Large language models(LLMs)have demonstrated effectiveness in interpreting complex data.However,they encounter challenges in specialized applications,such as predicting material properties,due to limited integration w...Large language models(LLMs)have demonstrated effectiveness in interpreting complex data.However,they encounter challenges in specialized applications,such as predicting material properties,due to limited integration with domainspecific knowledge.To overcome these challenges,we introduce MatAgent,an artificial intelligence(AI)agent that combines computational chemistry tools,such as first-principles(FP)calculations,with the capabilities of LLMs to predict key properties of materials.By leveraging prompt engineering and advanced reasoning techniques,MatAgent integrates a series of tools and acquires domain-specific knowledge in the field of material property prediction,enabling it to accurately predict the properties of materials without the need for predefined input structures.The experimental results indicate that MatAgent achieves a significant improvement in prediction accuracy and efficiency.As a novel approach that integrates LLMs with FP calculation tools,MatAgent highlights the potential of combining advanced computational techniques to enhance material property predictions,representing a significant advancement in computational materials science.展开更多
The ability to rapidly evaluate materials properties through atomistic simulation approaches is the foundation of many new artificial intelligence-based approaches to materials identification and design.This depends o...The ability to rapidly evaluate materials properties through atomistic simulation approaches is the foundation of many new artificial intelligence-based approaches to materials identification and design.This depends on the availability of accurate descriptions of atomic bonding and an efficient means for determining materials properties.We present an efficient,robust platform for calculating materials properties from a wide-range of atomic bonding descriptions,i.e.,APEX,the Alloy Property Explorer.APEX enables the rapid evolution of interatomic potential development and optimization,which is of particular importance in fine-tuning new classes of general AI-based foundation models for applications in materials science and engineering.APEX is an open-source,extendable,cloud-native platform for material property calculations using a range of atomistic simulation methodologies that effectively manages diverse computational resources and is built upon user-friendly features including automatic results visualization,a web-based platform and a NoSQL database client.It is designed for expert and non-specialist users,lowering the barrier to entry for interdisciplinary research within an“AI for Materials”framework.We describe the foundation and use of APEX,as well as provide two examples of its application to properties of titanium and 179 metals and alloys for a wide-range of bonding descriptions.展开更多
A simulation can stand its ground against an experiment only if its prediction uncertainty is known.The unknown accuracy of interatomic potentials(IPs)is a major source of prediction uncertainty,severely limiting the ...A simulation can stand its ground against an experiment only if its prediction uncertainty is known.The unknown accuracy of interatomic potentials(IPs)is a major source of prediction uncertainty,severely limiting the use of large-scale classical atomistic simulations in a wide range of scientific and engineering applications.Here we explore covariance between predictions of metal plasticity,from 178 large-scale(~10^(8)atoms)molecular dynamics(MD)simulations,and a variety of indicator properties computed at small-scales(≤10^(2)atoms).All simulations use the same 178 IPs.In a manner similar to statistical studies in public health,weanalyze correlations of strength with indicators,identify the best predictor properties,and build a cross-scale“strength-on-predictors”regression model.This model is then used to estimate regression error over the statistical pool of IPs.Small-scale predictors found to be highly covariant with strength are computed using expensive quantum-accurate calculations and used to predict flow strength,within the statistical error bounds established in our study.展开更多
To compare the suitable working conditions of polypropylene(PP)and polycaprolactam(PA6)materials in actual use in automobiles,the effects of different temperature aging and different reagents on the mechanical propert...To compare the suitable working conditions of polypropylene(PP)and polycaprolactam(PA6)materials in actual use in automobiles,the effects of different temperature aging and different reagents on the mechanical properties of the two materials,such as tensile,bending,compression,and impact were studied.The results indicate that the short⁃term low⁃temperature environment had no much effect on the mechanical properties of PP and PA6.After long⁃term thermal aging at 80℃,the strength of PP and PA6 increased while their toughness decreased.After short⁃term thermal aging at 120℃,PP strength decreases and toughness increases,while PA6 strength increases and toughness decreases.The soaking of glass water and car shampoo had no much effect on the mechanical properties of PP,but had a significant impact on the mechanical properties of PA6.With the increase of soaking time,the strength of PA6 significantly decreases and the toughness significantly increases.The soaking of 95#gasoline had no much effect on the mechanical properties of PA6,but has a significant impact on the mechanical properties of PP.After 720 h of soaking,the retention rates of the tensile strength,bending strength,and compressive strength of PP were all less than 80%,while the retention rate of the impact strength of the cantilever beam was 160.4%.展开更多
Additive manufacturing(AM)technology has revolutionized engineering field by enabling the creation of intricate,high-performance structures that were once difficult or impossible to fabricate.This transformative techn...Additive manufacturing(AM)technology has revolutionized engineering field by enabling the creation of intricate,high-performance structures that were once difficult or impossible to fabricate.This transformative technology has particularly advanced the development of metamaterials-engineered materials whose unique properties arise from their structure rather than composition-unlocking immense potential in fields ranging from aerospace to biomedical engineering.展开更多
Soil cement bentonite(SCB)is a common material for constructing vertical cutoff walls to prevent groundwater migration at contaminated industrial sites.However,site contaminants can degrade the durability of the cutof...Soil cement bentonite(SCB)is a common material for constructing vertical cutoff walls to prevent groundwater migration at contaminated industrial sites.However,site contaminants can degrade the durability of the cutoff wall.To enhance its performance,this study developed a silica fume-SCB(SSCB).The macroscopic and microscopic properties of SSCB were assessed by unconfined compressive strength test,variable head permeability test,X-ray diffraction(XRD),scanning electron microscopy(SEM)and nuclear magnetic resonance(NMR)spectroscopy.The correlation between its multi-scale properties was analyzed based on pore characteristics.The results indicate that increasing the silica fume substitution ratio improved SSCB strength,especially in the middle and late curing stages.Moreover,increasing the substitution ratio decreased SSCB permeability coefficient,with a more pronounced effect in earlier curing stages.Silica fume addition also refined SSCB pore structure and reduced its porosity.The fractal dimension was used to quantify SSCB pore structure complexity.Increasing silica fume content reduced small pore fractal dimension in SSCB.Concurrently,SSCB strength increased and SSCB permeability coefficient decreased.The findings of this research will demonstrate the great potential of SSCB backfill for practical applications.展开更多
Legendre polynomial method is well-known in modeling acoustic wave characteristics.This method uses for the mechanical displacements a single polynomial expansion over the entire sandwich layers.This results in a limi...Legendre polynomial method is well-known in modeling acoustic wave characteristics.This method uses for the mechanical displacements a single polynomial expansion over the entire sandwich layers.This results in a limitation in the accuracy of the field profile restitution.Thus,it can deal with the guided waves in layered sandwich only when the material properties of adjacent layers do not change significantly.Despite the great efforts regarding this issue in the literature,there remain open questions.One of them is:“what is the exact threshold of contrasting material properties of adjacent layers for which this polynomial method cannot correctly restitute the roots of guided waves?”We investigated this numerical issue using the calculated guided phase velocities in 0°/φ/0°-carbon fibre reinforced plastics(CFRP)sandwich plates with gradually increasing angleφ.Then,we approached this numerical problem by varying the middle layer thickness h90°for the 0°/90°/0°-CFRP sandwich structure,and we proposed an exact thickness threshold of the middle layer for the Legendre polynomial method limitations.We showed that the polynomial method fails to calculate the quasi-symmetric Lamb mode in 0°/φ/0°-CFRP whenφ>25°.Moreover,we introduced a new Lamb mode so-called minimum-group-velocity that has never been addressed in literature.展开更多
To develop more efficient catalysts and discover new materials to work towards efficient solutions to the growing environmental problems,machine learning(ML)offers viable new ideas.Due to its ability to process large-...To develop more efficient catalysts and discover new materials to work towards efficient solutions to the growing environmental problems,machine learning(ML)offers viable new ideas.Due to its ability to process large-scale data and mine underlying patterns,ML has been widely used in the design and development of materials in recent years.The purpose of this manuscript is to summarize the research progress of ML to guide the development of materials in the environmental field and open new horizons for environmental pollution control.This manuscript firstly details the basic ML definitions and operational procedures.Secondly,it summarizes the main ways of applying ML in materials.Then it unfolds to introduce the specific application examples of ML in different materials.Finally,we summarize the shortcomings and research trends of ML in predicting material design.展开更多
[ Objective] The research aimed to study effects of material physical properties on white-rot fungi mycelial growth and provide theoretical basis for further expanding the application range of white-rot fungi. [ Metho...[ Objective] The research aimed to study effects of material physical properties on white-rot fungi mycelial growth and provide theoretical basis for further expanding the application range of white-rot fungi. [ Method Four common species of white-rot fungi were cultivated by wood meal fowl dung mixture in test tube and culture dishes. The relationship between physical properties of culture material and the growth of these mycelials were studied. [Result] The results showed the water retention capacity of culture material was decreased with the increasing of its grain size and porosity, but the decrease of its specific gravity reduced the material water retention. And the dehydration rate of medium prepared with these materials at the same moisture conditions tended to increase. These physical properties of material, such as grain size, specific gravity, porosity, water retention and water drainage, influenced the growth of white-rot fungi mycelial by affecting the moisture and ventilation condition of media. The results hinted that above material physical properties had feedback effects on the growth of white-rot fungi mycelia. [ Conclution] Physical properties of culture material have significant effects on the growth of white-rot fungi mycelial.展开更多
Intermetallic compounds have the characteristics of long-range ordered structure and combination of metallic and covalent bonds,showing intrinsic brittleness and outstanding performance stability.The synthesis mechani...Intermetallic compounds have the characteristics of long-range ordered structure and combination of metallic and covalent bonds,showing intrinsic brittleness and outstanding performance stability.The synthesis mechanism,pore structure characterization and material properties of powder metallurgy porous intermetallics are reviewed in this paper.Compared with traditional porous materials,porous intermetallics have good thermal impact resistance,machinability,thermal and electrical conductivity similar to metals,as well as good chemical corrosion resistance,rigidity and high-temperature property similar to ceramics.The mechanisms of preparation and pore formation of porous intermetallics mainly include four aspects:(1)the physical process based on the interstitial space between the initial particles and its evolution in the subsequent procedures;(2)the chemical combustion process based on the violent reaction between the initial powder components;(3)the reaction kinetics process based on the difference between the diffusion rates of elements;(4)the phase transition process based on the difference between the phase densities.The characterization parameters to the pore structure description for porous intermetallics include mainly overall porosity,open porosity,permeability,maximum pore size,pore size distribution and tortuosity factor.In terms of microstructure characterization of porous intermetallics,three-dimensional pore morphology scanning technology has the potential to reveal the internal characteristics of pore structures.The research on material properties of porous intermetallics mainly focuses on electrochemical catalytic activity,generalized oxidation resistivity at high temperature,resistance against chemical corrosion and mechanical properties,which have obvious advantages over traditional porous materials.In the field of the development of porous intermetallics,it is expected to expand their applications by further reducing the pore size to the nanoscale level to improve the filtration accuracy or increase the specific surface area,as well as introducing the high entropy design on the composition to improve the brittleness and enhance their material performance.展开更多
Only two macroscopic parameters are needed to describe the mechanical properties of linear elastic solids, i.e. the Poisson＇s ratio and Young＇s modulus. Correspondingly, there should be two microscopic parameters to...Only two macroscopic parameters are needed to describe the mechanical properties of linear elastic solids, i.e. the Poisson＇s ratio and Young＇s modulus. Correspondingly, there should be two microscopic parameters to determine the mechanical properties of material if the macroscopic mechanical properties of linear elastic solids are derived from the microscopic level. Enlightened by this idea, a multiscale mechanical model for material, the virtual multi-dimensional internal bonds （VMIB） model, is proposed by incorporating a shear bond into the virtual internal bond （VIB） model. By this modification, the VMIB model associates the macro mechanical properties of material with the microscopic mechanical properties of discrete structure and the corresponding relationship between micro and macro parameters is derived. The tensor quality of the energy density function, which contains coordinate vector, is mathematically proved. From the point of view of VMIB, the macroscopic nonlinear behaviors of material could be attributed to the evolution of virtual bond distribution density induced by the imposed deformation. With this theoretical hypothesis, as an application example, a uniaxial compressive failure of brittle material is simulated. Good agreement between the experimental results and the simulated ones is found.展开更多
Statistical manipulation of material data was conducted for probabilistic life assessment or risk-based design and maintenance for high temperature components of power plants. To obtain the statistical distribution of...Statistical manipulation of material data was conducted for probabilistic life assessment or risk-based design and maintenance for high temperature components of power plants. To obtain the statistical distribution of material properties, dominant parameters affecting material properties are introduced into normalization of statistical variables. Those parameters are hardness, chemical composition, characteristic micro structural features and so on. Creep and fatigue properties are expressed by normalized parameters and the unified statistical distributions are obtained. These probability distribution functions show good coincidence statistically with the field database of steam turbine components. It was concluded that the unified statistical baseline approach is useful for the risk management of components in power plants.展开更多
The properties and tensile behaviors of polypropylene (PP) geogrids and geonets for reinforcement of soil structures are investigated.Mass per unit area of the geogrids and geonets was weighed using an electronic bala...The properties and tensile behaviors of polypropylene (PP) geogrids and geonets for reinforcement of soil structures are investigated.Mass per unit area of the geogrids and geonets was weighed using an electronic balance and aperture sizes of the geonets were exactly measured using a computer.Laboratory tests were performed using a small tensile machine capable of monitoring tensile force and displacement.Tensile failure behaviors were described,and tensile index properties such as tensile strength,maximum tensile strain,tensile forces corresponding to different strains in the geogrids and gronets were obtained.The characterization of these indexes is discussed.展开更多
Large-volume presses(LVPs)are widely utilized in diverse research fields—including high-pressure physics,chemistry,materials science,and Earth and planetary sciences—to investigate the physical and chemical properti...Large-volume presses(LVPs)are widely utilized in diverse research fields—including high-pressure physics,chemistry,materials science,and Earth and planetary sciences—to investigate the physical and chemical properties of materials under extreme high-pressure and hightemperature conditions.A prerequisite for achieving reproducible property measurements is the determination and control of pressure within experimental setups.However,the lack of precise pressure calibration in LVPs hinders the broader application of such devices in ultrahigh-pressure studies.This study employs a suite of standard phase transition-based pressure markers—comprising metallic conductors,semiconductors,and minerals—through both in situ and ex situ identification approaches,to establish pressure calibration curves ranging from 0.4 to>30 GPa for various types of LVP installed at the Center for High Pressure Science and Technology Advanced Research(HPSTAR),Beijing,including piston–cylinder,cubic,and multi-anvil presses.The results provide a unified and traceable pressure reference for highpressure experiments conducted at HPSTAR,while also offering technical guidance and calibration standards for other researchers utilizing similar LVP systems,thereby enabling more consistent comparison between different laboratories.This work facilitates the advancement of LVP research toward broader applications in higher-pressure regimes.展开更多
Discovering new materials with excellent performance is a hot issue in the materials genome initiative.Traditional experiments and calculations often waste large amounts of time and money and are also limited by vario...Discovering new materials with excellent performance is a hot issue in the materials genome initiative.Traditional experiments and calculations often waste large amounts of time and money and are also limited by various conditions. Therefore, it is imperative to develop a new method to accelerate the discovery and design of new materials. In recent years, material discovery and design methods using machine learning have attracted much attention from material experts and have made some progress. This review first outlines available materials database and material data analytics tools and then elaborates on the machine learning algorithms used in materials science. Next, the field of application of machine learning in materials science is summarized, focusing on the aspects of structure determination, performance prediction, fingerprint prediction, and new material discovery. Finally, the review points out the problems of data and machine learning in materials science and points to future research. Using machine learning algorithms, the authors hope to achieve amazing results in material discovery and design.展开更多
The first author proposed the concept of the cemented material dam （CMD） in 2009. This concept was aimed at building an environmentally friendly dam in a safer and more economical way for both the dam and the area d...The first author proposed the concept of the cemented material dam （CMD） in 2009. This concept was aimed at building an environmentally friendly dam in a safer and more economical way for both the dam and the area downstream. The concept covers the cemented sand, gravel, and rock dam （CSGRD）, the rockfill concrete （RFC） dam （or the cemented rockfill dam, CRD）, and the cemented soil dam （CSD）. This paper summarizes the concept and principles of the CMD based on studies and practices in projects around the world. It also introduces new developments in the CSGRD, CRD, and CSD.展开更多
The discovery of new materials is one of the driving forces to promote the development of modern society and technology innovation,the traditional materials research mainly depended on the trial-and-error method,which...The discovery of new materials is one of the driving forces to promote the development of modern society and technology innovation,the traditional materials research mainly depended on the trial-and-error method,which is time-consuming and laborious.Recently,machine learning(ML)methods have made great progress in the researches of materials science with the arrival of the big-data era,which gives a deep revolution in human society and advance science greatly.However,there exist few systematic generalization and summaries about the applications of ML methods in materials science.In this review,we first provide a brief account of the progress of researches on materials science with ML employed,the main ideas and basic procedures of this method are emphatically introduced.Then the algorithms of ML which were frequently used in the researches of materials science are classified and compared.Finally,the recent meaningful applications of ML in metal materials,battery materials,photovoltaic materials and metallic glass are reviewed.展开更多
Material properties of blank have a great effect on power spinning process of aluminum alloy parts with transverse inner rib.By using finite element(FE) and Taguchi method,the effects and significance of five key mate...Material properties of blank have a great effect on power spinning process of aluminum alloy parts with transverse inner rib.By using finite element(FE) and Taguchi method,the effects and significance of five key material parameters,namely,anisotropic index in thickness direction,yield strength,hardening exponent,strengthening factor and elastic modulus on the formability of inner rib,tendency of wall fracture and degree of inhomogeneous deformation of finished spun parts were obtained.The achievements provide an important guide for selecting reasonable spinning material,and are very significant for the optimum design and precision control of power spinning process of parts with transverse inner rib.展开更多
Anionomer-type waterborne polyurethane dispersions （PUDs） were obtained from poly （propylene glycol） （PPG）, isophorone diisocyanate （IPDI） and dimethylolpropionic acid （DMPA） through a modified prepolymer is...Anionomer-type waterborne polyurethane dispersions （PUDs） were obtained from poly （propylene glycol） （PPG）, isophorone diisocyanate （IPDI） and dimethylolpropionic acid （DMPA） through a modified prepolymer isocyanate process. Two series of polyurethanes were prepared （Groups A and B） and a new prediction model based on grey relational analysis is introduced to predict the impact order of raw materials on several properties, such as solids content, viscosity, acid number and electrolytic stability of polyure- thanes. It is found that the model can successfully predict the impact of raw materials on the properties through the designed demonstration experiments. Furthermore, the results of the prediction model show that DMPA plays a key role in viscosity, oartial acid values and electrolvtic stability.展开更多
This study aims at proposing a reasonable roughness parameter that can reflect the peak shear strength(PSS)of rock joints.Firstly,the contribution of the asperities with different apparent dip angles to shear strength...This study aims at proposing a reasonable roughness parameter that can reflect the peak shear strength(PSS)of rock joints.Firstly,the contribution of the asperities with different apparent dip angles to shear strength is studied.Then the shear strength of the entire joint asperities is derived.The results showed that the PSS of the entire joint asperities is proportional to a key parameter hs,which is related to the geometric character of the joint surface and the joint material properties.The parameter hsis taken as the new roughness parameter,and it is reasonable to associate the PSS with the geometric characteristics of the joint surface.Based on the new roughness parameter and shear test results of 20 sets of joint specimens,a new PSS model for rock joints is proposed.The new model is validated with the artificial joints in this paper and real rock joints in published studies.Results showed that it is suitable for different types of rock joints except for gneiss joints.The new model has the form of the Mohr-Coulomb model,which can directly reflect the relationship between the 3 D roughness parameters and the peak dilation angle.展开更多
基金supported by the Anhui Province Science and Technology Innovation Project(Grant No.202423k09020010)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB0450101)+3 种基金the Innovation Program for Quantum Science and Technology(No.2021ZD0303306)the National Natural Science Foundation of China(21688102,22173093,and 22373096)the Anhui Provincial Key Research and Development Program(2022a05020052)the National Key Research and Development Program of China(2021YFB0300600).
文摘Large language models(LLMs)have demonstrated effectiveness in interpreting complex data.However,they encounter challenges in specialized applications,such as predicting material properties,due to limited integration with domainspecific knowledge.To overcome these challenges,we introduce MatAgent,an artificial intelligence(AI)agent that combines computational chemistry tools,such as first-principles(FP)calculations,with the capabilities of LLMs to predict key properties of materials.By leveraging prompt engineering and advanced reasoning techniques,MatAgent integrates a series of tools and acquires domain-specific knowledge in the field of material property prediction,enabling it to accurately predict the properties of materials without the need for predefined input structures.The experimental results indicate that MatAgent achieves a significant improvement in prediction accuracy and efficiency.As a novel approach that integrates LLMs with FP calculation tools,MatAgent highlights the potential of combining advanced computational techniques to enhance material property predictions,representing a significant advancement in computational materials science.
基金supported by the Research Grants Council,Hong Kong SAR through the General Research Fund(17210723,17200424)the support of The University of Hong Kong via seed fund(2201100392)+2 种基金supported by the National Key R&D Program of China(Grant No.2022YFA1004300)the National Natural Science Foundation of China(Grant No.12122103)startup funding from Materials Innovation Institute for Life Sciences and Energy(MILES),HKU-SIRI in Shenzhen for support of this manuscript.
文摘The ability to rapidly evaluate materials properties through atomistic simulation approaches is the foundation of many new artificial intelligence-based approaches to materials identification and design.This depends on the availability of accurate descriptions of atomic bonding and an efficient means for determining materials properties.We present an efficient,robust platform for calculating materials properties from a wide-range of atomic bonding descriptions,i.e.,APEX,the Alloy Property Explorer.APEX enables the rapid evolution of interatomic potential development and optimization,which is of particular importance in fine-tuning new classes of general AI-based foundation models for applications in materials science and engineering.APEX is an open-source,extendable,cloud-native platform for material property calculations using a range of atomistic simulation methodologies that effectively manages diverse computational resources and is built upon user-friendly features including automatic results visualization,a web-based platform and a NoSQL database client.It is designed for expert and non-specialist users,lowering the barrier to entry for interdisciplinary research within an“AI for Materials”framework.We describe the foundation and use of APEX,as well as provide two examples of its application to properties of titanium and 179 metals and alloys for a wide-range of bonding descriptions.
基金supported by the National Science Foundation(NSF)under grant no.1922758.E.B.T.and I.N.acknowledge partial support through NSF under grant no.1834251 and 1834332funding support from the Laboratory Directed Research and Development program(tracking number 23-SI-006)+1 种基金a special computational time allocation on the Lassen supercomputer from the Computational Grand Challenge program at Lawrence Livermore National LaboratoryThis work was performed under the auspices of the U.S.Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.
文摘A simulation can stand its ground against an experiment only if its prediction uncertainty is known.The unknown accuracy of interatomic potentials(IPs)is a major source of prediction uncertainty,severely limiting the use of large-scale classical atomistic simulations in a wide range of scientific and engineering applications.Here we explore covariance between predictions of metal plasticity,from 178 large-scale(~10^(8)atoms)molecular dynamics(MD)simulations,and a variety of indicator properties computed at small-scales(≤10^(2)atoms).All simulations use the same 178 IPs.In a manner similar to statistical studies in public health,weanalyze correlations of strength with indicators,identify the best predictor properties,and build a cross-scale“strength-on-predictors”regression model.This model is then used to estimate regression error over the statistical pool of IPs.Small-scale predictors found to be highly covariant with strength are computed using expensive quantum-accurate calculations and used to predict flow strength,within the statistical error bounds established in our study.
文摘To compare the suitable working conditions of polypropylene(PP)and polycaprolactam(PA6)materials in actual use in automobiles,the effects of different temperature aging and different reagents on the mechanical properties of the two materials,such as tensile,bending,compression,and impact were studied.The results indicate that the short⁃term low⁃temperature environment had no much effect on the mechanical properties of PP and PA6.After long⁃term thermal aging at 80℃,the strength of PP and PA6 increased while their toughness decreased.After short⁃term thermal aging at 120℃,PP strength decreases and toughness increases,while PA6 strength increases and toughness decreases.The soaking of glass water and car shampoo had no much effect on the mechanical properties of PP,but had a significant impact on the mechanical properties of PA6.With the increase of soaking time,the strength of PA6 significantly decreases and the toughness significantly increases.The soaking of 95#gasoline had no much effect on the mechanical properties of PA6,but has a significant impact on the mechanical properties of PP.After 720 h of soaking,the retention rates of the tensile strength,bending strength,and compressive strength of PP were all less than 80%,while the retention rate of the impact strength of the cantilever beam was 160.4%.
文摘Additive manufacturing(AM)technology has revolutionized engineering field by enabling the creation of intricate,high-performance structures that were once difficult or impossible to fabricate.This transformative technology has particularly advanced the development of metamaterials-engineered materials whose unique properties arise from their structure rather than composition-unlocking immense potential in fields ranging from aerospace to biomedical engineering.
基金Project(2019YFC1803601)supported by the National Key Research and Development Program of ChinaProject(52274182)supported by the National Natural Science Foundation of China+1 种基金Project(2021zzts0274)supported by the Fundamental Research Funds for the Central Universities,ChinaProject(CX20210295)supported by the Postgraduate Scientific Research Innovation Project of Hunan Province,China。
文摘Soil cement bentonite(SCB)is a common material for constructing vertical cutoff walls to prevent groundwater migration at contaminated industrial sites.However,site contaminants can degrade the durability of the cutoff wall.To enhance its performance,this study developed a silica fume-SCB(SSCB).The macroscopic and microscopic properties of SSCB were assessed by unconfined compressive strength test,variable head permeability test,X-ray diffraction(XRD),scanning electron microscopy(SEM)and nuclear magnetic resonance(NMR)spectroscopy.The correlation between its multi-scale properties was analyzed based on pore characteristics.The results indicate that increasing the silica fume substitution ratio improved SSCB strength,especially in the middle and late curing stages.Moreover,increasing the substitution ratio decreased SSCB permeability coefficient,with a more pronounced effect in earlier curing stages.Silica fume addition also refined SSCB pore structure and reduced its porosity.The fractal dimension was used to quantify SSCB pore structure complexity.Increasing silica fume content reduced small pore fractal dimension in SSCB.Concurrently,SSCB strength increased and SSCB permeability coefficient decreased.The findings of this research will demonstrate the great potential of SSCB backfill for practical applications.
基金supported by the National Natural Science Foundation of China(Grant No.12102131).
文摘Legendre polynomial method is well-known in modeling acoustic wave characteristics.This method uses for the mechanical displacements a single polynomial expansion over the entire sandwich layers.This results in a limitation in the accuracy of the field profile restitution.Thus,it can deal with the guided waves in layered sandwich only when the material properties of adjacent layers do not change significantly.Despite the great efforts regarding this issue in the literature,there remain open questions.One of them is:“what is the exact threshold of contrasting material properties of adjacent layers for which this polynomial method cannot correctly restitute the roots of guided waves?”We investigated this numerical issue using the calculated guided phase velocities in 0°/φ/0°-carbon fibre reinforced plastics(CFRP)sandwich plates with gradually increasing angleφ.Then,we approached this numerical problem by varying the middle layer thickness h90°for the 0°/90°/0°-CFRP sandwich structure,and we proposed an exact thickness threshold of the middle layer for the Legendre polynomial method limitations.We showed that the polynomial method fails to calculate the quasi-symmetric Lamb mode in 0°/φ/0°-CFRP whenφ>25°.Moreover,we introduced a new Lamb mode so-called minimum-group-velocity that has never been addressed in literature.
基金the National Natural Science Foundation of China(Nos.52370083 and 52170088)Sichuan Science and Technology Program(No.2024NSFTD0014)Key R&D Program of Heilongjiang Province(No.2023ZX02C01)for financial support。
文摘To develop more efficient catalysts and discover new materials to work towards efficient solutions to the growing environmental problems,machine learning(ML)offers viable new ideas.Due to its ability to process large-scale data and mine underlying patterns,ML has been widely used in the design and development of materials in recent years.The purpose of this manuscript is to summarize the research progress of ML to guide the development of materials in the environmental field and open new horizons for environmental pollution control.This manuscript firstly details the basic ML definitions and operational procedures.Secondly,it summarizes the main ways of applying ML in materials.Then it unfolds to introduce the specific application examples of ML in different materials.Finally,we summarize the shortcomings and research trends of ML in predicting material design.
基金Supported by Qian Jiang Manpower Program of Zhejiang Province Science and Technology Department (No.2007R10039)National Basic Research Program of China (No.2005CB724204)Under-graduate Technology Innovation Program of Zhejiang Province~~
文摘[ Objective] The research aimed to study effects of material physical properties on white-rot fungi mycelial growth and provide theoretical basis for further expanding the application range of white-rot fungi. [ Method Four common species of white-rot fungi were cultivated by wood meal fowl dung mixture in test tube and culture dishes. The relationship between physical properties of culture material and the growth of these mycelials were studied. [Result] The results showed the water retention capacity of culture material was decreased with the increasing of its grain size and porosity, but the decrease of its specific gravity reduced the material water retention. And the dehydration rate of medium prepared with these materials at the same moisture conditions tended to increase. These physical properties of material, such as grain size, specific gravity, porosity, water retention and water drainage, influenced the growth of white-rot fungi mycelial by affecting the moisture and ventilation condition of media. The results hinted that above material physical properties had feedback effects on the growth of white-rot fungi mycelia. [ Conclution] Physical properties of culture material have significant effects on the growth of white-rot fungi mycelial.
基金the National Natural Science Foundation of China (No. 51971251, 51774336)。
文摘Intermetallic compounds have the characteristics of long-range ordered structure and combination of metallic and covalent bonds,showing intrinsic brittleness and outstanding performance stability.The synthesis mechanism,pore structure characterization and material properties of powder metallurgy porous intermetallics are reviewed in this paper.Compared with traditional porous materials,porous intermetallics have good thermal impact resistance,machinability,thermal and electrical conductivity similar to metals,as well as good chemical corrosion resistance,rigidity and high-temperature property similar to ceramics.The mechanisms of preparation and pore formation of porous intermetallics mainly include four aspects:(1)the physical process based on the interstitial space between the initial particles and its evolution in the subsequent procedures;(2)the chemical combustion process based on the violent reaction between the initial powder components;(3)the reaction kinetics process based on the difference between the diffusion rates of elements;(4)the phase transition process based on the difference between the phase densities.The characterization parameters to the pore structure description for porous intermetallics include mainly overall porosity,open porosity,permeability,maximum pore size,pore size distribution and tortuosity factor.In terms of microstructure characterization of porous intermetallics,three-dimensional pore morphology scanning technology has the potential to reveal the internal characteristics of pore structures.The research on material properties of porous intermetallics mainly focuses on electrochemical catalytic activity,generalized oxidation resistivity at high temperature,resistance against chemical corrosion and mechanical properties,which have obvious advantages over traditional porous materials.In the field of the development of porous intermetallics,it is expected to expand their applications by further reducing the pore size to the nanoscale level to improve the filtration accuracy or increase the specific surface area,as well as introducing the high entropy design on the composition to improve the brittleness and enhance their material performance.
基金Project supported by the National Basic Research Program of China (973 Project) (No. 2002CB412704).
文摘Only two macroscopic parameters are needed to describe the mechanical properties of linear elastic solids, i.e. the Poisson＇s ratio and Young＇s modulus. Correspondingly, there should be two microscopic parameters to determine the mechanical properties of material if the macroscopic mechanical properties of linear elastic solids are derived from the microscopic level. Enlightened by this idea, a multiscale mechanical model for material, the virtual multi-dimensional internal bonds （VMIB） model, is proposed by incorporating a shear bond into the virtual internal bond （VIB） model. By this modification, the VMIB model associates the macro mechanical properties of material with the microscopic mechanical properties of discrete structure and the corresponding relationship between micro and macro parameters is derived. The tensor quality of the energy density function, which contains coordinate vector, is mathematically proved. From the point of view of VMIB, the macroscopic nonlinear behaviors of material could be attributed to the evolution of virtual bond distribution density induced by the imposed deformation. With this theoretical hypothesis, as an application example, a uniaxial compressive failure of brittle material is simulated. Good agreement between the experimental results and the simulated ones is found.
文摘Statistical manipulation of material data was conducted for probabilistic life assessment or risk-based design and maintenance for high temperature components of power plants. To obtain the statistical distribution of material properties, dominant parameters affecting material properties are introduced into normalization of statistical variables. Those parameters are hardness, chemical composition, characteristic micro structural features and so on. Creep and fatigue properties are expressed by normalized parameters and the unified statistical distributions are obtained. These probability distribution functions show good coincidence statistically with the field database of steam turbine components. It was concluded that the unified statistical baseline approach is useful for the risk management of components in power plants.
文摘The properties and tensile behaviors of polypropylene (PP) geogrids and geonets for reinforcement of soil structures are investigated.Mass per unit area of the geogrids and geonets was weighed using an electronic balance and aperture sizes of the geonets were exactly measured using a computer.Laboratory tests were performed using a small tensile machine capable of monitoring tensile force and displacement.Tensile failure behaviors were described,and tensile index properties such as tensile strength,maximum tensile strain,tensile forces corresponding to different strains in the geogrids and gronets were obtained.The characterization of these indexes is discussed.
基金supported by the National Science Foundation of China(Grant Nos.U1530402 and U1930401).
文摘Large-volume presses(LVPs)are widely utilized in diverse research fields—including high-pressure physics,chemistry,materials science,and Earth and planetary sciences—to investigate the physical and chemical properties of materials under extreme high-pressure and hightemperature conditions.A prerequisite for achieving reproducible property measurements is the determination and control of pressure within experimental setups.However,the lack of precise pressure calibration in LVPs hinders the broader application of such devices in ultrahigh-pressure studies.This study employs a suite of standard phase transition-based pressure markers—comprising metallic conductors,semiconductors,and minerals—through both in situ and ex situ identification approaches,to establish pressure calibration curves ranging from 0.4 to>30 GPa for various types of LVP installed at the Center for High Pressure Science and Technology Advanced Research(HPSTAR),Beijing,including piston–cylinder,cubic,and multi-anvil presses.The results provide a unified and traceable pressure reference for highpressure experiments conducted at HPSTAR,while also offering technical guidance and calibration standards for other researchers utilizing similar LVP systems,thereby enabling more consistent comparison between different laboratories.This work facilitates the advancement of LVP research toward broader applications in higher-pressure regimes.
基金financially supported by the National Natural Science Foundation of China (Nos. 61971208, 61671225 and 51864027)the Yunnan Applied Basic Research Projects (No. 2018FA034)+2 种基金the Yunnan Reserve Talents of Young and Middleaged Academic and Technical Leaders (Shen Tao, 2018)the Yunnan Young Top Talents of Ten Thousands Plan (Shen Tao, Zhu Yan, Yunren Social Development No. 2018 73)the Scientific Research Foundation of Kunming University of Science and Technology (No. KKSY201703016)。
文摘Discovering new materials with excellent performance is a hot issue in the materials genome initiative.Traditional experiments and calculations often waste large amounts of time and money and are also limited by various conditions. Therefore, it is imperative to develop a new method to accelerate the discovery and design of new materials. In recent years, material discovery and design methods using machine learning have attracted much attention from material experts and have made some progress. This review first outlines available materials database and material data analytics tools and then elaborates on the machine learning algorithms used in materials science. Next, the field of application of machine learning in materials science is summarized, focusing on the aspects of structure determination, performance prediction, fingerprint prediction, and new material discovery. Finally, the review points out the problems of data and machine learning in materials science and points to future research. Using machine learning algorithms, the authors hope to achieve amazing results in material discovery and design.
文摘The first author proposed the concept of the cemented material dam （CMD） in 2009. This concept was aimed at building an environmentally friendly dam in a safer and more economical way for both the dam and the area downstream. The concept covers the cemented sand, gravel, and rock dam （CSGRD）, the rockfill concrete （RFC） dam （or the cemented rockfill dam, CRD）, and the cemented soil dam （CSD）. This paper summarizes the concept and principles of the CMD based on studies and practices in projects around the world. It also introduces new developments in the CSGRD, CRD, and CSD.
基金This work was financially supported by the National Natural Science Foundation of China(No.51627802)。
文摘The discovery of new materials is one of the driving forces to promote the development of modern society and technology innovation,the traditional materials research mainly depended on the trial-and-error method,which is time-consuming and laborious.Recently,machine learning(ML)methods have made great progress in the researches of materials science with the arrival of the big-data era,which gives a deep revolution in human society and advance science greatly.However,there exist few systematic generalization and summaries about the applications of ML methods in materials science.In this review,we first provide a brief account of the progress of researches on materials science with ML employed,the main ideas and basic procedures of this method are emphatically introduced.Then the algorithms of ML which were frequently used in the researches of materials science are classified and compared.Finally,the recent meaningful applications of ML in metal materials,battery materials,photovoltaic materials and metallic glass are reviewed.
基金Projects(50405039,50575186) supported by the National Natural Science Foundation of ChinaProject(50225518) supported by the National Natural Science Foundation of China for Distinguished Young ScholarsProject(2008AA04Z122) supported by the National High-tech Research and Development Program of China
文摘Material properties of blank have a great effect on power spinning process of aluminum alloy parts with transverse inner rib.By using finite element(FE) and Taguchi method,the effects and significance of five key material parameters,namely,anisotropic index in thickness direction,yield strength,hardening exponent,strengthening factor and elastic modulus on the formability of inner rib,tendency of wall fracture and degree of inhomogeneous deformation of finished spun parts were obtained.The achievements provide an important guide for selecting reasonable spinning material,and are very significant for the optimum design and precision control of power spinning process of parts with transverse inner rib.
基金?nancial support provided by Program for New Century Excellent Talents in University by the Ministry of Education of China(Grant No.NCET-12-1045)the Shaanxi Programs for Science and Technology Development(No.2010K01-096)Ph.D.Innovation Fund Projects of Xi’an University of Technology(No.310-252071501)
文摘Anionomer-type waterborne polyurethane dispersions （PUDs） were obtained from poly （propylene glycol） （PPG）, isophorone diisocyanate （IPDI） and dimethylolpropionic acid （DMPA） through a modified prepolymer isocyanate process. Two series of polyurethanes were prepared （Groups A and B） and a new prediction model based on grey relational analysis is introduced to predict the impact order of raw materials on several properties, such as solids content, viscosity, acid number and electrolytic stability of polyure- thanes. It is found that the model can successfully predict the impact of raw materials on the properties through the designed demonstration experiments. Furthermore, the results of the prediction model show that DMPA plays a key role in viscosity, oartial acid values and electrolvtic stability.
基金supported by China Postdoctoral Science Foundation(No.2020M680007)Beijing Postdoctoral Research Foundation(No.2020-zz-087)+1 种基金National Natural Science Foundation of China(Nos.51478027 and 51174012)Fundamental Research Funds for Beijing Civil Engineering and Architecture(No.X20031)。
文摘This study aims at proposing a reasonable roughness parameter that can reflect the peak shear strength(PSS)of rock joints.Firstly,the contribution of the asperities with different apparent dip angles to shear strength is studied.Then the shear strength of the entire joint asperities is derived.The results showed that the PSS of the entire joint asperities is proportional to a key parameter hs,which is related to the geometric character of the joint surface and the joint material properties.The parameter hsis taken as the new roughness parameter,and it is reasonable to associate the PSS with the geometric characteristics of the joint surface.Based on the new roughness parameter and shear test results of 20 sets of joint specimens,a new PSS model for rock joints is proposed.The new model is validated with the artificial joints in this paper and real rock joints in published studies.Results showed that it is suitable for different types of rock joints except for gneiss joints.The new model has the form of the Mohr-Coulomb model,which can directly reflect the relationship between the 3 D roughness parameters and the peak dilation angle.
