Traditional two-dimensional（2D） complex resistivity forward modeling is based on Poisson＇s equation but spectral induced polarization（SIP） data are the coproducts of the induced polarization（IP） and the electro...Traditional two-dimensional（2D） complex resistivity forward modeling is based on Poisson＇s equation but spectral induced polarization（SIP） data are the coproducts of the induced polarization（IP） and the electromagnetic induction（EMI） effects.This is especially true under high frequencies,where the EMI effect can exceed the IP effect.2D inversion that only considers the IP effect reduces the reliability of the inversion data.In this paper,we derive differential equations using Maxwell＇s equations.With the introduction of the Cole-Cole model,we use the finite-element method to conduct2 D SIP forward modeling that considers the EMI and IP effects simultaneously.The data-space Occam method,in which different constraints to the model smoothness and parametric boundaries are introduced,is then used to simultaneously obtain the four parameters of the Cole-Cole model using multi-array electric field data.This approach not only improves the stability of the inversion but also significantly reduces the solution ambiguity.To improve the computational efficiency,message passing interface programming was used to accelerate the 2D SIP forward modeling and inversion.Synthetic datasets were tested using both serial and parallel algorithms,and the tests suggest that the proposed parallel algorithm is robust and efficient.展开更多
In this paper,the physics informed neural network(PINN)deep learning method is applied to solve two-dimensional nonlocal equations,including the partial reverse space y-nonlocal Mel'nikov equation,the partial reve...In this paper,the physics informed neural network(PINN)deep learning method is applied to solve two-dimensional nonlocal equations,including the partial reverse space y-nonlocal Mel'nikov equation,the partial reverse space-time nonlocal Mel'nikov equation and the nonlocal twodimensional nonlinear Schr?dinger(NLS)equation.By the PINN method,we successfully derive a data-driven two soliton solution,lump solution and rogue wave solution.Numerical simulation results indicate that the error range between the data-driven solution and the exact solution is relatively small,which verifies the effectiveness of the PINN deep learning method for solving high dimensional nonlocal equations.Moreover,the parameter discovery of the partial reverse space-time nonlocal Mel'nikov equation is analysed in terms of its soliton solution for the first time.展开更多
Regularization inversion uses constraints and a regularization factor to solve ill- posed inversion problems in geophysics. The choice of the regularization factor and of the initial model is critical in regularizatio...Regularization inversion uses constraints and a regularization factor to solve ill- posed inversion problems in geophysics. The choice of the regularization factor and of the initial model is critical in regularization inversion. To deal with these problems, we propose a multiobjective particle swarm inversion （MOPSOI） algorithm to simultaneously minimize the data misfit and model constraints, and obtain a multiobjective inversion solution set without the gradient information of the objective function and the regularization factor. We then choose the optimum solution from the solution set based on the trade-off between data misfit and constraints that substitute for the regularization factor. The inversion of synthetic two-dimensional magnetic data suggests that the MOPSOI algorithm can obtain as many feasible solutions as possible; thus, deeper insights of the inversion process can be gained and more reasonable solutions can be obtained by balancing the data misfit and constraints. The proposed MOPSOI algorithm can deal with the problems of choosing the right regularization factor and the initial model.展开更多
We studied finite-element-method-based two-dimensional frequency-domain acoustic FWI under rugged topography conditions. The exponential attenuation boundary condition suitable for rugged topography is proposed to sol...We studied finite-element-method-based two-dimensional frequency-domain acoustic FWI under rugged topography conditions. The exponential attenuation boundary condition suitable for rugged topography is proposed to solve the cutoff botmdary problem as well as to consider the requirement of using the same subdivision grid in joint multifrequency inversion. The proposed method introduces the attenuation factor, and by adjusting it, acoustic waves are sufficiently attenuated in the attenuation layer to minimize the cutoff boundary effect. Based on the law of exponential attenuation, expressions for computing the attenuation factor and the thickness of attenuation layers are derived for different frequencies. In multifrequency-domain FWI, the conjugate gradient method is used to solve equations in the Gauss-Newton algorithm and thus minimize the computation cost in calculating the Hessian matrix. In addition, the effect of initial model selection and frequency combination on FWI is analyzed. Examples using numerical simulations and FWI calculations are used to verify the efficiency of the proposed method.展开更多
Young's modulus and Poisson's ratio are crucial parameters for reservoir characterization and rock brittleness evaluation.Conventional methods often rely on indirect computation or approximations of the Zoeppr...Young's modulus and Poisson's ratio are crucial parameters for reservoir characterization and rock brittleness evaluation.Conventional methods often rely on indirect computation or approximations of the Zoeppritz equations to estimate Young's modulus,which can introduce cumulative errors and reduce the accuracy of inversion results.To address these issues,this paper introduces the analytical solution of the Zoeppritz equation into the inversion process.The equation is re-derived and expressed in terms of Young's modulus,Poisson's ratio,and density.Within the Bayesian framework,we construct an objective function for the joint inversion of PP and PS waves.Traditional gradient-based algorithms often suffer from low precision and the computational complexity.In this study,we address limitations of conventional approaches related to low precision and complicated code by using Circle chaotic mapping,Levy flights,and Gaussian mutation to optimize the quantum particle swarm optimization(QPSO),named improved quantum particle swarm optimization(IQPSO).The IQPSO demonstrates superior global optimization capabilities.We test the proposed inversion method with both synthetic and field data.The test results demonstrate the proposed method's feasibility and effectiveness,indicating an improvement in inversion accuracy over traditional methods.展开更多
The proliferation of wearable biodevices has boosted the development of soft,innovative,and multifunctional materials for human health monitoring.The integration of wearable sensors with intelligent systems is an over...The proliferation of wearable biodevices has boosted the development of soft,innovative,and multifunctional materials for human health monitoring.The integration of wearable sensors with intelligent systems is an overwhelming tendency,providing powerful tools for remote health monitoring and personal health management.Among many candidates,two-dimensional(2D)materials stand out due to several exotic mechanical,electrical,optical,and chemical properties that can be efficiently integrated into atomic-thin films.While previous reviews on 2D materials for biodevices primarily focus on conventional configurations and materials like graphene,the rapid development of new 2D materials with exotic properties has opened up novel applications,particularly in smart interaction and integrated functionalities.This review aims to consolidate recent progress,highlight the unique advantages of 2D materials,and guide future research by discussing existing challenges and opportunities in applying 2D materials for smart wearable biodevices.We begin with an in-depth analysis of the advantages,sensing mechanisms,and potential applications of 2D materials in wearable biodevice fabrication.Following this,we systematically discuss state-of-the-art biodevices based on 2D materials for monitoring various physiological signals within the human body.Special attention is given to showcasing the integration of multi-functionality in 2D smart devices,mainly including self-power supply,integrated diagnosis/treatment,and human–machine interaction.Finally,the review concludes with a concise summary of existing challenges and prospective solutions concerning the utilization of2D materials for advanced biodevices.展开更多
Due to the heterogeneity of rock masses and the variability of in situ stress,the traditional linear inversion method is insufficiently accurate to achieve high accuracy of the in situ stress field.To address this cha...Due to the heterogeneity of rock masses and the variability of in situ stress,the traditional linear inversion method is insufficiently accurate to achieve high accuracy of the in situ stress field.To address this challenge,nonlinear stress boundaries for a numerical model are determined through regression analysis of a series of nonlinear coefficient matrices,which are derived from the bubbling method.Considering the randomness and flexibility of the bubbling method,a parametric study is conducted to determine recommended ranges for these parameters,including the standard deviation(σb)of bubble radii,the non-uniform coefficient matrix number(λ)for nonlinear stress boundaries,and the number(m)and positions of in situ stress measurement points.A model case study provides a reference for the selection of these parameters.Additionally,when the nonlinear in situ stress inversion method is employed,stress distortion inevitably occurs near model boundaries,aligning with the Saint Venant's principle.Two strategies are proposed accordingly:employing a systematic reduction of nonlinear coefficients to achieve high inversion accuracy while minimizing significant stress distortion,and excluding regions with severe stress distortion near the model edges while utilizing the central part of the model for subsequent simulations.These two strategies have been successfully implemented in the nonlinear in situ stress inversion of the Xincheng Gold Mine and have achieved higher inversion accuracy than the linear method.Specifically,the linear and nonlinear inversion methods yield root mean square errors(RMSE)of 4.15 and 3.2,and inversion relative errors(δAve)of 22.08%and 17.55%,respectively.Therefore,the nonlinear inversion method outperforms the traditional multiple linear regression method,even in the presence of a systematic reduction in the nonlinear stress boundaries.展开更多
A functional interlayer based on two-dimensional(2D)porous modified vermiculite nanosheets(PVS)was obtained by acid-etching vermiculite nanosheets.The as-obtained 2D porous nanosheets exhibited a high specific surface...A functional interlayer based on two-dimensional(2D)porous modified vermiculite nanosheets(PVS)was obtained by acid-etching vermiculite nanosheets.The as-obtained 2D porous nanosheets exhibited a high specific surface area of 427 m^(2)·g^(-1)and rich surface active sites,which help restrain polysulfides(LiPSs)through good physi-cal and chemical adsorption,while simultaneously accelerating the nucleation and dissolution kinetics of Li_(2)S,effec-tively suppressing the shuttle effect.The assembled lithium-sulfur batteries(LSBs)employing the PVS-based inter-layer delivered a high initial discharge capacity of 1386 mAh·g^(-1)at 0.1C(167.5 mAh·g^(-1)),long-term cycling stabil-ity,and good rate property.展开更多
Lithium-sulfur(Li-S)batteries with high energy density and capacity have garnered significant research attention among various energy storage devices.However,the shuttle effect of polysulfides(LiPSs)remains a major ch...Lithium-sulfur(Li-S)batteries with high energy density and capacity have garnered significant research attention among various energy storage devices.However,the shuttle effect of polysulfides(LiPSs)remains a major challenge for their practical application.The design of battery separators has become a key aspect in addressing the challenge.MXenes,a promising two-dimensional(2D)material,offer exceptional conductivity,large surface area,high mechanical strength,and active sites for surface reactions.When assembled into layered films,MXenes form highly tunable two-dimensional channels ranging from a few angstroms to over 1 nm.These nanoconfined channels are instrumental in facilitating lithium-ion transport while effectively impeding the shuttle effect of LiPSs,which are essential for improving the specific capacity and cyclic stability of Li-S batteries.Substantial progress has been made in developing MXenes-based separators for Li-S batteries,yet there remains a research gap in summarizing advancements from the perspective of interlayer engineering.This entails maintaining the 2D nanochannels of layered MXenes-based separators while modulating the physicochemical environment within the MXenes interlayers through targeted modifications.This review highlights advancements in in situ modification of MXenes and their integration with 0D,1D,and 2D materials to construct laminated nanocomposite separators for Li-S batteries.The future development directions of MXenes-based materials in Li-S energy storage devices are also outlined,to drive further advancements in MXenes for Li-S battery separators.展开更多
In Rayleigh wave exploration,the inversion of dispersion curves is a crucial step for obtaining subsurface stratigraphic information,characterized by its multi-parameter and multi-extremum nature.Local optimization al...In Rayleigh wave exploration,the inversion of dispersion curves is a crucial step for obtaining subsurface stratigraphic information,characterized by its multi-parameter and multi-extremum nature.Local optimization algorithms used in dispersion curve inversion are highly dependent on the initial model and are prone to being trapped in local optima,while classical global optimization algorithms often suffer from slow convergence and low solution accuracy.To address these issues,this study introduces the Osprey Optimization Algorithm(OOA),known for its strong global search and local exploitation capabilities,into the inversion of dispersion curves to enhance inversion performance.In noiseless theoretical models,the OOA demonstrates excellent inversion accuracy and stability,accurately recovering model parameters.Even in noisy models,OOA maintains robust performance,achieving high inversion precision under high-noise conditions.In multimode dispersion curve tests,OOA effectively handles higher modes due to its efficient global and local search capabilities,and the inversion results show high consistency with theoretical values.Field data from the Wyoming region in the United States and a landfill site in Italy further verify the practical applicability of the OOA.Comprehensive test results indicate that the OOA outperforms the Particle Swarm Optimization(PSO)algorithm,providing a highly accurate and reliable inversion strategy for dispersion curve inversion.展开更多
Titanium dioxide(TiO_(2))has been an important protective ingredient in mineral-based sunscreens since the 1990s.However,traditional TiO_(2)nanoparticle formulations have seen little improvement over the past decades ...Titanium dioxide(TiO_(2))has been an important protective ingredient in mineral-based sunscreens since the 1990s.However,traditional TiO_(2)nanoparticle formulations have seen little improvement over the past decades and continue to face persistent challenges related to light transmission,biosafety,and visual appearance.Here,we report the discovery of two-dimensional(2D)TiO_(2),characterized by a micro-sized lateral dimension(~1.6μm)and atomic-scale thickness,which fundamentally resolves these long-standing issues.The 2D structure enables exceptional light management,achieving 80%visible light transparency—rendering it nearly invisible on the skin—while maintaining UV-blocking performance comparable to unmodified rutile TiO_(2)nanoparticles.Its larger lateral size results in a two-orders-of-magnitude reduction in skin penetration(0.96 w/w%),significantly enhancing biosafety.Moreover,the unique layered architecture inherently suppresses the generation of reactive oxygen species(ROS)under sunlight exposure,reducing the ROS generation rate by 50-fold compared to traditional TiO_(2)nanoparticles.Through precise metal element modulation,we further developed the first customizable sunscreen material capable of tuning UV protection ranges and automatically matching diverse skin tones.The 2D TiO_(2)offers a potentially transformative approach to modern sunscreen formulation,combining superior UV protection,enhanced safety and a natural appearance.展开更多
Owing to their rolling friction,two-dimensional piston pumps are highly suitable as power components for electro-hydrostatic actuators(EHAs).These pumps are particularly advantageous for applications requiring high ef...Owing to their rolling friction,two-dimensional piston pumps are highly suitable as power components for electro-hydrostatic actuators(EHAs).These pumps are particularly advantageous for applications requiring high efficiency and reliability.However,the ambiguity surrounding the output flow characteristics of individual two-dimensional pumps poses a significant challenge in achieving precise closed-loop control of the EHA positions.To address this issue,this study established a comprehensive numerical model that included gap leakage to analyze the impact of leakage on the output flow characteristics of a two-dimensional piston pump.The validity of the numerical analysis was indirectly confirmed through meticulous measurements of the leakage and volumetric efficiency,ensuring robust results.The research findings indicated that,at lower pump speeds,leakage significantly affected the output flow rate,leading to potential inefficiencies in the system.Conversely,at higher rotational speeds,the impact of leakage was less pronounced,implying that the influence of leakage on the pump outlet flow must be carefully considered and managed for EHAs to perform position servo control.Additionally,the research demonstrates that two-dimensional motion does not have a unique or additional effect on pump leakage,thus simplifying the design considerations.Finally,the study concluded that maintaining an oil-filled leakage environment is beneficial because it helps reduce the impact of leakage and enhances the overall volumetric efficiency of the pump system.展开更多
Environmental catalysis has been considered one of the important research topics.Some technologies(e.g.,photocatalysis and electrocatalysis)have been intensively developed with the advance of synthetic technologies of...Environmental catalysis has been considered one of the important research topics.Some technologies(e.g.,photocatalysis and electrocatalysis)have been intensively developed with the advance of synthetic technologies of catalytical materials.In 2019,we discussed the development trend of this field,and wrote a roadmap on this topic in Chinese Chemical Letters(30(2019)2065-2088).Nowadays,we discuss it again from a new viewpoint along this road.In this paper,several subtopics are discussed,e.g.,photocatalysis based on titanium dioxide,violet phosphorus,graphitic carbon and covalent organic frameworks,electrocatalysts based on carbon,metal-and covalent-organic framework.Finally,we hope that this roadmap can enrich the development of two-dimensional materials in environmental catalysis with novel understanding,and give useful inspiration to explore new catalysts for practical applications.展开更多
It is a key challenge to prepare two-dimensional diamond(2D-diamond).Herein,we develop a method for synthesizing 2D-diamond by depositing monodisperse tantalum(Ta)atoms onto graphene substrates using a hot-filament ch...It is a key challenge to prepare two-dimensional diamond(2D-diamond).Herein,we develop a method for synthesizing 2D-diamond by depositing monodisperse tantalum(Ta)atoms onto graphene substrates using a hot-filament chemical vapor deposition setup,followed by annealing treatment under different temperatures at ambient pressure.The results indicate that when the annealing temperature increases from 700℃ to 1000℃,the size of the 2D-diamond found in the samples gradually increases from close to 20 nm to around 30 nm.Meanwhile,the size and number of amorphous carbon spheres and Ta-containing compounds between the graphene layers gradually increase.As the annealing temperature continues to rise to 1100℃,a significant aggregation of Ta-containing compounds is observed in the samples,with no diamond structure detected.This further confirms that monodisperse Ta atoms play a key role in graphene phase transition into 2D-diamond.This study provides a novel method for the ambient-pressure phase transition of graphene into 2D-diamond.展开更多
Electron-hole interactions play a crucial role in determining the optoelectronic properties of materials,and in lowdimensional systems this is especially true due to the decrease of screening.In this review,we focus o...Electron-hole interactions play a crucial role in determining the optoelectronic properties of materials,and in lowdimensional systems this is especially true due to the decrease of screening.In this review,we focus on one unique quantum phase induced by the electron-hole interaction in two-dimensional systems,known as“exciton insulators”(EIs).Although this phase of matter has been studied for more than half a century,suitable platforms for its stable realization remain scarce.We provide an overview of the strategies to realize EIs in accessible materials and structures,along with a discussion on some unique properties of EIs stemming from the band structures of these materials.Additionally,signatures in experiments to distinguish EIs are discussed.展开更多
Machine learning(ML)techniques have emerged as powerful tools for improving the predictive capabilities of Reynolds-averaged Navier-Stokes(RANS)turbulence models in separated flows.This improvement is achieved by leve...Machine learning(ML)techniques have emerged as powerful tools for improving the predictive capabilities of Reynolds-averaged Navier-Stokes(RANS)turbulence models in separated flows.This improvement is achieved by leveraging complex ML models,such as those developed using field inversion and machine learning(FIML),to dynamically adjust the constants within the baseline RANS model.However,the ML models often overlook the fundamental calibrations of the RANS turbulence model.Consequently,the basic calibration of the baseline RANS model is disrupted,leading to a degradation in the accuracy,particularly in basic wall-attached flows outside of the training set.To address this issue,a modified version of the Spalart-Allmaras(SA)turbulence model,known as Rubber-band SA(RBSA),has been proposed recently.This modification involves identifying and embedding constraints related to basic wall-attached flows directly into the model.It is shown that no matter how the parameters of the RBSA model are adjusted as constants throughout the flow field,its accuracy in wall-attached flows remains unaffected.In this paper,we propose a new constraint for the RBSA model,which better safeguards the law of wall in extreme conditions where the model parameter is adjusted dramatically.The resultant model is called the RBSA-poly model.We then show that when combined with FIML augmentation,the RBSA-poly model effectively preserves the accuracy of simple wall-attached flows,even when the adjusted parameters become functions of local flow variables rather than constants.A comparative analysis with the FIML-augmented original SA model reveals that the augmented RBSA-poly model reduces error in basic wall-attached flows by 50%while maintaining comparable accuracy in trained separated flows.These findings confirm the effectiveness of utilizing FIML in conjunction with the RBSA model,offering superior accuracy retention in cardinal flows.展开更多
The pursuit of sustainable hydrogen production has positioned water electrolysis as a cornerstone technology for global carbon neutrality.However,sluggish kinetics,catalyst scarcity,and system integration challenges h...The pursuit of sustainable hydrogen production has positioned water electrolysis as a cornerstone technology for global carbon neutrality.However,sluggish kinetics,catalyst scarcity,and system integration challenges hinder its widespread deployment.Ultrathin two-dimensional(2D)materials,with their atomically exposed surfaces,tunable electronic structures,and defect-engineering capabilities,present unique opportunities for next-generation electrocatalysts.This review provides an integrated overview of ultrathin 2D electrocatalysts,discussing their structural diversity,synthetic routes,structure-activity relationships,and mechanistic understanding in water electrolysis processes.Special focus is placed on the translation of 2D materials from laboratory research to practical device implementation,emphasizing challenges such as scalable fabrication,interfacial engineering,and operational durability in realistic electrolyzer environments.The role of advanced characterization techniques in capturing dynamic structural changes and active site evolution is discussed.Finally,we outline future research directions,emphasizing the synergy of machine learning-driven materials discovery,advanced operando characterization,and scalable system integration to accelerate the industrial translation of 2D electrocatalysts for green hydrogen production.展开更多
Deblending is a data processing procedure used to separate the source interferences of blended seismic data,which are obtained by simultaneous sources with random time delays to reduce the cost of seismic acquisition....Deblending is a data processing procedure used to separate the source interferences of blended seismic data,which are obtained by simultaneous sources with random time delays to reduce the cost of seismic acquisition.There are three types of deblending algorithms,i.e.,filtering-type noise suppression algorithm,inversion-based algorithm and deep-learning based algorithm.We review the merits of these techniques,and propose to use a sparse inversion method for seismic data deblending.Filtering-based deblending approach is applicable to blended data with a low blending fold and simple geometry.Otherwise,it can suffer from signal distortion and noise leakage.At present,the deep learning based deblending methods are still under development and field data applications are limited due to the lack of high-quality training labels.In contrast,the inversion-based deblending approaches have gained industrial acceptance.Our used inversion approach transforms the pseudo-deblended data into the frequency-wavenumber-wavenumher(FKK)domain,and a sparse constraint is imposed for the coherent signal estimation.The estimated signal is used to predict the interference noise for subtraction from the original pseudo-deblended data.Via minimizing the data misfit,the signal can be iteratively updated with a shrinking threshold until the signal and interference are fully separated.The used FKK sparse inversion algorithm is very accurate and efficient compared with other sparse inversion methods,and it is widely applied in field cases.Synthetic example shows that the deblending error is less than 1%in average amplitudes and less than-40 dB in amplitude spectra.We present three field data examples of land,marine OBN(Ocean Bottom Nodes)and streamer acquisitions to demonstrate its successful applications in separating the source interferences efficiently and accurately.展开更多
Full waveform inversion methods evaluate the properties of subsurface media by minimizing the misfit between synthetic and observed data.However,these methods omit measurement errors and physical assumptions in modeli...Full waveform inversion methods evaluate the properties of subsurface media by minimizing the misfit between synthetic and observed data.However,these methods omit measurement errors and physical assumptions in modeling,resulting in several problems in practical applications.In particular,full waveform inversion methods are very sensitive to erroneous observations(outliers)that violate the Gauss–Markov theorem.Herein,we propose a method for addressing spurious observations or outliers.Specifically,we remove outliers by inverting the synthetic data using the local convexity of the Gaussian distribution.To achieve this,we apply a waveform-like noise model based on a specific covariance matrix definition.Finally,we build an inversion problem based on the updated data,which is consistent with the wavefield reconstruction inversion method.Overall,we report an alternative optimization inversion problem for data containing outliers.The proposed method is robust because it uses uncertainties.This method enables accurate inversion,even when based on noisy models or a wrong wavelet.展开更多
Vehicle electrification,an important method for reducing carbon emissions from road transport,has been promoted globally.In this study,we analyze how individuals adapt to this transition in transportation and its subs...Vehicle electrification,an important method for reducing carbon emissions from road transport,has been promoted globally.In this study,we analyze how individuals adapt to this transition in transportation and its subsequent impact on urban structure.Considering the varying travel costs associated with electric and fuel vehicles,we analyze the dynamic choices of households concerning house locations and vehicle types in a two-dimensional monocentric city.A spatial equilibrium is developed to model the interactions between urban density,vehicle age and vehicle type.An agent-based microeconomic residential choice model dynamically coupled with a house rent market is developed to analyze household choices of home locations and vehicle energy types,considering vehicle ages and competition for public charging piles.Key findings from our proposed models show that the proportion of electric vehicles(EVs)peaks at over 50%by the end of the first scrappage period,accompanied by more than a 40%increase in commuting distance and time compared to the scenario with only fuel vehicles.Simulation experiments on a theoretical grid indicate that heterogeneity-induced residential segregation can lead to urban sprawl and congestion.Furthermore,households with EVs tend to be located farther from the city center,and an increase in EV ownership contributes to urban expansion.Our study provides insights into how individuals adapt to EV transitions and the resulting impacts on home locations and land use changes.It offers a novel perspective on the dynamic interactions between EV adoption and urban development.展开更多
基金jointly sponsored by the National Natural Science Foundation of China(Grant No.41374078)the Geological Survey Projects of the Ministry of Land and Resources of China(Grant Nos.12120113086100 and 12120113101300)Beijing Higher Education Young Elite Teacher Project
文摘Traditional two-dimensional（2D） complex resistivity forward modeling is based on Poisson＇s equation but spectral induced polarization（SIP） data are the coproducts of the induced polarization（IP） and the electromagnetic induction（EMI） effects.This is especially true under high frequencies,where the EMI effect can exceed the IP effect.2D inversion that only considers the IP effect reduces the reliability of the inversion data.In this paper,we derive differential equations using Maxwell＇s equations.With the introduction of the Cole-Cole model,we use the finite-element method to conduct2 D SIP forward modeling that considers the EMI and IP effects simultaneously.The data-space Occam method,in which different constraints to the model smoothness and parametric boundaries are introduced,is then used to simultaneously obtain the four parameters of the Cole-Cole model using multi-array electric field data.This approach not only improves the stability of the inversion but also significantly reduces the solution ambiguity.To improve the computational efficiency,message passing interface programming was used to accelerate the 2D SIP forward modeling and inversion.Synthetic datasets were tested using both serial and parallel algorithms,and the tests suggest that the proposed parallel algorithm is robust and efficient.
文摘In this paper,the physics informed neural network(PINN)deep learning method is applied to solve two-dimensional nonlocal equations,including the partial reverse space y-nonlocal Mel'nikov equation,the partial reverse space-time nonlocal Mel'nikov equation and the nonlocal twodimensional nonlinear Schr?dinger(NLS)equation.By the PINN method,we successfully derive a data-driven two soliton solution,lump solution and rogue wave solution.Numerical simulation results indicate that the error range between the data-driven solution and the exact solution is relatively small,which verifies the effectiveness of the PINN deep learning method for solving high dimensional nonlocal equations.Moreover,the parameter discovery of the partial reverse space-time nonlocal Mel'nikov equation is analysed in terms of its soliton solution for the first time.
基金supported by the Natural Science Foundation of China(No.61273179)Department of Education,Science and Technology Research Project of Hubei Province of China(No.D20131206,No.20141304)
文摘Regularization inversion uses constraints and a regularization factor to solve ill- posed inversion problems in geophysics. The choice of the regularization factor and of the initial model is critical in regularization inversion. To deal with these problems, we propose a multiobjective particle swarm inversion （MOPSOI） algorithm to simultaneously minimize the data misfit and model constraints, and obtain a multiobjective inversion solution set without the gradient information of the objective function and the regularization factor. We then choose the optimum solution from the solution set based on the trade-off between data misfit and constraints that substitute for the regularization factor. The inversion of synthetic two-dimensional magnetic data suggests that the MOPSOI algorithm can obtain as many feasible solutions as possible; thus, deeper insights of the inversion process can be gained and more reasonable solutions can be obtained by balancing the data misfit and constraints. The proposed MOPSOI algorithm can deal with the problems of choosing the right regularization factor and the initial model.
基金financially supported by the National High Technology Research and Development Program of China(No.2012AA09A20105)the National Science Foundation Network(No.41574127)
文摘We studied finite-element-method-based two-dimensional frequency-domain acoustic FWI under rugged topography conditions. The exponential attenuation boundary condition suitable for rugged topography is proposed to solve the cutoff botmdary problem as well as to consider the requirement of using the same subdivision grid in joint multifrequency inversion. The proposed method introduces the attenuation factor, and by adjusting it, acoustic waves are sufficiently attenuated in the attenuation layer to minimize the cutoff boundary effect. Based on the law of exponential attenuation, expressions for computing the attenuation factor and the thickness of attenuation layers are derived for different frequencies. In multifrequency-domain FWI, the conjugate gradient method is used to solve equations in the Gauss-Newton algorithm and thus minimize the computation cost in calculating the Hessian matrix. In addition, the effect of initial model selection and frequency combination on FWI is analyzed. Examples using numerical simulations and FWI calculations are used to verify the efficiency of the proposed method.
基金supported by Fundamental Research Funds for the Central Universities,CHD300102264715National Key Research and Development Program of China under Grant 2021YFA0716902Natural Science Basic Research Program of Shaanxi 2024JCYBMS-199。
文摘Young's modulus and Poisson's ratio are crucial parameters for reservoir characterization and rock brittleness evaluation.Conventional methods often rely on indirect computation or approximations of the Zoeppritz equations to estimate Young's modulus,which can introduce cumulative errors and reduce the accuracy of inversion results.To address these issues,this paper introduces the analytical solution of the Zoeppritz equation into the inversion process.The equation is re-derived and expressed in terms of Young's modulus,Poisson's ratio,and density.Within the Bayesian framework,we construct an objective function for the joint inversion of PP and PS waves.Traditional gradient-based algorithms often suffer from low precision and the computational complexity.In this study,we address limitations of conventional approaches related to low precision and complicated code by using Circle chaotic mapping,Levy flights,and Gaussian mutation to optimize the quantum particle swarm optimization(QPSO),named improved quantum particle swarm optimization(IQPSO).The IQPSO demonstrates superior global optimization capabilities.We test the proposed inversion method with both synthetic and field data.The test results demonstrate the proposed method's feasibility and effectiveness,indicating an improvement in inversion accuracy over traditional methods.
基金the support from the National Natural Science Foundation of China(22272004,62272041)the Fundamental Research Funds for the Central Universities(YWF-22-L-1256)+1 种基金the National Key R&D Program of China(2023YFC3402600)the Beijing Institute of Technology Research Fund Program for Young Scholars(No.1870011182126)。
文摘The proliferation of wearable biodevices has boosted the development of soft,innovative,and multifunctional materials for human health monitoring.The integration of wearable sensors with intelligent systems is an overwhelming tendency,providing powerful tools for remote health monitoring and personal health management.Among many candidates,two-dimensional(2D)materials stand out due to several exotic mechanical,electrical,optical,and chemical properties that can be efficiently integrated into atomic-thin films.While previous reviews on 2D materials for biodevices primarily focus on conventional configurations and materials like graphene,the rapid development of new 2D materials with exotic properties has opened up novel applications,particularly in smart interaction and integrated functionalities.This review aims to consolidate recent progress,highlight the unique advantages of 2D materials,and guide future research by discussing existing challenges and opportunities in applying 2D materials for smart wearable biodevices.We begin with an in-depth analysis of the advantages,sensing mechanisms,and potential applications of 2D materials in wearable biodevice fabrication.Following this,we systematically discuss state-of-the-art biodevices based on 2D materials for monitoring various physiological signals within the human body.Special attention is given to showcasing the integration of multi-functionality in 2D smart devices,mainly including self-power supply,integrated diagnosis/treatment,and human–machine interaction.Finally,the review concludes with a concise summary of existing challenges and prospective solutions concerning the utilization of2D materials for advanced biodevices.
基金funded by the National Key R&D Program of China(Grant No.2022YFC2903904)the National Natural Science Foundation of China(Grant Nos.51904057 and U1906208).
文摘Due to the heterogeneity of rock masses and the variability of in situ stress,the traditional linear inversion method is insufficiently accurate to achieve high accuracy of the in situ stress field.To address this challenge,nonlinear stress boundaries for a numerical model are determined through regression analysis of a series of nonlinear coefficient matrices,which are derived from the bubbling method.Considering the randomness and flexibility of the bubbling method,a parametric study is conducted to determine recommended ranges for these parameters,including the standard deviation(σb)of bubble radii,the non-uniform coefficient matrix number(λ)for nonlinear stress boundaries,and the number(m)and positions of in situ stress measurement points.A model case study provides a reference for the selection of these parameters.Additionally,when the nonlinear in situ stress inversion method is employed,stress distortion inevitably occurs near model boundaries,aligning with the Saint Venant's principle.Two strategies are proposed accordingly:employing a systematic reduction of nonlinear coefficients to achieve high inversion accuracy while minimizing significant stress distortion,and excluding regions with severe stress distortion near the model edges while utilizing the central part of the model for subsequent simulations.These two strategies have been successfully implemented in the nonlinear in situ stress inversion of the Xincheng Gold Mine and have achieved higher inversion accuracy than the linear method.Specifically,the linear and nonlinear inversion methods yield root mean square errors(RMSE)of 4.15 and 3.2,and inversion relative errors(δAve)of 22.08%and 17.55%,respectively.Therefore,the nonlinear inversion method outperforms the traditional multiple linear regression method,even in the presence of a systematic reduction in the nonlinear stress boundaries.
文摘A functional interlayer based on two-dimensional(2D)porous modified vermiculite nanosheets(PVS)was obtained by acid-etching vermiculite nanosheets.The as-obtained 2D porous nanosheets exhibited a high specific surface area of 427 m^(2)·g^(-1)and rich surface active sites,which help restrain polysulfides(LiPSs)through good physi-cal and chemical adsorption,while simultaneously accelerating the nucleation and dissolution kinetics of Li_(2)S,effec-tively suppressing the shuttle effect.The assembled lithium-sulfur batteries(LSBs)employing the PVS-based inter-layer delivered a high initial discharge capacity of 1386 mAh·g^(-1)at 0.1C(167.5 mAh·g^(-1)),long-term cycling stabil-ity,and good rate property.
基金supported by Beijing Natural Science Foundation(Nos.2232037 and 2242035)the National Natural Science Foundation of China(Nos.22005012,22105012 and 51803183)+1 种基金Chunhui Plan Cooperative Project of Ministry of Education(No.202201298)the China Postdoctoral Science Foundation Funded Project(No.2023M733520).
文摘Lithium-sulfur(Li-S)batteries with high energy density and capacity have garnered significant research attention among various energy storage devices.However,the shuttle effect of polysulfides(LiPSs)remains a major challenge for their practical application.The design of battery separators has become a key aspect in addressing the challenge.MXenes,a promising two-dimensional(2D)material,offer exceptional conductivity,large surface area,high mechanical strength,and active sites for surface reactions.When assembled into layered films,MXenes form highly tunable two-dimensional channels ranging from a few angstroms to over 1 nm.These nanoconfined channels are instrumental in facilitating lithium-ion transport while effectively impeding the shuttle effect of LiPSs,which are essential for improving the specific capacity and cyclic stability of Li-S batteries.Substantial progress has been made in developing MXenes-based separators for Li-S batteries,yet there remains a research gap in summarizing advancements from the perspective of interlayer engineering.This entails maintaining the 2D nanochannels of layered MXenes-based separators while modulating the physicochemical environment within the MXenes interlayers through targeted modifications.This review highlights advancements in in situ modification of MXenes and their integration with 0D,1D,and 2D materials to construct laminated nanocomposite separators for Li-S batteries.The future development directions of MXenes-based materials in Li-S energy storage devices are also outlined,to drive further advancements in MXenes for Li-S battery separators.
基金sponsored by China Geological Survey Project(DD20243193 and DD20230206508).
文摘In Rayleigh wave exploration,the inversion of dispersion curves is a crucial step for obtaining subsurface stratigraphic information,characterized by its multi-parameter and multi-extremum nature.Local optimization algorithms used in dispersion curve inversion are highly dependent on the initial model and are prone to being trapped in local optima,while classical global optimization algorithms often suffer from slow convergence and low solution accuracy.To address these issues,this study introduces the Osprey Optimization Algorithm(OOA),known for its strong global search and local exploitation capabilities,into the inversion of dispersion curves to enhance inversion performance.In noiseless theoretical models,the OOA demonstrates excellent inversion accuracy and stability,accurately recovering model parameters.Even in noisy models,OOA maintains robust performance,achieving high inversion precision under high-noise conditions.In multimode dispersion curve tests,OOA effectively handles higher modes due to its efficient global and local search capabilities,and the inversion results show high consistency with theoretical values.Field data from the Wyoming region in the United States and a landfill site in Italy further verify the practical applicability of the OOA.Comprehensive test results indicate that the OOA outperforms the Particle Swarm Optimization(PSO)algorithm,providing a highly accurate and reliable inversion strategy for dispersion curve inversion.
基金supported by the National Key Research and Development Project(No.2019YFA0705403)the National Natural Science Foundation of China(No.T2293693,52273311)+2 种基金the Guangdong Basic and Applied Basic Research Foundation(No.2020B0301030002)and the Shenzhen Basic Research Project(Nos.WDZC20200824091903001,JSGG20220831105402004,JCYJ20220818100806014)Shenzhen Major Science and Technology Projects(Nos.KCXFZ20240903094013018,KCXFZ20240903094203005)。
文摘Titanium dioxide(TiO_(2))has been an important protective ingredient in mineral-based sunscreens since the 1990s.However,traditional TiO_(2)nanoparticle formulations have seen little improvement over the past decades and continue to face persistent challenges related to light transmission,biosafety,and visual appearance.Here,we report the discovery of two-dimensional(2D)TiO_(2),characterized by a micro-sized lateral dimension(~1.6μm)and atomic-scale thickness,which fundamentally resolves these long-standing issues.The 2D structure enables exceptional light management,achieving 80%visible light transparency—rendering it nearly invisible on the skin—while maintaining UV-blocking performance comparable to unmodified rutile TiO_(2)nanoparticles.Its larger lateral size results in a two-orders-of-magnitude reduction in skin penetration(0.96 w/w%),significantly enhancing biosafety.Moreover,the unique layered architecture inherently suppresses the generation of reactive oxygen species(ROS)under sunlight exposure,reducing the ROS generation rate by 50-fold compared to traditional TiO_(2)nanoparticles.Through precise metal element modulation,we further developed the first customizable sunscreen material capable of tuning UV protection ranges and automatically matching diverse skin tones.The 2D TiO_(2)offers a potentially transformative approach to modern sunscreen formulation,combining superior UV protection,enhanced safety and a natural appearance.
基金Supported by National Natural Science Foundation of China(Grant No.52205072).
文摘Owing to their rolling friction,two-dimensional piston pumps are highly suitable as power components for electro-hydrostatic actuators(EHAs).These pumps are particularly advantageous for applications requiring high efficiency and reliability.However,the ambiguity surrounding the output flow characteristics of individual two-dimensional pumps poses a significant challenge in achieving precise closed-loop control of the EHA positions.To address this issue,this study established a comprehensive numerical model that included gap leakage to analyze the impact of leakage on the output flow characteristics of a two-dimensional piston pump.The validity of the numerical analysis was indirectly confirmed through meticulous measurements of the leakage and volumetric efficiency,ensuring robust results.The research findings indicated that,at lower pump speeds,leakage significantly affected the output flow rate,leading to potential inefficiencies in the system.Conversely,at higher rotational speeds,the impact of leakage was less pronounced,implying that the influence of leakage on the pump outlet flow must be carefully considered and managed for EHAs to perform position servo control.Additionally,the research demonstrates that two-dimensional motion does not have a unique or additional effect on pump leakage,thus simplifying the design considerations.Finally,the study concluded that maintaining an oil-filled leakage environment is beneficial because it helps reduce the impact of leakage and enhances the overall volumetric efficiency of the pump system.
基金supported by the National Natural Science Foundation of China(Nos.52272290,21972030,52073119,and 52373210)the Natural Science Foundation of Jilin Province(No.20230101029JC)+1 种基金the Fundamental Research Program of Shanxi Province(No.202303021212159)the Monash University Malaysia–ASEAN grant(No.ASE-000010)。
文摘Environmental catalysis has been considered one of the important research topics.Some technologies(e.g.,photocatalysis and electrocatalysis)have been intensively developed with the advance of synthetic technologies of catalytical materials.In 2019,we discussed the development trend of this field,and wrote a roadmap on this topic in Chinese Chemical Letters(30(2019)2065-2088).Nowadays,we discuss it again from a new viewpoint along this road.In this paper,several subtopics are discussed,e.g.,photocatalysis based on titanium dioxide,violet phosphorus,graphitic carbon and covalent organic frameworks,electrocatalysts based on carbon,metal-and covalent-organic framework.Finally,we hope that this roadmap can enrich the development of two-dimensional materials in environmental catalysis with novel understanding,and give useful inspiration to explore new catalysts for practical applications.
基金supported by the Key Project of the National Natural Science Foundation of China(Grant No.U1809210)the International Science Technology Cooperation Program of China(Grant No.2014DFR51160)+3 种基金the One Belt and One Road International Cooperation Project from the Key Research and Development Program of Zhejiang Province,China(Grant No.2018C04021)the National Natural Science Foundation of China(Grant Nos.50972129,50602039,and 52102052)the Fund from Institute of Wenzhou,Zhejiang University(Grant Nos.XMGL-CX-202305 and XMGLKJZX-202307)the Project from Tanghe Scientific&Technology Company(Grant No.KYY-HX-20230024).
文摘It is a key challenge to prepare two-dimensional diamond(2D-diamond).Herein,we develop a method for synthesizing 2D-diamond by depositing monodisperse tantalum(Ta)atoms onto graphene substrates using a hot-filament chemical vapor deposition setup,followed by annealing treatment under different temperatures at ambient pressure.The results indicate that when the annealing temperature increases from 700℃ to 1000℃,the size of the 2D-diamond found in the samples gradually increases from close to 20 nm to around 30 nm.Meanwhile,the size and number of amorphous carbon spheres and Ta-containing compounds between the graphene layers gradually increase.As the annealing temperature continues to rise to 1100℃,a significant aggregation of Ta-containing compounds is observed in the samples,with no diamond structure detected.This further confirms that monodisperse Ta atoms play a key role in graphene phase transition into 2D-diamond.This study provides a novel method for the ambient-pressure phase transition of graphene into 2D-diamond.
基金supported by the National Key Research&Development Program of China(Grant Nos.2022YFA1403500 and 2021YFA1400500)the National Science Foundation of China(Grant Nos.62321004,12234001,and 12474215)+1 种基金supported by New Cornerstone Science Foundationa fellowship and a CRF award from the Research Grants Council of the Hong Kong Special Administrative Region,China(Grant Nos.HKUST SRFS2324-6S01 and C7037-22GF)。
文摘Electron-hole interactions play a crucial role in determining the optoelectronic properties of materials,and in lowdimensional systems this is especially true due to the decrease of screening.In this review,we focus on one unique quantum phase induced by the electron-hole interaction in two-dimensional systems,known as“exciton insulators”(EIs).Although this phase of matter has been studied for more than half a century,suitable platforms for its stable realization remain scarce.We provide an overview of the strategies to realize EIs in accessible materials and structures,along with a discussion on some unique properties of EIs stemming from the band structures of these materials.Additionally,signatures in experiments to distinguish EIs are discussed.
基金supported by the National Natural Science Foundation of China(Grant Nos.12388101,12372288,U23A2069,and 92152301).
文摘Machine learning(ML)techniques have emerged as powerful tools for improving the predictive capabilities of Reynolds-averaged Navier-Stokes(RANS)turbulence models in separated flows.This improvement is achieved by leveraging complex ML models,such as those developed using field inversion and machine learning(FIML),to dynamically adjust the constants within the baseline RANS model.However,the ML models often overlook the fundamental calibrations of the RANS turbulence model.Consequently,the basic calibration of the baseline RANS model is disrupted,leading to a degradation in the accuracy,particularly in basic wall-attached flows outside of the training set.To address this issue,a modified version of the Spalart-Allmaras(SA)turbulence model,known as Rubber-band SA(RBSA),has been proposed recently.This modification involves identifying and embedding constraints related to basic wall-attached flows directly into the model.It is shown that no matter how the parameters of the RBSA model are adjusted as constants throughout the flow field,its accuracy in wall-attached flows remains unaffected.In this paper,we propose a new constraint for the RBSA model,which better safeguards the law of wall in extreme conditions where the model parameter is adjusted dramatically.The resultant model is called the RBSA-poly model.We then show that when combined with FIML augmentation,the RBSA-poly model effectively preserves the accuracy of simple wall-attached flows,even when the adjusted parameters become functions of local flow variables rather than constants.A comparative analysis with the FIML-augmented original SA model reveals that the augmented RBSA-poly model reduces error in basic wall-attached flows by 50%while maintaining comparable accuracy in trained separated flows.These findings confirm the effectiveness of utilizing FIML in conjunction with the RBSA model,offering superior accuracy retention in cardinal flows.
文摘The pursuit of sustainable hydrogen production has positioned water electrolysis as a cornerstone technology for global carbon neutrality.However,sluggish kinetics,catalyst scarcity,and system integration challenges hinder its widespread deployment.Ultrathin two-dimensional(2D)materials,with their atomically exposed surfaces,tunable electronic structures,and defect-engineering capabilities,present unique opportunities for next-generation electrocatalysts.This review provides an integrated overview of ultrathin 2D electrocatalysts,discussing their structural diversity,synthetic routes,structure-activity relationships,and mechanistic understanding in water electrolysis processes.Special focus is placed on the translation of 2D materials from laboratory research to practical device implementation,emphasizing challenges such as scalable fabrication,interfacial engineering,and operational durability in realistic electrolyzer environments.The role of advanced characterization techniques in capturing dynamic structural changes and active site evolution is discussed.Finally,we outline future research directions,emphasizing the synergy of machine learning-driven materials discovery,advanced operando characterization,and scalable system integration to accelerate the industrial translation of 2D electrocatalysts for green hydrogen production.
基金supported by National Science and Technology Major Project(Grant No.2017ZX05018-001)。
文摘Deblending is a data processing procedure used to separate the source interferences of blended seismic data,which are obtained by simultaneous sources with random time delays to reduce the cost of seismic acquisition.There are three types of deblending algorithms,i.e.,filtering-type noise suppression algorithm,inversion-based algorithm and deep-learning based algorithm.We review the merits of these techniques,and propose to use a sparse inversion method for seismic data deblending.Filtering-based deblending approach is applicable to blended data with a low blending fold and simple geometry.Otherwise,it can suffer from signal distortion and noise leakage.At present,the deep learning based deblending methods are still under development and field data applications are limited due to the lack of high-quality training labels.In contrast,the inversion-based deblending approaches have gained industrial acceptance.Our used inversion approach transforms the pseudo-deblended data into the frequency-wavenumber-wavenumher(FKK)domain,and a sparse constraint is imposed for the coherent signal estimation.The estimated signal is used to predict the interference noise for subtraction from the original pseudo-deblended data.Via minimizing the data misfit,the signal can be iteratively updated with a shrinking threshold until the signal and interference are fully separated.The used FKK sparse inversion algorithm is very accurate and efficient compared with other sparse inversion methods,and it is widely applied in field cases.Synthetic example shows that the deblending error is less than 1%in average amplitudes and less than-40 dB in amplitude spectra.We present three field data examples of land,marine OBN(Ocean Bottom Nodes)and streamer acquisitions to demonstrate its successful applications in separating the source interferences efficiently and accurately.
基金National Natural Science Foundation of China under Grant 42276055National Key Research and Development Program under Grant 2022YFC2803503Fundamental Research Funds for the Central Universities under Grant 202262008.
文摘Full waveform inversion methods evaluate the properties of subsurface media by minimizing the misfit between synthetic and observed data.However,these methods omit measurement errors and physical assumptions in modeling,resulting in several problems in practical applications.In particular,full waveform inversion methods are very sensitive to erroneous observations(outliers)that violate the Gauss–Markov theorem.Herein,we propose a method for addressing spurious observations or outliers.Specifically,we remove outliers by inverting the synthetic data using the local convexity of the Gaussian distribution.To achieve this,we apply a waveform-like noise model based on a specific covariance matrix definition.Finally,we build an inversion problem based on the updated data,which is consistent with the wavefield reconstruction inversion method.Overall,we report an alternative optimization inversion problem for data containing outliers.The proposed method is robust because it uses uncertainties.This method enables accurate inversion,even when based on noisy models or a wrong wavelet.
基金supported by National Natural Science Foundation of China(72288101,72361137002,and 72101018)the Dutch Research Council(NWO Grant 482.22.01).
文摘Vehicle electrification,an important method for reducing carbon emissions from road transport,has been promoted globally.In this study,we analyze how individuals adapt to this transition in transportation and its subsequent impact on urban structure.Considering the varying travel costs associated with electric and fuel vehicles,we analyze the dynamic choices of households concerning house locations and vehicle types in a two-dimensional monocentric city.A spatial equilibrium is developed to model the interactions between urban density,vehicle age and vehicle type.An agent-based microeconomic residential choice model dynamically coupled with a house rent market is developed to analyze household choices of home locations and vehicle energy types,considering vehicle ages and competition for public charging piles.Key findings from our proposed models show that the proportion of electric vehicles(EVs)peaks at over 50%by the end of the first scrappage period,accompanied by more than a 40%increase in commuting distance and time compared to the scenario with only fuel vehicles.Simulation experiments on a theoretical grid indicate that heterogeneity-induced residential segregation can lead to urban sprawl and congestion.Furthermore,households with EVs tend to be located farther from the city center,and an increase in EV ownership contributes to urban expansion.Our study provides insights into how individuals adapt to EV transitions and the resulting impacts on home locations and land use changes.It offers a novel perspective on the dynamic interactions between EV adoption and urban development.
