Workpiece rotational grinding is widely used in the ultra-precision machining of hard and brittle semiconductor materials,including single-crystal silicon,silicon carbide,and gallium arsenide.Surface roughness and sub...Workpiece rotational grinding is widely used in the ultra-precision machining of hard and brittle semiconductor materials,including single-crystal silicon,silicon carbide,and gallium arsenide.Surface roughness and subsurface damage depth(SDD)are crucial indicators for evaluating the surface quality of these materials after grinding.Existing prediction models lack general applicability and do not accurately account for the complex material behavior under grinding conditions.This paper introduces novel models for predicting both surface roughness and SDD in hard and brittle semiconductor materials.The surface roughness model uniquely incorporates the material’s elastic recovery properties,revealing the significant impact of these properties on prediction accuracy.The SDD model is distinguished by its analysis of the interactions between abrasive grits and the workpiece,as well as the mechanisms governing stress-induced damage evolution.The surface roughness model and SDD model both establish a stable relationship with the grit depth of cut(GDC).Additionally,we have developed an analytical relationship between the GDC and grinding process parameters.This,in turn,enables the establishment of an analytical framework for predicting surface roughness and SDD based on grinding process parameters,which cannot be achieved by previous models.The models were validated through systematic experiments on three different semiconductor materials,demonstrating excellent agreement with experimental data,with prediction errors of 6.3%for surface roughness and6.9%for SDD.Additionally,this study identifies variations in elastic recovery and material plasticity as critical factors influencing surface roughness and SDD across different materials.These findings significantly advance the accuracy of predictive models and broaden their applicability for grinding hard and brittle semiconductor materials.展开更多
Particle shape and local breakage significantly affect the deformation characteristics of crushable granular materials.However,in the existing constitutive model research,there is less introduction of particle shape o...Particle shape and local breakage significantly affect the deformation characteristics of crushable granular materials.However,in the existing constitutive model research,there is less introduction of particle shape on particle breakage.A quantitative parameter for the three-dimensional particle shape(Average spherical modulus G_(M))is proposed in this study.Combined with G_(M),the triaxial compression test of granular materials with different particle shapes was carried out,and the particle size distribution before and after the test was determined.The results indicate that the local damage mechanism governs the macroscopic deformation behavior of granular materials,as influenced by the particle gradation of the samples before and after the triaxial compression test.Based on these findings,a binary medium model with a friction element weakening factor is proposed.This model incorporates the effects of particle shape and breakage behavior,significantly enhancing its calculation accuracy.Experimental results demonstrate that the model effectively predicts the deformation of crushable granular materials,accounting for particle shape.展开更多
This review presents a comprehensive and forward-looking analysis of how Large Language Models(LLMs)are transforming knowledge discovery in the rational design of advancedmicro/nano electrocatalyst materials.Electroca...This review presents a comprehensive and forward-looking analysis of how Large Language Models(LLMs)are transforming knowledge discovery in the rational design of advancedmicro/nano electrocatalyst materials.Electrocatalysis is central to sustainable energy and environmental technologies,but traditional catalyst discovery is often hindered by high complexity,fragmented knowledge,and inefficiencies.LLMs,particularly those based on Transformer architectures,offer unprecedented capabilities in extracting,synthesizing,and generating scientific knowledge from vast unstructured textual corpora.This work provides the first structured synthesis of how LLMs have been leveraged across various electrocatalysis tasks,including automated information extraction from literature,text-based property prediction,hypothesis generation,synthesis planning,and knowledge graph construction.We comparatively analyze leading LLMs and domain-specific frameworks(e.g.,CatBERTa,CataLM,CatGPT)in terms of methodology,application scope,performance metrics,and limitations.Through curated case studies across key electrocatalytic reactions—HER,OER,ORR,and CO_(2)RR—we highlight emerging trends such as the growing use of embedding-based prediction,retrieval-augmented generation,and fine-tuned scientific LLMs.The review also identifies persistent challenges,including data heterogeneity,hallucination risks,lack of standard benchmarks,and limited multimodal integration.Importantly,we articulate future research directions,such as the development of multimodal and physics-informedMatSci-LLMs,enhanced interpretability tools,and the integration of LLMswith selfdriving laboratories for autonomous discovery.By consolidating fragmented advances and outlining a unified research roadmap,this review provides valuable guidance for both materials scientists and AI practitioners seeking to accelerate catalyst innovation through large language model technologies.展开更多
This study is devoted to a novel fractional friction-damage model for quasi-brittle rock materials subjected to cyclic loadings in the framework of micromechanics.The total damage of material describing the microstruc...This study is devoted to a novel fractional friction-damage model for quasi-brittle rock materials subjected to cyclic loadings in the framework of micromechanics.The total damage of material describing the microstructural degradation is decomposed into two parts:an instantaneous part arising from monotonic loading and a fatigue-related one induced by cyclic loading,relating to the initiation and propagation of microcracks.The inelastic deformation arises directly from frictional sliding along microcracks,inherently coupled with the damage effect.A fractional plastic flow rule is introduced using stress-fractional plasticity operations and covariant transformation approach,instead of classical plastic flow function.Additionally,the progression of fatigue damage is intricately tied to subcracks and can be calculated through application of a convolution law.The number of loading cycles serves as an integration variable,establishing a connection between inelastic deformation and the evolution of fatigue damage.In order to verify the accuracy of the proposed model,comparison between analytical solutions and experimental data are carried out on three different rocks subjected to conventional triaxial compression and cyclic loading tests.The evolution of damage variables is also investigated along with the cumulative deformation and fatigue lifetime.The improvement of the fractional model is finally discussed by comparing with an existing associated fatigue model in literature.展开更多
[Objective]This study aims to develop a thermodynamically consistent phase-field framework for modeling the initiation and evolution of discontinuous structures in geomaterials.[Methods]Our model introduces crack driv...[Objective]This study aims to develop a thermodynamically consistent phase-field framework for modeling the initiation and evolution of discontinuous structures in geomaterials.[Methods]Our model introduces crack driving forces derived from the volumetric-deviatoric strain decomposition strategy,incorporating distinct tension,compression,and shear degradation mechanisms.Inertia effects capture compaction-band formation driven by wave-like disturbances,grain crushing,and frictional rearrangement.A monolithic algorithm ensures numerical stability and rapid convergence.[Results]The framework reproduces tensile,shear,mixed tensile-shear,and compressive-shear failures using the Benzeggagh-Kenane criterion.Validation against benchmark simulations-including uniaxial compression of rock-like and triaxial compression of V-notched sandstone specimens-demonstrates accurate predictions of crack initiation stress,localization orientation,and energy dissipation.[Conclusions]The framework provides a unified and robust numerical tool for analyzing the spatiotemporal evolution of strain localization and fracture in geomaterials.[Significance]By linking microscale fracture dynamics with macroscale failure within a thermodynamically consistent scheme,this study advances predictive modeling of rock stability,slope failure,and subsurface energy systems,contributing to safer and more sustainable geotechnical practice.展开更多
Mechanical loading constitutes a fundamental determinant in the process of bone remodeling.This modeling encompasses the incorporation of mechanical stimuli,the involvement of cellular and molecular constituents,as we...Mechanical loading constitutes a fundamental determinant in the process of bone remodeling.This modeling encompasses the incorporation of mechanical stimuli,the involvement of cellular and molecular constituents,as well as the utilization of sophisticated computational methodologies.Such an approach is imperative for forecasting bone behaviour across varying environmental conditions.In the present study,key findings from bone mechanobiology are reviewed,along with the possibility that Functionally Graded Materials(FGM)enhances osseointegration and lowers the stress-shielding effect during bone remodeling and compared to titanium,FGM improves periprosthetic bone remodeling.To summarise some of the most important findings from computational models of bone mechanobiology,explaining how modifications to the mechanical environment affect implant design,growth of bone,and bone response.The impact that changes related to the mechanical environment have on bone response is examined using computational models and methods such as surface microtopography to determine how an implant’s bone density has increased over time.This review focuses on the refinement of advanced simulation frameworks and their synergy with imaging technologies to strengthen model validation,ultimately resulting in better clinical outcomes in the context of bone health treatments.展开更多
The strength characteristics of ice materials are crucial for the analysis of the interaction between ice and structure in ocean engineering and ice engineering.In this investigation,six machine learning methods were ...The strength characteristics of ice materials are crucial for the analysis of the interaction between ice and structure in ocean engineering and ice engineering.In this investigation,six machine learning methods were utilized to predict the strength of the envelope surface of ice materials.The database for the ice strength was first established by collecting 1,481 testing data reported in the previous literatures.A quadric strength criterion was adopted to describe failure behaviors of ice materials under different conditions of material property and laboratory.Three model parameters in this strength criterion were forecasted by using six machine learning methods.The prediction capacities of six machine learning methods were evaluated by three statics indices,and the integrated simulation ability of six machine learning methods was arranged.Three machine learning algorithms were selected to be improved and optimized,and the simulation capacity of the three algorithms was further explored.The optimization results indicate that the improved potential of the Ensemble algorithm is much higher than that of the SVM algorithm and the GPR algorithm for predicting the ice strength.展开更多
This paper presents a theoretical model of dislocation penetration through grain boundaries(GBs)in micro-crystalline materials,taking into account the interactions between dislocations and GBs in a hydrogen environmen...This paper presents a theoretical model of dislocation penetration through grain boundaries(GBs)in micro-crystalline materials,taking into account the interactions between dislocations and GBs in a hydrogen environment.It describes the pile-up and penetration of dislocations at GBs in poly-crystalline materials,and discusses the effects of grain size and GB disorientation angle on dislocation distribution within grains.The results reveal that decreasing grain size or increasing GB disorientation angle reduces the dislocation distribution region in grains.Moreover,the presence of hydrogen further decreases this distribution area,suggesting a reduction in dislocations emitted in a hydrogen environment.Consequently,this diminishes the shielding effect of slip band dislocations on crack growth and weakens the passivation ability of the crack,promoting increased crack propagation.The maximum reduction in the critical stress intensity factor for poly-crystalline materials in a hydrogen environment is approximately 16%.These results are significant for understanding the fracture behavior of poly-crystalline materials exposed to hydrogen.展开更多
A novel method is introduced to optimize the traditional Skanavi model by decomposing the electric field of molecules into the electric field of ions and quantitatively describing the ionic-scale electric field by the...A novel method is introduced to optimize the traditional Skanavi model by decomposing the electric field of molecules into the electric field of ions and quantitatively describing the ionic-scale electric field by the structural coefficient of the effective electric field.Furthermore,the optimization of the Skanavi model is demonstrated and the ferroelectric phase transition of BaTiO_(3)crystals is revealed by calculating the optical and static permittivities of BaTiO_(3),CaTiO_(3),and SrTiO_(3)crystals and the structure coefficients of the effective electric field of BT crystals after Ti4+displacement.This research compensates for the deficiencies of the traditional Skanavi model and refines the theoretical framework for analyzing dielectric properties in high permittivity materials.展开更多
The integration of visual elements,such as emojis,into educational content represents a promising approach to enhancing student engagement and comprehension.However,existing efforts in emoji integration often lack sys...The integration of visual elements,such as emojis,into educational content represents a promising approach to enhancing student engagement and comprehension.However,existing efforts in emoji integration often lack systematic frameworks capable of addressing the contextual and pedagogical nuances required for effective implementation.This paper introduces a novel framework that combines Data-Driven Error-Correcting Output Codes(DECOC),Long Short-Term Memory(LSTM)networks,and Multi-Layer Deep Neural Networks(ML-DNN)to identify optimal emoji placements within computer science course materials.The originality of the proposed system lies in its ability to leverage sentiment analysis techniques and contextual embeddings to align emoji recommendations with both the emotional tone and learning objectives of course content.A meticulously annotated dataset,comprising diverse topics in computer science,was developed to train and validate the model,ensuring its applicability across a wide range of educational contexts.Comprehensive validation demonstrated the system’s superior performance,achieving an accuracy of 92.4%,precision of 90.7%,recall of 89.3%,and an F1-score of 90.0%.Comparative analysis with baselinemodels and relatedworks confirms themodel’s ability tooutperformexisting approaches inbalancing accuracy,relevance,and contextual appropriateness.Beyond its technical advancements,this framework offers practical benefits for educators by providing an Artificial Intelligence-assisted(AI-assisted)tool that facilitates personalized content adaptation based on student sentiment and engagement patterns.By automating the identification of appropriate emoji placements,teachers can enhance digital course materials with minimal effort,improving the clarity of complex concepts and fostering an emotionally supportive learning environment.This paper contributes to the emerging field of AI-enhanced education by addressing critical gaps in personalized content delivery and pedagogical support.Its findings highlight the transformative potential of integrating AI-driven emoji placement systems into educational materials,offering an innovative tool for fostering student engagement and enhancing learning outcomes.The proposed framework establishes a foundation for future advancements in the visual augmentation of educational resources,emphasizing scalability and adaptability for broader applications in e-learning.展开更多
The inelastic behavior of thermoplastic polymers may involve shearing and crazing,and both depend on temperature and strain rate.Traditional constitutive models account for temperature and strain rate through phenomen...The inelastic behavior of thermoplastic polymers may involve shearing and crazing,and both depend on temperature and strain rate.Traditional constitutive models account for temperature and strain rate through phenomenological or empirical formulas.In this study,we present a physics-guided machine learning(ML)framework to model shear and craze in polymeric materials.The effects of all three principal stresses for the craze initiation are considered other than the maximum tensile principal stress solely in previous works.We implemented a finite element framework through a user-defined material subroutine and applied the constitutive model to the deformation in three polymers(PLA 4060D,PLA 3051D,and HIPS).The result shows that our ML-based model can predict the stress-strain and volume-strain responses at different strain rates with high accuracy.Notably,the ML-based approach needs no assumptions about yield criteria or hardening laws.This work highlights the potential of hybrid physics-ML paradigms to overcome the trade-offs between model complexity and accuracy in polymer mechanics,paving the way for computationally efficient and generalizable constitutive models for thermoplastic materials.展开更多
In order to improve the traditional teaching model of traditional Chinese medicine identification course for graduate students of pharmacy,this paper described the research on constructing and practicing the blended t...In order to improve the traditional teaching model of traditional Chinese medicine identification course for graduate students of pharmacy,this paper described the research on constructing and practicing the blended teaching model of"online+offline"based on the teaching concept of TfU by taking the course of"Authentic Medicinal Materials and Quality of Traditional Chinese Medicine"as an example.In the preparatory phase,through resource integration and course content decomposition,it identifies"generative topics"to engage students in pre-class online discussions.During the instructional phase,"comprehension-oriented objectives"are established based on learning analytics,followed by the implementation of"understanding-focused activities"for guided inquiry in offline classrooms.The post-class phase employs online extended materials to conduct"sustained assessment"through evaluations and summaries,thereby continuously optimizing subsequent teaching practices.This pedagogical framework not only effectively cultivates investigative research thinking among graduate students but also enhances standardized management and scientific development of the teaching team.The practical research outcomes and experiences derived from this model can provide valuable references for analogous course reforms.展开更多
Zirconium,titanium,and other hexagonally close-packed(HCP)metals and their alloys are representative high specific strength,high reaction enthalpy,and high thermal conductivity structural materials.In this study,two t...Zirconium,titanium,and other hexagonally close-packed(HCP)metals and their alloys are representative high specific strength,high reaction enthalpy,and high thermal conductivity structural materials.In this study,two typical HCP metals,zirconium,and titanium,were applied to reactive materials(RMs)to prepare Zr/PTFE/W RMs and Ti/PTFE/W RMs,validating the feasibility of HCP metal/PTFE/W RMs.The impact response process of typical HCP metal/PTFE/W RMs under high-velocity dynamic loads was studied using shock equations of state(EOS)based on porous mixtures and chemical reaction kinetics equations.An improved hemispherical quasi-sealed test chamber was employed to measure the energy release characteristic curves of 10 types of Zr/PTFE/W RMs and Ti/PTFE/W RMs under impact velocities ranging from 500 m/s to 1300 m/s.The datasets of the impact-induced energy release characteristics of HCP metal/PTFE/W RMs were established.Additionally,the energy release efficiency of HCP metal/PTFE/W RMs under impact was predicted using the support vector regression(SVR)kernel function model.The datasets of Zr/PTFE/W RMs and Ti/PTFE/W RMs with W contents of 0%,25%,50%,and 75%were used as test sets,respectively.The model predictions showed a high degree of agreement with the experimental data,with mean absolute errors(MAE)of 4.8,6.5,4.6,and 4.1,respectively.展开更多
The freeze-thaw(FT)behavior of porous materials(PMs)involves the coupling of the thermo-hydromechanical(THM)processes and is significantly influenced by the microstructure.However,modeling FT in unsaturated PMs remain...The freeze-thaw(FT)behavior of porous materials(PMs)involves the coupling of the thermo-hydromechanical(THM)processes and is significantly influenced by the microstructure.However,modeling FT in unsaturated PMs remains an open issue,and the influence of microstructure is not yet fully understood.To address these challenges,we propose a THM model for FT in PMs that considers microstructure and variable air content.In this work,a non-equilibrium thermodynamic approach is proposed to capture ice formation/melting,the microstructure is accounted for utilizing micromechanics,and the FT processes in air-entrained PMs are formulated within the proposed THM model.This model incorporates variable air void characteristics,e.g.air content,spacing factor,specific surface area,and supercooled water-filled regimes,and distinguishes the roles of air voids between freezing and thawing.The FT behaviors,including deformation,ice formation/melting,spacing factor,and pore water pressure evolutions,are focused.Comparisons with experimental results,confirm the capability of the present model.The results demonstrate the effects of variable air voids on the FT behavior of air-entrained PMs.The findings reveal that assuming fixed air void characteristics can lead to underestimation of pore pressure and deformation,particularly at low air content.Additionally,air voids act as cryo-pumps during freezing and when the cooling temperature stabilizes.During thawing,air voids supply gas to the melting sites(i.e.“gas escape”),preventing further significant deformation reduction.These results can provide novel insights for understanding the frost damage of PMs.展开更多
To identify coatings and analyze the anti-detection capabilities of camouflage patterns, material samples can be prepared using the super-pixel segmentation method. A spectral polarization imaging system is developed,...To identify coatings and analyze the anti-detection capabilities of camouflage patterns, material samples can be prepared using the super-pixel segmentation method. A spectral polarization imaging system is developed, based on the principle of bidirectional reflectance distribution function(BRDF), to obtain spectral reflection intensities of coatings at full spatial angles, and use polarization images to calculate the refractive index by the Fresnel equation. The index is then coupled into TorranceSparrow model to simulate the spectral scattering intensity to mutually verify the experimental results. The spectral scattering characteristics of standard camouflage patterns are then revealed and pinpoint the signature band and the angle of reflecting sensitivity.展开更多
In this work, we present numerical modelling of coupled heat and mass transfer within porous materials. Our study focuses on cinder block bricks generally used in building construction. The material is assumed to be p...In this work, we present numerical modelling of coupled heat and mass transfer within porous materials. Our study focuses on cinder block bricks generally used in building construction. The material is assumed to be placed in air. Moisture content and temperature have been chosen as the main transfer drivers and the equations governing these transfer drivers are based on the Luikov model. These equations are solved by an implicit finite difference scheme. A Fortran code associated with the Thomas algorithm was used to solve the equations. The results show that heat and mass transfer depend on the temperature of the air in contact with the material. As this air temperature rises, the temperature within the material increases, and more rapidly at the material surface. Also, thermal conductivity plays a very important role in the thermal conduction of building materials and influences heat and mass transfer in these materials. Materials with higher thermal conductivity diffuse more heat.展开更多
Based on the Karma model and the Eggleston regularization technique of the strong interfacial energy anisotropy, a phase-field model was established for HCP materials. An explicit finite difference numerical method wa...Based on the Karma model and the Eggleston regularization technique of the strong interfacial energy anisotropy, a phase-field model was established for HCP materials. An explicit finite difference numerical method was used to solve phase field model and simulate the dendrite growth behaviors of HCP materials. Results indicate that the dendrite morphology presents obvious six-fold symmetry, and discontinuity in the variation of interface orientation occurs, resulting in a fact that the corners were formed at the tips of the main stem and side branches. When the interfacial energy anisotropy strength is lower than the critical value（1/35）, the steady-state tip velocity of dendrite increases with anisotropy as expected. As the anisotropy strength crosses the critical value, the steady-state tip velocity drops down by about 0.89%. With further increase in anisotropy strength, the steady-state tip velocity increases and reaches the maximum value at anisotropy strength of 0.04, then decreases.展开更多
Physical mechanisms and influencing factors on the effective stress coefficient for rock/soil-like porous materials are investigated, based on which equivalent connectivity index is proposed. The equivalent connectivi...Physical mechanisms and influencing factors on the effective stress coefficient for rock/soil-like porous materials are investigated, based on which equivalent connectivity index is proposed. The equivalent connectivity index, relying on the meso-scale structure of porous material and the property of liquid, denotes the connectivity of pores in Representative Element Area （REA）. If the conductivity of the porous material is anisotropic, the equivalent connectivity index is a second order tensor. Based on the basic theories of continuous mechanics and tensor analysis, relationship between area porosity and volumetric porosity of porous materials is deduced. Then a generalized expression, describing the relation between effective stress coefficient tensor and equivalent connectivity tensor of pores, is proposed, and the expression can be applied to isotropic media and also to anisotropic materials. Furthermore, evolution of porosity and equivalent connectivity index of the pore are studied in the strain space, and the method to determine the corresponding functions in expressions above is proposed using genetic algorithm and genetic programming. Two applications show that the results obtained by the method in this paper perfectly agree with the test data. This paper provides an important theoretical support to the coupled hydro-mechanical research.展开更多
Grain crushing is commonly encountered in deep foundation engineering,high rockfill dam engineering,railway engineering,mining engineering,coastal engineering,petroleum engineering,and other geoscience application.Gra...Grain crushing is commonly encountered in deep foundation engineering,high rockfill dam engineering,railway engineering,mining engineering,coastal engineering,petroleum engineering,and other geoscience application.Grain crushing is affected by fundamental soil characteristics,such as their mineral strength,grain size and distribution,grain shape,density and specimen size,and also by external factors including stress magnitude and path,loading rate and duration,degree of saturation,temperature and geochemical environment.Crushable material becomes a series of different materials with the change in its grading during grain crushing,resulting in a decrease in strength and dilatancy and an increase in compressibility.Effects of grain crushing on strength,dilatancy,deformation and failure mechanisms have been extensively investigated through laboratory testing,discrete element method(DEM)modelling,Weibull statistics,and constitutive modelling within the framework of the extended crushing-dependent critical state theory or the energy-based theory.Eleven papers summarized in this review article for this special issue addressed the above issues in grain crushing through the advanced testing and modelling.展开更多
A non-linear continuum damage model was presented based on the irreversible thermodynamics framework developed by LEMAITRE and CHABOCHE. The proposed model was formulated by taking into account the influence of loadin...A non-linear continuum damage model was presented based on the irreversible thermodynamics framework developed by LEMAITRE and CHABOCHE. The proposed model was formulated by taking into account the influence of loading frequency on fatigue life. The parameters H and c are constants for frequency-independent materials, but functions of cyclic frequency for frequency-dependent materials. In addition, the expression of the model was discussed in detail at different stress ratios （R）. Fatigue test data of AlZnMgCu1.5 aluminium alloy and AMg6N alloy were used to verify the proposed model. The results showed that the model possesses a good ability of predicting fatigue life at different loading frequencies and stress ratios.展开更多
基金supported by the National Key Research and Development Program of China(2022YFB3605902)the National Natural Science Foundation of China(52375411,52293402)。
文摘Workpiece rotational grinding is widely used in the ultra-precision machining of hard and brittle semiconductor materials,including single-crystal silicon,silicon carbide,and gallium arsenide.Surface roughness and subsurface damage depth(SDD)are crucial indicators for evaluating the surface quality of these materials after grinding.Existing prediction models lack general applicability and do not accurately account for the complex material behavior under grinding conditions.This paper introduces novel models for predicting both surface roughness and SDD in hard and brittle semiconductor materials.The surface roughness model uniquely incorporates the material’s elastic recovery properties,revealing the significant impact of these properties on prediction accuracy.The SDD model is distinguished by its analysis of the interactions between abrasive grits and the workpiece,as well as the mechanisms governing stress-induced damage evolution.The surface roughness model and SDD model both establish a stable relationship with the grit depth of cut(GDC).Additionally,we have developed an analytical relationship between the GDC and grinding process parameters.This,in turn,enables the establishment of an analytical framework for predicting surface roughness and SDD based on grinding process parameters,which cannot be achieved by previous models.The models were validated through systematic experiments on three different semiconductor materials,demonstrating excellent agreement with experimental data,with prediction errors of 6.3%for surface roughness and6.9%for SDD.Additionally,this study identifies variations in elastic recovery and material plasticity as critical factors influencing surface roughness and SDD across different materials.These findings significantly advance the accuracy of predictive models and broaden their applicability for grinding hard and brittle semiconductor materials.
基金the National Natural Science Foundation of China(Grant No.12372376)the Scientific Innovation Practice Project of Postgraduates of Chang’an University(300103724017)。
文摘Particle shape and local breakage significantly affect the deformation characteristics of crushable granular materials.However,in the existing constitutive model research,there is less introduction of particle shape on particle breakage.A quantitative parameter for the three-dimensional particle shape(Average spherical modulus G_(M))is proposed in this study.Combined with G_(M),the triaxial compression test of granular materials with different particle shapes was carried out,and the particle size distribution before and after the test was determined.The results indicate that the local damage mechanism governs the macroscopic deformation behavior of granular materials,as influenced by the particle gradation of the samples before and after the triaxial compression test.Based on these findings,a binary medium model with a friction element weakening factor is proposed.This model incorporates the effects of particle shape and breakage behavior,significantly enhancing its calculation accuracy.Experimental results demonstrate that the model effectively predicts the deformation of crushable granular materials,accounting for particle shape.
文摘This review presents a comprehensive and forward-looking analysis of how Large Language Models(LLMs)are transforming knowledge discovery in the rational design of advancedmicro/nano electrocatalyst materials.Electrocatalysis is central to sustainable energy and environmental technologies,but traditional catalyst discovery is often hindered by high complexity,fragmented knowledge,and inefficiencies.LLMs,particularly those based on Transformer architectures,offer unprecedented capabilities in extracting,synthesizing,and generating scientific knowledge from vast unstructured textual corpora.This work provides the first structured synthesis of how LLMs have been leveraged across various electrocatalysis tasks,including automated information extraction from literature,text-based property prediction,hypothesis generation,synthesis planning,and knowledge graph construction.We comparatively analyze leading LLMs and domain-specific frameworks(e.g.,CatBERTa,CataLM,CatGPT)in terms of methodology,application scope,performance metrics,and limitations.Through curated case studies across key electrocatalytic reactions—HER,OER,ORR,and CO_(2)RR—we highlight emerging trends such as the growing use of embedding-based prediction,retrieval-augmented generation,and fine-tuned scientific LLMs.The review also identifies persistent challenges,including data heterogeneity,hallucination risks,lack of standard benchmarks,and limited multimodal integration.Importantly,we articulate future research directions,such as the development of multimodal and physics-informedMatSci-LLMs,enhanced interpretability tools,and the integration of LLMswith selfdriving laboratories for autonomous discovery.By consolidating fragmented advances and outlining a unified research roadmap,this review provides valuable guidance for both materials scientists and AI practitioners seeking to accelerate catalyst innovation through large language model technologies.
基金Fundamental Research Funds for the Central Universities(Grant No.B230201059)for the support.
文摘This study is devoted to a novel fractional friction-damage model for quasi-brittle rock materials subjected to cyclic loadings in the framework of micromechanics.The total damage of material describing the microstructural degradation is decomposed into two parts:an instantaneous part arising from monotonic loading and a fatigue-related one induced by cyclic loading,relating to the initiation and propagation of microcracks.The inelastic deformation arises directly from frictional sliding along microcracks,inherently coupled with the damage effect.A fractional plastic flow rule is introduced using stress-fractional plasticity operations and covariant transformation approach,instead of classical plastic flow function.Additionally,the progression of fatigue damage is intricately tied to subcracks and can be calculated through application of a convolution law.The number of loading cycles serves as an integration variable,establishing a connection between inelastic deformation and the evolution of fatigue damage.In order to verify the accuracy of the proposed model,comparison between analytical solutions and experimental data are carried out on three different rocks subjected to conventional triaxial compression and cyclic loading tests.The evolution of damage variables is also investigated along with the cumulative deformation and fatigue lifetime.The improvement of the fractional model is finally discussed by comparing with an existing associated fatigue model in literature.
文摘[Objective]This study aims to develop a thermodynamically consistent phase-field framework for modeling the initiation and evolution of discontinuous structures in geomaterials.[Methods]Our model introduces crack driving forces derived from the volumetric-deviatoric strain decomposition strategy,incorporating distinct tension,compression,and shear degradation mechanisms.Inertia effects capture compaction-band formation driven by wave-like disturbances,grain crushing,and frictional rearrangement.A monolithic algorithm ensures numerical stability and rapid convergence.[Results]The framework reproduces tensile,shear,mixed tensile-shear,and compressive-shear failures using the Benzeggagh-Kenane criterion.Validation against benchmark simulations-including uniaxial compression of rock-like and triaxial compression of V-notched sandstone specimens-demonstrates accurate predictions of crack initiation stress,localization orientation,and energy dissipation.[Conclusions]The framework provides a unified and robust numerical tool for analyzing the spatiotemporal evolution of strain localization and fracture in geomaterials.[Significance]By linking microscale fracture dynamics with macroscale failure within a thermodynamically consistent scheme,this study advances predictive modeling of rock stability,slope failure,and subsurface energy systems,contributing to safer and more sustainable geotechnical practice.
文摘Mechanical loading constitutes a fundamental determinant in the process of bone remodeling.This modeling encompasses the incorporation of mechanical stimuli,the involvement of cellular and molecular constituents,as well as the utilization of sophisticated computational methodologies.Such an approach is imperative for forecasting bone behaviour across varying environmental conditions.In the present study,key findings from bone mechanobiology are reviewed,along with the possibility that Functionally Graded Materials(FGM)enhances osseointegration and lowers the stress-shielding effect during bone remodeling and compared to titanium,FGM improves periprosthetic bone remodeling.To summarise some of the most important findings from computational models of bone mechanobiology,explaining how modifications to the mechanical environment affect implant design,growth of bone,and bone response.The impact that changes related to the mechanical environment have on bone response is examined using computational models and methods such as surface microtopography to determine how an implant’s bone density has increased over time.This review focuses on the refinement of advanced simulation frameworks and their synergy with imaging technologies to strengthen model validation,ultimately resulting in better clinical outcomes in the context of bone health treatments.
基金supported by the National Key R&D Program of China(2022YFC2903903)the National Natural Science Foundation of China(42271153,42471164)+2 种基金Western light project of Chinese Academy of Sciences of China(xbzg-zdsys-202311)The Science and Technology program of Gansu Province(23ZDFA017)Natural Science Foundation of Gansu province of China(24JRRA102)。
文摘The strength characteristics of ice materials are crucial for the analysis of the interaction between ice and structure in ocean engineering and ice engineering.In this investigation,six machine learning methods were utilized to predict the strength of the envelope surface of ice materials.The database for the ice strength was first established by collecting 1,481 testing data reported in the previous literatures.A quadric strength criterion was adopted to describe failure behaviors of ice materials under different conditions of material property and laboratory.Three model parameters in this strength criterion were forecasted by using six machine learning methods.The prediction capacities of six machine learning methods were evaluated by three statics indices,and the integrated simulation ability of six machine learning methods was arranged.Three machine learning algorithms were selected to be improved and optimized,and the simulation capacity of the three algorithms was further explored.The optimization results indicate that the improved potential of the Ensemble algorithm is much higher than that of the SVM algorithm and the GPR algorithm for predicting the ice strength.
基金supported by the National Natural Science Foundation of China(Grant No.12393782).
文摘This paper presents a theoretical model of dislocation penetration through grain boundaries(GBs)in micro-crystalline materials,taking into account the interactions between dislocations and GBs in a hydrogen environment.It describes the pile-up and penetration of dislocations at GBs in poly-crystalline materials,and discusses the effects of grain size and GB disorientation angle on dislocation distribution within grains.The results reveal that decreasing grain size or increasing GB disorientation angle reduces the dislocation distribution region in grains.Moreover,the presence of hydrogen further decreases this distribution area,suggesting a reduction in dislocations emitted in a hydrogen environment.Consequently,this diminishes the shielding effect of slip band dislocations on crack growth and weakens the passivation ability of the crack,promoting increased crack propagation.The maximum reduction in the critical stress intensity factor for poly-crystalline materials in a hydrogen environment is approximately 16%.These results are significant for understanding the fracture behavior of poly-crystalline materials exposed to hydrogen.
基金Project supported by the National Natural Science Foundation of China(Grant No.51277138)the Natural Science Basic Research Program of Shaanxi Province of China(Grant No.2021JM-442)the Fund from the Shaanxi Provincial Science and Technology Department for Qin Chuangyuan Scientist+Engineer Team(Grant No.2024QCY-KXJ-194)。
文摘A novel method is introduced to optimize the traditional Skanavi model by decomposing the electric field of molecules into the electric field of ions and quantitatively describing the ionic-scale electric field by the structural coefficient of the effective electric field.Furthermore,the optimization of the Skanavi model is demonstrated and the ferroelectric phase transition of BaTiO_(3)crystals is revealed by calculating the optical and static permittivities of BaTiO_(3),CaTiO_(3),and SrTiO_(3)crystals and the structure coefficients of the effective electric field of BT crystals after Ti4+displacement.This research compensates for the deficiencies of the traditional Skanavi model and refines the theoretical framework for analyzing dielectric properties in high permittivity materials.
基金funded by the Deanship of Postgraduate Studies and Scientific Research at Majmaah University,grant number[R-2025-1637].
文摘The integration of visual elements,such as emojis,into educational content represents a promising approach to enhancing student engagement and comprehension.However,existing efforts in emoji integration often lack systematic frameworks capable of addressing the contextual and pedagogical nuances required for effective implementation.This paper introduces a novel framework that combines Data-Driven Error-Correcting Output Codes(DECOC),Long Short-Term Memory(LSTM)networks,and Multi-Layer Deep Neural Networks(ML-DNN)to identify optimal emoji placements within computer science course materials.The originality of the proposed system lies in its ability to leverage sentiment analysis techniques and contextual embeddings to align emoji recommendations with both the emotional tone and learning objectives of course content.A meticulously annotated dataset,comprising diverse topics in computer science,was developed to train and validate the model,ensuring its applicability across a wide range of educational contexts.Comprehensive validation demonstrated the system’s superior performance,achieving an accuracy of 92.4%,precision of 90.7%,recall of 89.3%,and an F1-score of 90.0%.Comparative analysis with baselinemodels and relatedworks confirms themodel’s ability tooutperformexisting approaches inbalancing accuracy,relevance,and contextual appropriateness.Beyond its technical advancements,this framework offers practical benefits for educators by providing an Artificial Intelligence-assisted(AI-assisted)tool that facilitates personalized content adaptation based on student sentiment and engagement patterns.By automating the identification of appropriate emoji placements,teachers can enhance digital course materials with minimal effort,improving the clarity of complex concepts and fostering an emotionally supportive learning environment.This paper contributes to the emerging field of AI-enhanced education by addressing critical gaps in personalized content delivery and pedagogical support.Its findings highlight the transformative potential of integrating AI-driven emoji placement systems into educational materials,offering an innovative tool for fostering student engagement and enhancing learning outcomes.The proposed framework establishes a foundation for future advancements in the visual augmentation of educational resources,emphasizing scalability and adaptability for broader applications in e-learning.
基金supported by the National Natural Science Foundation of China(NSFC)Excellent Research Group Program for“Multiscale Problems in Nonlinear Mechanics”(Grant No.12588201)。
文摘The inelastic behavior of thermoplastic polymers may involve shearing and crazing,and both depend on temperature and strain rate.Traditional constitutive models account for temperature and strain rate through phenomenological or empirical formulas.In this study,we present a physics-guided machine learning(ML)framework to model shear and craze in polymeric materials.The effects of all three principal stresses for the craze initiation are considered other than the maximum tensile principal stress solely in previous works.We implemented a finite element framework through a user-defined material subroutine and applied the constitutive model to the deformation in three polymers(PLA 4060D,PLA 3051D,and HIPS).The result shows that our ML-based model can predict the stress-strain and volume-strain responses at different strain rates with high accuracy.Notably,the ML-based approach needs no assumptions about yield criteria or hardening laws.This work highlights the potential of hybrid physics-ML paradigms to overcome the trade-offs between model complexity and accuracy in polymer mechanics,paving the way for computationally efficient and generalizable constitutive models for thermoplastic materials.
基金Supported by Guangxi Degree and Postgraduate Education Reform Project(JGY2023213,JGY2023211).
文摘In order to improve the traditional teaching model of traditional Chinese medicine identification course for graduate students of pharmacy,this paper described the research on constructing and practicing the blended teaching model of"online+offline"based on the teaching concept of TfU by taking the course of"Authentic Medicinal Materials and Quality of Traditional Chinese Medicine"as an example.In the preparatory phase,through resource integration and course content decomposition,it identifies"generative topics"to engage students in pre-class online discussions.During the instructional phase,"comprehension-oriented objectives"are established based on learning analytics,followed by the implementation of"understanding-focused activities"for guided inquiry in offline classrooms.The post-class phase employs online extended materials to conduct"sustained assessment"through evaluations and summaries,thereby continuously optimizing subsequent teaching practices.This pedagogical framework not only effectively cultivates investigative research thinking among graduate students but also enhances standardized management and scientific development of the teaching team.The practical research outcomes and experiences derived from this model can provide valuable references for analogous course reforms.
基金sponsored by the National Natural Science Foundation of China(Grant Nos.U2241285,62201267)。
文摘Zirconium,titanium,and other hexagonally close-packed(HCP)metals and their alloys are representative high specific strength,high reaction enthalpy,and high thermal conductivity structural materials.In this study,two typical HCP metals,zirconium,and titanium,were applied to reactive materials(RMs)to prepare Zr/PTFE/W RMs and Ti/PTFE/W RMs,validating the feasibility of HCP metal/PTFE/W RMs.The impact response process of typical HCP metal/PTFE/W RMs under high-velocity dynamic loads was studied using shock equations of state(EOS)based on porous mixtures and chemical reaction kinetics equations.An improved hemispherical quasi-sealed test chamber was employed to measure the energy release characteristic curves of 10 types of Zr/PTFE/W RMs and Ti/PTFE/W RMs under impact velocities ranging from 500 m/s to 1300 m/s.The datasets of the impact-induced energy release characteristics of HCP metal/PTFE/W RMs were established.Additionally,the energy release efficiency of HCP metal/PTFE/W RMs under impact was predicted using the support vector regression(SVR)kernel function model.The datasets of Zr/PTFE/W RMs and Ti/PTFE/W RMs with W contents of 0%,25%,50%,and 75%were used as test sets,respectively.The model predictions showed a high degree of agreement with the experimental data,with mean absolute errors(MAE)of 4.8,6.5,4.6,and 4.1,respectively.
基金the funding support from the National Natural Science Foundation of China(Grant Nos.52350004 and 51925903).
文摘The freeze-thaw(FT)behavior of porous materials(PMs)involves the coupling of the thermo-hydromechanical(THM)processes and is significantly influenced by the microstructure.However,modeling FT in unsaturated PMs remains an open issue,and the influence of microstructure is not yet fully understood.To address these challenges,we propose a THM model for FT in PMs that considers microstructure and variable air content.In this work,a non-equilibrium thermodynamic approach is proposed to capture ice formation/melting,the microstructure is accounted for utilizing micromechanics,and the FT processes in air-entrained PMs are formulated within the proposed THM model.This model incorporates variable air void characteristics,e.g.air content,spacing factor,specific surface area,and supercooled water-filled regimes,and distinguishes the roles of air voids between freezing and thawing.The FT behaviors,including deformation,ice formation/melting,spacing factor,and pore water pressure evolutions,are focused.Comparisons with experimental results,confirm the capability of the present model.The results demonstrate the effects of variable air voids on the FT behavior of air-entrained PMs.The findings reveal that assuming fixed air void characteristics can lead to underestimation of pore pressure and deformation,particularly at low air content.Additionally,air voids act as cryo-pumps during freezing and when the cooling temperature stabilizes.During thawing,air voids supply gas to the melting sites(i.e.“gas escape”),preventing further significant deformation reduction.These results can provide novel insights for understanding the frost damage of PMs.
基金supported by the Jilin Province Science and Technology Development Plan Item (No.20240402068GH)。
文摘To identify coatings and analyze the anti-detection capabilities of camouflage patterns, material samples can be prepared using the super-pixel segmentation method. A spectral polarization imaging system is developed, based on the principle of bidirectional reflectance distribution function(BRDF), to obtain spectral reflection intensities of coatings at full spatial angles, and use polarization images to calculate the refractive index by the Fresnel equation. The index is then coupled into TorranceSparrow model to simulate the spectral scattering intensity to mutually verify the experimental results. The spectral scattering characteristics of standard camouflage patterns are then revealed and pinpoint the signature band and the angle of reflecting sensitivity.
文摘In this work, we present numerical modelling of coupled heat and mass transfer within porous materials. Our study focuses on cinder block bricks generally used in building construction. The material is assumed to be placed in air. Moisture content and temperature have been chosen as the main transfer drivers and the equations governing these transfer drivers are based on the Luikov model. These equations are solved by an implicit finite difference scheme. A Fortran code associated with the Thomas algorithm was used to solve the equations. The results show that heat and mass transfer depend on the temperature of the air in contact with the material. As this air temperature rises, the temperature within the material increases, and more rapidly at the material surface. Also, thermal conductivity plays a very important role in the thermal conduction of building materials and influences heat and mass transfer in these materials. Materials with higher thermal conductivity diffuse more heat.
基金Project(10834015)supported by the National Natural Science Foundation of ChinaProject(12SKY01-1)supported by the Doctoral Fund of Shangluo University,China
文摘Based on the Karma model and the Eggleston regularization technique of the strong interfacial energy anisotropy, a phase-field model was established for HCP materials. An explicit finite difference numerical method was used to solve phase field model and simulate the dendrite growth behaviors of HCP materials. Results indicate that the dendrite morphology presents obvious six-fold symmetry, and discontinuity in the variation of interface orientation occurs, resulting in a fact that the corners were formed at the tips of the main stem and side branches. When the interfacial energy anisotropy strength is lower than the critical value（1/35）, the steady-state tip velocity of dendrite increases with anisotropy as expected. As the anisotropy strength crosses the critical value, the steady-state tip velocity drops down by about 0.89%. With further increase in anisotropy strength, the steady-state tip velocity increases and reaches the maximum value at anisotropy strength of 0.04, then decreases.
基金supported by the Yalongjiang River Joint Fund by the National Natural Science Foundation of China(NSFC)Ertan Hydropower Development Company,LTD(Nos.50579091 and 50539090)+1 种基金NSFC(No.10772190)Major State Basic Research Project of China(No.2002CB412708)
文摘Physical mechanisms and influencing factors on the effective stress coefficient for rock/soil-like porous materials are investigated, based on which equivalent connectivity index is proposed. The equivalent connectivity index, relying on the meso-scale structure of porous material and the property of liquid, denotes the connectivity of pores in Representative Element Area （REA）. If the conductivity of the porous material is anisotropic, the equivalent connectivity index is a second order tensor. Based on the basic theories of continuous mechanics and tensor analysis, relationship between area porosity and volumetric porosity of porous materials is deduced. Then a generalized expression, describing the relation between effective stress coefficient tensor and equivalent connectivity tensor of pores, is proposed, and the expression can be applied to isotropic media and also to anisotropic materials. Furthermore, evolution of porosity and equivalent connectivity index of the pore are studied in the strain space, and the method to determine the corresponding functions in expressions above is proposed using genetic algorithm and genetic programming. Two applications show that the results obtained by the method in this paper perfectly agree with the test data. This paper provides an important theoretical support to the coupled hydro-mechanical research.
基金financial support from the National Science Foundation of China (Grant Nos. 51922024, 41831282, 51678094 and 51578096)
文摘Grain crushing is commonly encountered in deep foundation engineering,high rockfill dam engineering,railway engineering,mining engineering,coastal engineering,petroleum engineering,and other geoscience application.Grain crushing is affected by fundamental soil characteristics,such as their mineral strength,grain size and distribution,grain shape,density and specimen size,and also by external factors including stress magnitude and path,loading rate and duration,degree of saturation,temperature and geochemical environment.Crushable material becomes a series of different materials with the change in its grading during grain crushing,resulting in a decrease in strength and dilatancy and an increase in compressibility.Effects of grain crushing on strength,dilatancy,deformation and failure mechanisms have been extensively investigated through laboratory testing,discrete element method(DEM)modelling,Weibull statistics,and constitutive modelling within the framework of the extended crushing-dependent critical state theory or the energy-based theory.Eleven papers summarized in this review article for this special issue addressed the above issues in grain crushing through the advanced testing and modelling.
文摘A non-linear continuum damage model was presented based on the irreversible thermodynamics framework developed by LEMAITRE and CHABOCHE. The proposed model was formulated by taking into account the influence of loading frequency on fatigue life. The parameters H and c are constants for frequency-independent materials, but functions of cyclic frequency for frequency-dependent materials. In addition, the expression of the model was discussed in detail at different stress ratios （R）. Fatigue test data of AlZnMgCu1.5 aluminium alloy and AMg6N alloy were used to verify the proposed model. The results showed that the model possesses a good ability of predicting fatigue life at different loading frequencies and stress ratios.
