In modern physics and fabrication technology,simulation of projectile and target collision is vital to improve design in some critical applications,like;bulletproofing and medical applications.Graphene,the most promin...In modern physics and fabrication technology,simulation of projectile and target collision is vital to improve design in some critical applications,like;bulletproofing and medical applications.Graphene,the most prominent member of two dimensional materials presents ultrahigh tensile strength and stiffness.Moreover,polydimethylsiloxane(PDMS)is one of the most important elastomeric materials with a high extensive application area,ranging from medical,fabric,and interface material.In this work we considered graphene/PDMS structures to explore the bullet resistance of resulting nanocomposites.To this aim,extensive molecular dynamic simulations were carried out to identify the penetration of bullet through the graphene and PDMS composite structures.In this paper,we simulate the impact of a diamond bullet with different velocities on the composites made of single-or bi-layer graphene placed in different positions of PDMS polymers.The underlying mechanism concerning how the PDMS improves the resistance of graphene against impact loading is discussed.We discuss that with the same content of graphene,placing the graphene in between the PDMS result in enhanced bullet resistance.This work comparatively examines the enhancement in design of polymer nanocomposites to improve their bulletproofing response and the obtained results may serve as valuable guide for future experimental and theoretical studies.展开更多
Machine-learning(ML)models are novel and robust tools to establish structure-to-property connection on the basis of computationally expensive ab-initio datasets.For advanced technologies,predicting novel materials and...Machine-learning(ML)models are novel and robust tools to establish structure-to-property connection on the basis of computationally expensive ab-initio datasets.For advanced technologies,predicting novel materials and identifying their specification are critical issues.Two-dimensional(2D)materials are currently a rapidly growing class which show highly desirable properties for diverse advanced technologies.In this work,our objective is to search for desirable properties,such as the electronic band gap and total energy,among others,for which the accelerated prediction is highly appealing,prior to conducting accurate theoretical and experimental investigations.Among all available componential methods,gradient-boosted(GB)ML algorithms are known to provide highly accurate predictions and have shown great potential to predict material properties based on the importance of features.In this work,we applied the GB algorithm to a dataset of electronic and structural properties of 2D materials in order to predict the specification with high accuracy.Conducted statistical analysis of the selected features identifies design guidelines for the discovery of novel 2D materials with desired properties.展开更多
Over the past two decades,nanofluidics[1]has emerged as a pivotal field with profound implications across various disciplines,including fluid mechanics,condensed matter physics,engineering,materials science,and biomed...Over the past two decades,nanofluidics[1]has emerged as a pivotal field with profound implications across various disciplines,including fluid mechanics,condensed matter physics,engineering,materials science,and biomedicine.By confining and manipulating liquids,gases,ions,and other complex fluids at the nanoscale,nanofluidics enables unprecedented control over their behavior and properties.展开更多
Despite the uniquely high thermal conductivity of graphene is well known,the exploitation of graphene into thermally conductive nanomaterials and devices is limited by the inefficiency of thermal contacts between the ...Despite the uniquely high thermal conductivity of graphene is well known,the exploitation of graphene into thermally conductive nanomaterials and devices is limited by the inefficiency of thermal contacts between the individual nanosheets.A fascinating yet experimentally challenging route to enhance thermal conductance at contacts between graphene nanosheets is through molecular junctions,allowing covalently connecting nanosheets,otherwise interacting only via weak Van der Waals forces.Beside the bare existence of covalent connections,the choice of molecular structures to be used as thermal junctions should be guided by their vibrational properties,in terms of phonon transfer through the molecular junction.In this paper,density functional tight-binding combined with Green's functions formalism was applied for the calculation of thermal conductance and phonon spectra of several different aliphatic and aromatic molecular junctions between graphene nanosheets.Effects of molecular junction length,conformation,and aromaticity were studied in detail and correlated with phonon tunnelling spectra.The theoretical insight provided by this work can guide future experimental studies to select suitable molecular junctions,in order to enhance the thermal transport by suppressing the interfacial thermal resistances.This is attractive for various systems,including graphene nanopapers and graphene polymer nanocomposites,as well as related devices.In a broader view,the possibility to design molecular junctions to control phonon transport currently appears as an efficient way to produce phononic devices and controlling heat management in nanostructures.展开更多
基金B.M.and X.Z.appreciate the funding by the Deutsche Forschungsgemeinschaft(DFG,German Research Foundation)under Germany’s Excellence Strategy within the Cluster of Excellence PhoenixD(EXC 2122,Project ID 390833453).
文摘In modern physics and fabrication technology,simulation of projectile and target collision is vital to improve design in some critical applications,like;bulletproofing and medical applications.Graphene,the most prominent member of two dimensional materials presents ultrahigh tensile strength and stiffness.Moreover,polydimethylsiloxane(PDMS)is one of the most important elastomeric materials with a high extensive application area,ranging from medical,fabric,and interface material.In this work we considered graphene/PDMS structures to explore the bullet resistance of resulting nanocomposites.To this aim,extensive molecular dynamic simulations were carried out to identify the penetration of bullet through the graphene and PDMS composite structures.In this paper,we simulate the impact of a diamond bullet with different velocities on the composites made of single-or bi-layer graphene placed in different positions of PDMS polymers.The underlying mechanism concerning how the PDMS improves the resistance of graphene against impact loading is discussed.We discuss that with the same content of graphene,placing the graphene in between the PDMS result in enhanced bullet resistance.This work comparatively examines the enhancement in design of polymer nanocomposites to improve their bulletproofing response and the obtained results may serve as valuable guide for future experimental and theoretical studies.
文摘Machine-learning(ML)models are novel and robust tools to establish structure-to-property connection on the basis of computationally expensive ab-initio datasets.For advanced technologies,predicting novel materials and identifying their specification are critical issues.Two-dimensional(2D)materials are currently a rapidly growing class which show highly desirable properties for diverse advanced technologies.In this work,our objective is to search for desirable properties,such as the electronic band gap and total energy,among others,for which the accelerated prediction is highly appealing,prior to conducting accurate theoretical and experimental investigations.Among all available componential methods,gradient-boosted(GB)ML algorithms are known to provide highly accurate predictions and have shown great potential to predict material properties based on the importance of features.In this work,we applied the GB algorithm to a dataset of electronic and structural properties of 2D materials in order to predict the specification with high accuracy.Conducted statistical analysis of the selected features identifies design guidelines for the discovery of novel 2D materials with desired properties.
文摘Over the past two decades,nanofluidics[1]has emerged as a pivotal field with profound implications across various disciplines,including fluid mechanics,condensed matter physics,engineering,materials science,and biomedicine.By confining and manipulating liquids,gases,ions,and other complex fluids at the nanoscale,nanofluidics enables unprecedented control over their behavior and properties.
文摘Despite the uniquely high thermal conductivity of graphene is well known,the exploitation of graphene into thermally conductive nanomaterials and devices is limited by the inefficiency of thermal contacts between the individual nanosheets.A fascinating yet experimentally challenging route to enhance thermal conductance at contacts between graphene nanosheets is through molecular junctions,allowing covalently connecting nanosheets,otherwise interacting only via weak Van der Waals forces.Beside the bare existence of covalent connections,the choice of molecular structures to be used as thermal junctions should be guided by their vibrational properties,in terms of phonon transfer through the molecular junction.In this paper,density functional tight-binding combined with Green's functions formalism was applied for the calculation of thermal conductance and phonon spectra of several different aliphatic and aromatic molecular junctions between graphene nanosheets.Effects of molecular junction length,conformation,and aromaticity were studied in detail and correlated with phonon tunnelling spectra.The theoretical insight provided by this work can guide future experimental studies to select suitable molecular junctions,in order to enhance the thermal transport by suppressing the interfacial thermal resistances.This is attractive for various systems,including graphene nanopapers and graphene polymer nanocomposites,as well as related devices.In a broader view,the possibility to design molecular junctions to control phonon transport currently appears as an efficient way to produce phononic devices and controlling heat management in nanostructures.
