The size distribution of loops in the network is primarily related to the functionality of the precursors,and the system with fewer precursor arms exhibiting larger average loop sizes.Strain-stress curves indicate that materials with identical functionality and precursor arm lengths generally exhibit superior performance.This method of characterizing network structures helps to refine microscopic structural analysis and contributes to the enhancement and optimization of material properties.展开更多
Existing imaging techniques cannot simultaneously achieve high resolution and a wide field of view,and manual multi-mineral segmentation in shale lacks precision.To address these limitations,we propose a comprehensive...Existing imaging techniques cannot simultaneously achieve high resolution and a wide field of view,and manual multi-mineral segmentation in shale lacks precision.To address these limitations,we propose a comprehensive framework based on generative adversarial network(GAN)for characterizing pore structure properties of shale,which incorporates image augmentation,super-resolution reconstruction,and multi-mineral auto-segmentation.Using real 2D and 3D shale images,the framework was assessed through correlation function,entropy,porosity,pore size distribution,and permeability.The application results show that this framework enables the enhancement of 3D low-resolution digital cores by a scale factor of 8,without paired shale images,effectively reconstructing the unresolved fine-scale pores under a low resolution,rather than merely denoising,deblurring,and edge clarification.The trained GAN-based segmentation model effectively improves manual multi-mineral segmentation results,resulting in a strong resemblance to real samples in terms of pore size distribution and permeability.This framework significantly improves the characterization of complex shale microstructures and can be expanded to other heterogeneous porous media,such as carbonate,coal,and tight sandstone reservoirs.展开更多
As in-situ observations are sparse,targeted observations of a specific mesoscale eddy are rare.Therefore,it is difficult to study the three-dimensional structure of moving mesoscale eddies.From April to September 2014...As in-situ observations are sparse,targeted observations of a specific mesoscale eddy are rare.Therefore,it is difficult to study the three-dimensional structure of moving mesoscale eddies.From April to September 2014,an anticyclonic eddy located at 135°E-155°E,26°N-42°N was observed using 17 rapidsampling Argo floats,and the spatiotemporal variations in the three-dimensional structure were studied.The results are as follows:(1)the eddy was identified and tracked using satellite altimeter data.It had a lifetime of 269 days and an average radius of 91.5 km.The lifetime of the eddy can be divided into three phases,i.e.,the initiation,maturity,and termination phases.The depth of its influence reached 1000 m;(2)the Argo profiles were divided into seven periods(approximately 20 days in each)for composite analysis,and the composite Argo profiles and CARS2009(CSIRO Atlas of Regional Seas)climatology data were merged following the data-interpolating variational analysis(DIVA)method to reconstruct the three-dimensional structure.The temperature and salinity anomaly cores of the anticyclonic mesoscale eddy are located from 400 to 600 m.From 800 to 900 m,there is an area of low salinity at the center of the eddy.A high concentration anomaly of dissolved oxygen was located at approximately 250 m;(3)to better understand the features of the eddy and its interaction with the surroundings,we calculated the anomalous velocity of the geostrophic flow and the heat,salt,dissolved oxygen transport anomaly,and discussed the eddy's origin and its adjustments to topography.The maximum heat,salt,and oxygen transport caused by eddy were 9.37×10^11 W,3.08×10^3 kg/s,and 2.70×10^2 kg/s,which all occurred during the termination phase.This study highlights the applicability of using Argo floats to understand the three-dimensional structure thermohaline features of eddies in the North Pacific.展开更多
To assess the high-temperature creep properties of titanium matrix composites for aircraft skin,the TA15 alloy,TiB/TA15 and TiB/(TA15−Si)composites with network structure were fabricated using low-energy milling and v...To assess the high-temperature creep properties of titanium matrix composites for aircraft skin,the TA15 alloy,TiB/TA15 and TiB/(TA15−Si)composites with network structure were fabricated using low-energy milling and vacuum hot pressing sintering techniques.The results show that introducing TiB and Si can reduce the steady-state creep rate by an order of magnitude at 600℃ compared to the alloy.However,the beneficial effect of Si can be maintained at 700℃ while the positive effect of TiB gradually diminishes due to the pores near TiB and interface debonding.The creep deformation mechanism of the as-sintered TiB/(TA15−Si)composite is primarily governed by dislocation climbing.The high creep resistance at 600℃ can be mainly attributed to the absence of grain boundaryαphases,load transfer by TiB whisker,and the hindrance of dislocation movement by silicides.The low steady-state creep rate at 700℃ is mainly resulted from the elimination of grain boundaryαphases as well as increased dynamic precipitation of silicides andα_(2).展开更多
The three-dimensional structure and the seasonal variation of the North Pacific meridional overturning circulation （NPMOC） are analyzed based on the Simple Ocean Data Assimilation data and Argo profiling float data....The three-dimensional structure and the seasonal variation of the North Pacific meridional overturning circulation （NPMOC） are analyzed based on the Simple Ocean Data Assimilation data and Argo profiling float data. The NPMOC displays a multi-cell structure with four cells in the North Pacific altogether. The TC and the STC are a strong clockwise meridional cell in the low latitude ocean and a weaker clockwise meridional cell between 7°N and 18°N, respectively, while the DTC and the subpolar cell are a weaker anticlockwise meridional cell between 3°N and 15°N and a weakest anticlockwise meridional cell between 35°N and 50°N, respectively. The DTC, the TC and the STC are all of very strong seasonal variations. As to the DTC, the southward transport is strongest in fall and weakest in spring. For the TC, the northward transport is strongest in winter and weakest in spring, while the southward transport is strongest in fall and weakest in spring, which is associated with the strong southward fiow of the DTC in fall. As the STC, the northward transport is strongest in winter and weakest in summer, while the southward transport is strongest in summer and weakest in spring. This seasonal difference may be associated with the DTC. The zonal wind stress and the east-west slope of sea level play important roles in the seasonal variations of the TC, the STC and the DTC.展开更多
Background Post-stroke depression(PSD)is a common neuropsychiatric problem associated with a high disease burden and reduced quality of life(QoL).To date,few studies have examined the network structure of depressive s...Background Post-stroke depression(PSD)is a common neuropsychiatric problem associated with a high disease burden and reduced quality of life(QoL).To date,few studies have examined the network structure of depressive symptoms and their relationships with QoL in stroke survivors.Aims This study aimed to explore the network structure of depressive symptoms in PSD and investigate the interrelationships between specific depressive symptoms and QoL among older stroke survivors.Methods This study was based on the 2017–2018 collection of data from a large national survey in China.Depressive symptoms were assessed using the 10-item Centre for Epidemiological Studies Depression Scale(CESD),while QoL was measured with the World Health Organization Quality of Life-brief version.Network analysis was employed to explore the structure of PSD,using expected influence(EI)to identify the most central symptoms and the flow function to investigate the association between depressive symptoms and QoL.Results A total of 1123 stroke survivors were included,with an overall prevalence of depression of 34.3%(n=385;95%confidence interval 31.5%to 37.2%).In the network model of depression,the most central symptoms were CESD3(‘feeling blue/depressed’,EI:1.180),CESD6(‘feeling nervous/fearful’,EI:0.864)and CESD8(‘loneliness’,EI:0.843).In addition,CESD5(‘hopelessness’,EI:−0.195),CESD10(‘sleep disturbances’,EI:−0.169)and CESD4(‘everything was an effort’,EI:−0.150)had strong negative associations with QoL.Conclusion This study found that PSD was common among older Chinese stroke survivors.Given its negative impact on QoL,appropriate interventions targeting central symptoms and those associated with QoL should be developed and implemented for stroke survivors with PSD.展开更多
In order to obtain high-performance electromagnetic wave absorbers,the adjustment of structure and components is essential.Based on the above requirements,this system forms a three-dimensional frame structure consisti...In order to obtain high-performance electromagnetic wave absorbers,the adjustment of structure and components is essential.Based on the above requirements,this system forms a three-dimensional frame structure consisting of MXene and transition metal oxides(TMOs)through efficient electrostatic self-assembly.This three-dimensional network structure has rich heterojunction structures,which can cause a large amount of interface polarization and conduction losses in incident electromagnetic waves.Hollow structures cause multiple reflections and scattering of electromagnetic waves,which is also an important reason for further increasing electromagnetic wave losses.When the doping ratio is 1:1,the system has the best impedance matching,the maximum effective absorption bandwidth(EAB max)can reach 5.12 GHz at 1.7 mm,and the minimum reflection loss(RL_(min))is-50.30 dB at 1.8 mm.This provides a reference for the subsequent formation of 2D-MXene materials into 3D materials.展开更多
基金financially supported by the National Natural Science Foundation of China(Nos.21876164 and U2030203)A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions。
文摘To address the poor mechanical properties of polydimethylsiloxane(PDMS)and enhance the understanding of the reinforcement mechanisms of aerogel network structures in rubber matrices,this study reinforced PDMS using an ordered interconnected three-dimensional montmorillonite(MMT)aerogel network.The average pore diameter of the aerogels was successfully reduced from 11.53μm to 2.51μm by adjusting the ratio of poly(vinyl alcohol)(PVA)to MMT via directional freezing.Changes in the aerogel network were observed in field emission scanning electron microscope(FESEM)images.After vacuum impregnation,the aerogel network structure of the composites was observed using FESEM.Tensile tests indicated that as the pore diameter decreased,the elongation at break of the composites first increased to a peak of329.61%before decreasing,while the tensile strength and Young's modulus continuously increased to their maximum values of 6.29 MPa and24.67 MPa,respectively.Meanwhile,FESEM images of the tensile cracks and fracture surfaces showed that with a reduction in aerogel pore diameter,the degrees of crack deflection and interfacial debonding increased,presenting a rougher fracture surface.These phenomena enable the composites to dissipate substantial energy during tension,thus effectively improving the mechanical strength of the composites.The present work elucidates the bearing of ordered three-dimensional aerogel network structures on the performance of rubber matrices and provides crucial theoretical insights and technical guidance for the creation and optimization of high-performance PDMS-based composites.
基金Project supported by the National Natural Science Foun-dation of China(Grant No.62373197)the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province,China(Grant No.23KJB120010)+1 种基金the Industry-University-Research Cooperation Project of Jiangsu Province,China(Grant No.BY20251038)the Cultivation and In-cubation Project of the College of Automation,Nanjing Uni-versity of Posts and Telecommunications.
文摘Multilayer complex dynamical networks,characterized by the intricate topological connections and diverse hierarchical structures,present significant challenges in determining complete structural configurations due to the unique functional attributes and interaction patterns inherent to different layers.This paper addresses the critical question of whether structural information from a known layer can be used to reconstruct the unknown intralayer structure of a target layer within general weighted output-coupling multilayer networks.Building upon the generalized synchronization principle,we propose an innovative reconstruction method that incorporates two essential components in the design of structure observers,the cross-layer coupling modulator and the structural divergence term.A key advantage of the proposed reconstruction method lies in its flexibility to freely designate both the unknown target layer and the known reference layer from the general weighted output-coupling multilayer network.The reduced dependency on full-state observability enables more deployment in engineering applications with partial measurements.Numerical simulations are conducted to validate the effectiveness of the proposed structure reconstruction method.
基金supported by the National Natural Science Foundation of China(Nos.12422207 and 12372199).
文摘An efficient data-driven numerical framework is developed for transient heat conduction analysis in thin-walled structures.The proposed approach integrates spectral time discretization with neural network approximation,forming a spectral-integrated neural network(SINN)scheme tailored for problems characterized by long-time evolution.Temporal derivatives are treated through a spectral integration strategy based on orthogonal polynomial expansions,which significantly alleviates stability constraints associated with conventional time-marching schemes.A fully connected neural network is employed to approximate the temperature-related variables,while governing equa-tions and boundary conditions are enforced through a physics-informed loss formulation.Numerical investigations demonstrate that the proposed method maintains high accuracy even when large time steps are adopted,where standard numerical solvers often suffer from instability or excessive computational cost.Moreover,the framework exhibits strong robustness for ultrathin configurations with extreme aspect ratios,achieving relative errors on the order of 10−5 or lower.These results indicate that the SINN framework provides a reliable and efficient alternative for transient thermal analysis of thin-walled structures under challenging computational conditions.
基金support of the Fundamental Research Funds for the Central Universities(No.E2ET0411X2).
文摘In recent years,research investigations have focused on the substantial freshwater storage in the Beaufort Gyre(BG)region due to climate change.Despite active mesoscale eddies in the area,a notable gap in understanding the three-dimensional structure and induced transport has been observed.This study concentrates on the Canada Basin in the western Arctic Ocean,specifically examining a subsurface anticyclonic eddy(SAE)sampled by a Mooring A in the BG region.Hybrid Coordinate Ocean Model(HYCOM)analysis data reveal its lifecycle from February 15 to March 15,2017,marked by initiation,development,maturity,decay,and termination stages.This work extends the finding of SAE passing through Mooring A by examining its overall effects,spatiotemporal variations,and swirl transport.SAE generation through baroclinic instability,which contributes to the westward tilt of the vertical axis,is also confirmed in this study.Swirl transport induced by SAE is predominantly eastward and downward due to its trajectory and background flow.SAE temporarily weakens stratification and extends the subsurface depth but demonstrates transient effects.Moreover,SAE transports upper-layer freshwater,Pacific Winter Water,and Atlantic Water downward,emphasizing its potential influence on freshwater redistribution in the Canadian Basin.This research provides valuable insights into mesoscale eddy dynamics,revealing their role in modulating the upper water mass in the BG region.
文摘A zinc complex, [Zn(iso)_2(H_2O)_4](iso=C_6H_4NO_2^-), was synthesized and characterized by elemental analysis, thermal analysis and IR spectrum studies. The crystal structure of the complex was determined by X-ray diffraction. The crystal crystallizes in the triclinic system, molecular formula ZnC12H16N2O8, Mr=381.64, space group P with a = 6.338(1), b =6.919(1), c=9.277(1), α=96.28(1), β=104.91(1), γ=112.85(1)°, V=352.12(9)?3, Z=1, Dc=1.80g?cm-3 and F(000)=196, μ =1.791mm-1. The crystal structure was solved by direct methods for final R=0.0204 and Rw=0.0542 for 1258 observed reflections with [Fo>4σ(Fo)]. The crystal structure reveals that zinc ion is trans-octahedral with two pyridyl nitrogens and two aque oxygens at the equational positions and two aqua oxygens at the axial positions. The complex forms a three-dimensional network through intermolecular hydrogen bonds.
基金This work was financially supported by the Natural Science Foundation of Shandong Province, China (Y2006F03).
文摘Reticulated polyurethane was chosen as the preceramic material for preparing the porous preform using the replication process. The immersing and sintering processes were each performed twice for fabricating a high-porosity and super-strong skeleton. The aluminum magnesium matrix composites reinforced with three-dimensional network structure were prepared using the infiltration technique by pressure assisting and vacuum driving. Light interfacial reactions have played a profitable role in most of the ceramic-metal systems. The metal matrix composites interpenetrated with the ceramic phase have a higher wear resistance than the metal matrix phase. The volume fraction of ceramic reinforcement has a significant effect on the abrasive wear, and the wear rate can be decreased with the increase of the volume fraction of reinforcement.
基金financially supported by National Natural Science Foundation of China(Nos.22379056,52102100)Industry foresight and common key technology research in Carbon Peak and Carbon Neutrality Special Project from Zhenjiang city(No.CG2023003)Research and Practice Innovation Plan of Postgraduate Training Innovation Project in Jiangsu Province(No.SJCX23_2164)。
文摘To solve the volume expansion and poor electrical conductivity of germanium-based anode materials,Ge/rGO/CNTs nanocomposites with three-dimensional network structure are fabricated through the dispersion of polyethylene-polypropylene glycol(F127)and reduction of hydrogen.An interesting phenomenon is discovered that F127 can break GeO_(2)polycrystalline microparticles into 100 nm nanoparticles by only physical interaction,which promotes the uniform dispersion of GeO_(2)in a carbon network structure composed of graphene(rGO)and carbon nanotubes(CNTs).As evaluated as anode material of Lithium-ion batteries,Ge/rGO/CNTs nanocomposites exhibit excellent lithium storage performance.The initial specific capacity is high to 1549.7 mAh/g at 0.2 A/g,and the reversible capacity still retains972.4 mAh/g after 100 cycles.The improved lithium storage performance is attributed to that Ge nanoparticles can effectively slow down the volume expansion during charge and discharge processes,and threedimensional carbon networks can improve electrical conductivity and accelerate lithium-ion transfer of anode materials.
基金supported by Natural Science Foundation of Qinghai Province(2025-ZJ-994M)Scientific Research Innovation Capability Support Project for Young Faculty(SRICSPYF-BS2025007)National Natural Science Foundation of China(62566050).
文摘Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data collection process,resulting in temporal misalignment or displacement.Due to these factors,the node representations carry substantial noise,which reduces the adaptability of the multivariate coupled network structure and subsequently degrades anomaly detection performance.Accordingly,this study proposes a novel multivariate anomaly detection model grounded in graph structure learning.Firstly,a recommendation strategy is employed to identify strongly coupled variable pairs,which are then used to construct a recommendation-driven multivariate coupling network.Secondly,a multi-channel graph encoding layer is used to dynamically optimize the structural properties of the multivariate coupling network,while a multi-head attention mechanism enhances the spatial characteristics of the multivariate data.Finally,unsupervised anomaly detection is conducted using a dynamic threshold selection algorithm.Experimental results demonstrate that effectively integrating the structural and spatial features of multivariate data significantly mitigates anomalies caused by temporal dependency misalignment.
基金Supported by the National Key Research Program(No.2024-1129-954-112)National Natural Science Foundation of China(No.52372033)Guangxi Science and Technology Major Program(No.AA24263054)。
文摘Alkali-free SiO_(2)-Al_(2)O_(3)-CaO-MgO with different SiO_(2)/Al_(2)O_(3)mass ratios was prepared by conventional melt quenching method.The glass network structure,thermodynamic properties and elastic modulus changes with SiO_(2)and Al_(2)O_(3)ratios were investigated using various techniques.It is found that when SiO_(2)is replaced by Al_(2)O_(3),the Q^(4) to Q^(3) transition of silicon-oxygen network decreases while the aluminum-oxygen network increases,which result in the transformation of Si-O-Si bonds to Si-O-Al bonds and an increase in glass network connectivity even though the intermolecular bond strength decreases.The glass transition temperature(T_(g))increases continuously,while the thermal expansion coefficient increases and high-temperature viscosity first decreases and then increases.Meanwhile,the elastic modulus values increase from 93 to 102 GPa.This indicates that the elastic modulus is mainly affected by packing factor and dissociation energy,and elements with higher packing factors and dissociation energies supplant those with lower values,resulting in increased rigidity within the glass.
基金supported by the National Natural Science Foundation of China(No.52308332)the General Scientific Research Project of the Education Department of Zhejiang Province(No.Y202455824).
文摘This research centers on structural health monitoring of bridges,a critical transportation infrastructure.Owing to the cumulative action of heavy vehicle loads,environmental variations,and material aging,bridge components are prone to cracks and other defects,severely compromising structural safety and service life.Traditional inspection methods relying on manual visual assessment or vehicle-mounted sensors suffer from low efficiency,strong subjectivity,and high costs,while conventional image processing techniques and early deep learning models(e.g.,UNet,Faster R-CNN)still performinadequately in complex environments(e.g.,varying illumination,noise,false cracks)due to poor perception of fine cracks andmulti-scale features,limiting practical application.To address these challenges,this paper proposes CACNN-Net(CBAM-Augmented CNN),a novel dual-encoder architecture that innovatively couples a CNN for local detail extraction with a CBAM-Transformer for global context modeling.A key contribution is the dedicated Feature FusionModule(FFM),which strategically integratesmulti-scale features and focuses attention on crack regions while suppressing irrelevant noise.Experiments on bridge crack datasets demonstrate that CACNNNet achieves a precision of 77.6%,a recall of 79.4%,and an mIoU of 62.7%.These results significantly outperform several typical models(e.g.,UNet-ResNet34,Deeplabv3),confirming their superior accuracy and robust generalization,providing a high-precision automated solution for bridge crack detection and a novel network design paradigm for structural surface defect identification in complex scenarios,while future research may integrate physical features like depth information to advance intelligent infrastructure maintenance and digital twin management.
基金funded by National Research Council of Thailand(contract No.N42A671047).
文摘Physics-informed neural networks(PINNs)have emerged as a promising class of scientific machine learning techniques that integrate governing physical laws into neural network training.Their ability to enforce differential equations,constitutive relations,and boundary conditions within the loss function provides a physically grounded alternative to traditional data-driven models,particularly for solid and structural mechanics,where data are often limited or noisy.This review offers a comprehensive assessment of recent developments in PINNs,combining bibliometric analysis,theoretical foundations,application-oriented insights,and methodological innovations.A biblio-metric survey indicates a rapid increase in publications on PINNs since 2018,with prominent research clusters focused on numerical methods,structural analysis,and forecasting.Building upon this trend,the review consolidates advance-ments across five principal application domains,including forward structural analysis,inverse modeling and parameter identification,structural and topology optimization,assessment of structural integrity,and manufacturing processes.These applications are propelled by substantial methodological advancements,encompassing rigorous enforcement of boundary conditions,modified loss functions,adaptive training,domain decomposition strategies,multi-fidelity and transfer learning approaches,as well as hybrid finite element–PINN integration.These advances address recurring challenges in solid mechanics,such as high-order governing equations,material heterogeneity,complex geometries,localized phenomena,and limited experimental data.Despite remaining challenges in computational cost,scalability,and experimental validation,PINNs are increasingly evolving into specialized,physics-aware tools for practical solid and structural mechanics applications.
文摘This article aims to develop a head pursuit （HP） guidance law for three-dimensional hypervelocity interception, so that the effect of the perturbation induced by seeker detection can be reduced. On the basis of a novel HP three-dimensional guidance model, a nonlinear variable structure guidance law is presented by using Lyapunov stability theory. The guidance law positions the interceptor ahead of the target on its tlight trajectory, and the speed of the interceptor is required to be lower than that of the target, A numerical example of maneuvering ballistic target interception verifies the rightness of the guidance model and the effectiveness of the proposed method.
基金The authors wish to acknowledge financial support from the National Natural Science Foundation of China(51822407 and 51774327)Natural Science Foundation of Hunan Province in China(2018JJ1037)Innovation Driven project of Central South University(2020CX014).
文摘Microseismic/acoustic emission(MS/AE)source localization method is crucial for predicting and controlling of potentially dangerous sources of complex structures.However,the locating errors induced by both the irregular structure and pre-measured velocity are poorly understood in existing methods.To meet the high-accuracy locating requirements in complex three-dimensional hole-containing structures,a velocity-free MS/AE source location method is developed in this paper.It avoids manual repetitive training by using equidistant grid points to search the path,which introduces A*search algorithm and uses grid points to accommodate complex structures with irregular holes.It also takes advantage of the velocity-free source location method.To verify the validity of the proposed method,lead-breaking tests were performed on a cubic concrete test specimen with a size of 10 cm10 cm10 cm.It was cut out into a cylindrical empty space with a size of/6cm10 cm.Based on the arrivals,the classical Geiger method and the proposed method are used to locate lead-breaking sources.Results show that the locating error of the proposed method is 1.20 cm,which is less than 2.02 cm of the Geiger method.Hence,the proposed method can effectively locate sources in the complex three-dimensional structure with holes and achieve higher precision requirements.
基金supported by the National Natural Science Foundation of China(No.22373024,22463006,and 52463015)the joint fund between the Gansu Provincial Science and Technology Plan Project(Natural Science Foundation)(No.23JRRA794)the Open Research Fund of the Songshan Lake Materials Laboratory(No.2023SLABFK11)。
文摘The performance of polymer networks is directly determined by their structure.Understanding the network structure offers insights into optimizing material performance,such as elasticity,toughness,and swelling behavior.Herein,in this study we introduce the Dijkstra algorithm from graph theory to characterize polymer networks based on star-shaped multi-armed precursors by employing coarse-grained molecular dynamics simulations coupled with stochastic reaction model.Our research focuses on the structure characteristics of the generated networks,including the number and size of loops,as well as network dispersity characterized by loops.Tracking the number of loops during network generation allows for the identification of the gel point.The size distribution of loops in the network is primarily related to the functionality of the precursors,and the system with fewer precursor arms exhibiting larger average loop sizes.Strain-stress curves indicate that materials with identical functionality and precursor arm lengths generally exhibit superior performance.This method of characterizing network structures helps to refine microscopic structural analysis and contributes to the enhancement and optimization of material properties.
基金Supported by the National Natural Science Foundation of China(U23A20595,52034010,52288101)National Key Research and Development Program of China(2022YFE0203400)+1 种基金Shandong Provincial Natural Science Foundation(ZR2024ZD17)Fundamental Research Funds for the Central Universities(23CX10004A).
文摘Existing imaging techniques cannot simultaneously achieve high resolution and a wide field of view,and manual multi-mineral segmentation in shale lacks precision.To address these limitations,we propose a comprehensive framework based on generative adversarial network(GAN)for characterizing pore structure properties of shale,which incorporates image augmentation,super-resolution reconstruction,and multi-mineral auto-segmentation.Using real 2D and 3D shale images,the framework was assessed through correlation function,entropy,porosity,pore size distribution,and permeability.The application results show that this framework enables the enhancement of 3D low-resolution digital cores by a scale factor of 8,without paired shale images,effectively reconstructing the unresolved fine-scale pores under a low resolution,rather than merely denoising,deblurring,and edge clarification.The trained GAN-based segmentation model effectively improves manual multi-mineral segmentation results,resulting in a strong resemblance to real samples in terms of pore size distribution and permeability.This framework significantly improves the characterization of complex shale microstructures and can be expanded to other heterogeneous porous media,such as carbonate,coal,and tight sandstone reservoirs.
基金Supported by the National Key R&D Program of China(No.2018YFC1406202)the National Natural Science Foundation of China(Nos.41830964,41976188,41605051)。
文摘As in-situ observations are sparse,targeted observations of a specific mesoscale eddy are rare.Therefore,it is difficult to study the three-dimensional structure of moving mesoscale eddies.From April to September 2014,an anticyclonic eddy located at 135°E-155°E,26°N-42°N was observed using 17 rapidsampling Argo floats,and the spatiotemporal variations in the three-dimensional structure were studied.The results are as follows:(1)the eddy was identified and tracked using satellite altimeter data.It had a lifetime of 269 days and an average radius of 91.5 km.The lifetime of the eddy can be divided into three phases,i.e.,the initiation,maturity,and termination phases.The depth of its influence reached 1000 m;(2)the Argo profiles were divided into seven periods(approximately 20 days in each)for composite analysis,and the composite Argo profiles and CARS2009(CSIRO Atlas of Regional Seas)climatology data were merged following the data-interpolating variational analysis(DIVA)method to reconstruct the three-dimensional structure.The temperature and salinity anomaly cores of the anticyclonic mesoscale eddy are located from 400 to 600 m.From 800 to 900 m,there is an area of low salinity at the center of the eddy.A high concentration anomaly of dissolved oxygen was located at approximately 250 m;(3)to better understand the features of the eddy and its interaction with the surroundings,we calculated the anomalous velocity of the geostrophic flow and the heat,salt,dissolved oxygen transport anomaly,and discussed the eddy's origin and its adjustments to topography.The maximum heat,salt,and oxygen transport caused by eddy were 9.37×10^11 W,3.08×10^3 kg/s,and 2.70×10^2 kg/s,which all occurred during the termination phase.This study highlights the applicability of using Argo floats to understand the three-dimensional structure thermohaline features of eddies in the North Pacific.
基金financially supported by the National Key R&D Program of China(No.2022YFB3707405)the National Natural Science Foundation of China(Nos.U22A20113,52171137,52071116)+1 种基金Heilongjiang Provincial Natural Science Foundation,China(No.TD2020E001)Heilongjiang Touyan Team Program,China.
文摘To assess the high-temperature creep properties of titanium matrix composites for aircraft skin,the TA15 alloy,TiB/TA15 and TiB/(TA15−Si)composites with network structure were fabricated using low-energy milling and vacuum hot pressing sintering techniques.The results show that introducing TiB and Si can reduce the steady-state creep rate by an order of magnitude at 600℃ compared to the alloy.However,the beneficial effect of Si can be maintained at 700℃ while the positive effect of TiB gradually diminishes due to the pores near TiB and interface debonding.The creep deformation mechanism of the as-sintered TiB/(TA15−Si)composite is primarily governed by dislocation climbing.The high creep resistance at 600℃ can be mainly attributed to the absence of grain boundaryαphases,load transfer by TiB whisker,and the hindrance of dislocation movement by silicides.The low steady-state creep rate at 700℃ is mainly resulted from the elimination of grain boundaryαphases as well as increased dynamic precipitation of silicides andα_(2).
基金Supported by the National Basic Research Development Program of China(973 Program)under contract Nos 2007CB816002,2007CB816005the innovative key project of Chinese Academy of Sciences under contract No.KZCXZ-YW-201
文摘The three-dimensional structure and the seasonal variation of the North Pacific meridional overturning circulation （NPMOC） are analyzed based on the Simple Ocean Data Assimilation data and Argo profiling float data. The NPMOC displays a multi-cell structure with four cells in the North Pacific altogether. The TC and the STC are a strong clockwise meridional cell in the low latitude ocean and a weaker clockwise meridional cell between 7°N and 18°N, respectively, while the DTC and the subpolar cell are a weaker anticlockwise meridional cell between 3°N and 15°N and a weakest anticlockwise meridional cell between 35°N and 50°N, respectively. The DTC, the TC and the STC are all of very strong seasonal variations. As to the DTC, the southward transport is strongest in fall and weakest in spring. For the TC, the northward transport is strongest in winter and weakest in spring, while the southward transport is strongest in fall and weakest in spring, which is associated with the strong southward fiow of the DTC in fall. As the STC, the northward transport is strongest in winter and weakest in summer, while the southward transport is strongest in summer and weakest in spring. This seasonal difference may be associated with the DTC. The zonal wind stress and the east-west slope of sea level play important roles in the seasonal variations of the TC, the STC and the DTC.
基金supported by Beijing High Level Public Health Technology Talent Construction Project(Discipline Backbone-01-028)the Beijing Municipal Science&Technology Commission(No.Z181100001518005)+2 种基金the Capital's Funds for Health Improvement and Research(CFH 2024-2-1174)the University of Macao(MYRG-GRG2023-00141-FHS,CPG2025-00021-FHS)the Science and Technology Plan Foundation of Guangzhou(No.202201011663).
文摘Background Post-stroke depression(PSD)is a common neuropsychiatric problem associated with a high disease burden and reduced quality of life(QoL).To date,few studies have examined the network structure of depressive symptoms and their relationships with QoL in stroke survivors.Aims This study aimed to explore the network structure of depressive symptoms in PSD and investigate the interrelationships between specific depressive symptoms and QoL among older stroke survivors.Methods This study was based on the 2017–2018 collection of data from a large national survey in China.Depressive symptoms were assessed using the 10-item Centre for Epidemiological Studies Depression Scale(CESD),while QoL was measured with the World Health Organization Quality of Life-brief version.Network analysis was employed to explore the structure of PSD,using expected influence(EI)to identify the most central symptoms and the flow function to investigate the association between depressive symptoms and QoL.Results A total of 1123 stroke survivors were included,with an overall prevalence of depression of 34.3%(n=385;95%confidence interval 31.5%to 37.2%).In the network model of depression,the most central symptoms were CESD3(‘feeling blue/depressed’,EI:1.180),CESD6(‘feeling nervous/fearful’,EI:0.864)and CESD8(‘loneliness’,EI:0.843).In addition,CESD5(‘hopelessness’,EI:−0.195),CESD10(‘sleep disturbances’,EI:−0.169)and CESD4(‘everything was an effort’,EI:−0.150)had strong negative associations with QoL.Conclusion This study found that PSD was common among older Chinese stroke survivors.Given its negative impact on QoL,appropriate interventions targeting central symptoms and those associated with QoL should be developed and implemented for stroke survivors with PSD.
基金supported by the National Natural Science Foundation of China(Nos.51407134,52002196)Natural Science Foundation of Shandong Province(Nos.ZR2019YQ24,ZR2020QF084)+1 种基金Taishan Scholars and Young Experts Program of Shandong Province(No.tsqn202103057)the Qingchuang Talents Induction Program of Shandong Higher Education Institution(Research and Innovation Team of Structural-Functional Polymer Composites)and Special Financial of Shandong Province(Structural Design of High-efficiency Electromagnetic Wave-absorbing Composite Materials and Construction of Shandong Provincial Talent Teams(No.37000022P990304116449)).
文摘In order to obtain high-performance electromagnetic wave absorbers,the adjustment of structure and components is essential.Based on the above requirements,this system forms a three-dimensional frame structure consisting of MXene and transition metal oxides(TMOs)through efficient electrostatic self-assembly.This three-dimensional network structure has rich heterojunction structures,which can cause a large amount of interface polarization and conduction losses in incident electromagnetic waves.Hollow structures cause multiple reflections and scattering of electromagnetic waves,which is also an important reason for further increasing electromagnetic wave losses.When the doping ratio is 1:1,the system has the best impedance matching,the maximum effective absorption bandwidth(EAB max)can reach 5.12 GHz at 1.7 mm,and the minimum reflection loss(RL_(min))is-50.30 dB at 1.8 mm.This provides a reference for the subsequent formation of 2D-MXene materials into 3D materials.
