Patterns and rates of deposition,migration and retention of pollutants in man-made strata depend on the depositional history and physical and chemical characteristics of the constituent materials.A sound understanding...Patterns and rates of deposition,migration and retention of pollutants in man-made strata depend on the depositional history and physical and chemical characteristics of the constituent materials.A sound understanding of the spatial and chronological relationships of materials is required for effective evaluation of solid,liquid and gaseous geo-pollution and design and understanding and interpretation of investigations of potentially contaminated land.Description of materials and boundaries requires clear terminology.A proposed terminology has been developed based on experience from a variety of sites in Japan including the experimental infilling of an old quarry.The terminology is commended for further discussion.展开更多
Organic polymer materials,as the most abundantly produced materials,possess a flammable nature,making them potential hazards to human casualties and property losses.Target polymer design is still hindered due to the l...Organic polymer materials,as the most abundantly produced materials,possess a flammable nature,making them potential hazards to human casualties and property losses.Target polymer design is still hindered due to the lack of a scientific foundation.Herein,we present a robust,generalizable,yet intelligent polymer discovery framework,which synergizes diverse capabilities,including the in situ burning analyzer,virtual reaction generator,and material genomic model,to achieve results that surpass the sum of individual parts.Notably,the high-throughput analyzer created for the first time,grounded in multiple spectroscopic principles,enables in situ capturing of massive combustion intermediates;then,the created realistic apparatus transforming to the virtual reaction generator acquires exponentially more intermediate information;further,the proposed feature engineering tool,which embedded both polymer hierarchical structures and massive intermediate data,develops the generalizable genomic model with excellent universality(adapting over 20 kinds of polymers)and high accuracy(88.8%),succeeding discovering series of novel polymers.This emerging approach addresses the target polymer design for flame-retardant application and underscores a pivotal role in accelerating polymeric materials discovery.展开更多
CONSPECTUS:Additive Manufacturing(AM)technology produces three-dimensional components in a layer-by-layer fashion and offers numerous advantages over conventional manufacturing processes.Driven by the growing needs of...CONSPECTUS:Additive Manufacturing(AM)technology produces three-dimensional components in a layer-by-layer fashion and offers numerous advantages over conventional manufacturing processes.Driven by the growing needs of diverse industrial sectors,this technology has seen significant advances on both scientific and engineering fronts.Fusion-based processes are the mainstream techniques for AM of metallic materials.As the metals go through melting and solidification during the printing processes,the final microstructure and hence the properties of the printed components are highly sensitive to the printing conditions and can be very different from those of the feedstock.It is critical to understand the process-microstructure-property relationship for the accelerated optimization of the processing conditions and certification of the printed components.While experimentation has been used widely to acquire a mechanistic understanding of this subject matter,numerical modeling has become increasingly helpful in achieving the same purpose.In this Account,the authors review their ongoing collaborative effort to establish a multiphysics modeling framework to predict the process-microstructure-property relationship in fusion-based metal AM processes.The framework includes three individual modules to simulate the dominating physics that dictate the process dynamics and microstructure evolution during printing as well as the responses of the printed microstructure to specific mechanical loadings.The process model uses the material properties and processing conditions as the inputs and simulates the laser-material interaction,multiphase thermo-fluid flow,and fluid-driven powder motion.It has successfully revealed the physical causes of depression zone shape variation as well as powder motion during the laser powder bed fusion process.The microstructure model uses the thermal history of the printing process and the material chemistry as the inputs and predicts the nucleation and growth of multiple grains in the multipass and multilayer printing processes.It has been used to understand the effects of inoculation and thermal conditions on grain texture evolution.The property models use microstructure data from simulations,experimental measurements,or statistical analyses as the inputs and leverage various computational tools to predict the mechanical response of the AM materials.These models have been used to quantitatively evaluate the effects of grain structure,residual strain,and pore and void defects on their properties and performance.While this and many other modeling works have significantly grown our collective knowledge of the process-microstructure-property relationship in fusion-based metal AM processes,efforts should be further invested in developing advanced theories and algorithms for the governing physics,leveraging data-driven approaches,accelerating simulation speed,and calibrating/validating models with controlled experimental measurements,among other aspects.展开更多
文摘Patterns and rates of deposition,migration and retention of pollutants in man-made strata depend on the depositional history and physical and chemical characteristics of the constituent materials.A sound understanding of the spatial and chronological relationships of materials is required for effective evaluation of solid,liquid and gaseous geo-pollution and design and understanding and interpretation of investigations of potentially contaminated land.Description of materials and boundaries requires clear terminology.A proposed terminology has been developed based on experience from a variety of sites in Japan including the experimental infilling of an old quarry.The terminology is commended for further discussion.
基金supported by the National Natural Science Foundation of China(51991351,51827803,52103122,and 22375138)the Institutional Research Fund from Sichuan University(no.2021SCUNL201)the Fundamental Research Funds for the Central Universities,and the 111 project(B20001).
文摘Organic polymer materials,as the most abundantly produced materials,possess a flammable nature,making them potential hazards to human casualties and property losses.Target polymer design is still hindered due to the lack of a scientific foundation.Herein,we present a robust,generalizable,yet intelligent polymer discovery framework,which synergizes diverse capabilities,including the in situ burning analyzer,virtual reaction generator,and material genomic model,to achieve results that surpass the sum of individual parts.Notably,the high-throughput analyzer created for the first time,grounded in multiple spectroscopic principles,enables in situ capturing of massive combustion intermediates;then,the created realistic apparatus transforming to the virtual reaction generator acquires exponentially more intermediate information;further,the proposed feature engineering tool,which embedded both polymer hierarchical structures and massive intermediate data,develops the generalizable genomic model with excellent universality(adapting over 20 kinds of polymers)and high accuracy(88.8%),succeeding discovering series of novel polymers.This emerging approach addresses the target polymer design for flame-retardant application and underscores a pivotal role in accelerating polymeric materials discovery.
基金support provided by the National Science Foundation under Grant No.CMMI-2119671.
文摘CONSPECTUS:Additive Manufacturing(AM)technology produces three-dimensional components in a layer-by-layer fashion and offers numerous advantages over conventional manufacturing processes.Driven by the growing needs of diverse industrial sectors,this technology has seen significant advances on both scientific and engineering fronts.Fusion-based processes are the mainstream techniques for AM of metallic materials.As the metals go through melting and solidification during the printing processes,the final microstructure and hence the properties of the printed components are highly sensitive to the printing conditions and can be very different from those of the feedstock.It is critical to understand the process-microstructure-property relationship for the accelerated optimization of the processing conditions and certification of the printed components.While experimentation has been used widely to acquire a mechanistic understanding of this subject matter,numerical modeling has become increasingly helpful in achieving the same purpose.In this Account,the authors review their ongoing collaborative effort to establish a multiphysics modeling framework to predict the process-microstructure-property relationship in fusion-based metal AM processes.The framework includes three individual modules to simulate the dominating physics that dictate the process dynamics and microstructure evolution during printing as well as the responses of the printed microstructure to specific mechanical loadings.The process model uses the material properties and processing conditions as the inputs and simulates the laser-material interaction,multiphase thermo-fluid flow,and fluid-driven powder motion.It has successfully revealed the physical causes of depression zone shape variation as well as powder motion during the laser powder bed fusion process.The microstructure model uses the thermal history of the printing process and the material chemistry as the inputs and predicts the nucleation and growth of multiple grains in the multipass and multilayer printing processes.It has been used to understand the effects of inoculation and thermal conditions on grain texture evolution.The property models use microstructure data from simulations,experimental measurements,or statistical analyses as the inputs and leverage various computational tools to predict the mechanical response of the AM materials.These models have been used to quantitatively evaluate the effects of grain structure,residual strain,and pore and void defects on their properties and performance.While this and many other modeling works have significantly grown our collective knowledge of the process-microstructure-property relationship in fusion-based metal AM processes,efforts should be further invested in developing advanced theories and algorithms for the governing physics,leveraging data-driven approaches,accelerating simulation speed,and calibrating/validating models with controlled experimental measurements,among other aspects.
