Herein,we designed a core-shell structured bottlebrush copolymer(BBP),which is composed of rubbery poly(butyl acrylate)(PBA)core and an epoxy miscible/reactive poly(glycidyl methacrylate)(PGMA)shell,as an epoxy toughe...Herein,we designed a core-shell structured bottlebrush copolymer(BBP),which is composed of rubbery poly(butyl acrylate)(PBA)core and an epoxy miscible/reactive poly(glycidyl methacrylate)(PGMA)shell,as an epoxy tougheni ng age nt.The PGMA shell allows BBP to be uniformly dispersed within the epoxy matrix and to react with the epoxy groups,while the rubbery PBA block simultaneously induced nanocavitation effect,leading to improvement of mechanical properties of the epoxy resin.The mechanical properties were measured by the adhesion performance test,and the tensile and fracture test using universal testing machine.When BBP additives were added to the epoxy resin,a sign ifica nt improveme nt in the adhesion stren gth(2-fold increase)and fracture toughness(2-fold in crease in Kic and 5-fold in crease in Gic)compared to the neat epoxy was observed.In contrast,linear additives exhibited a decrease in adhesion strength and no improvement of fracture toughness over the neat epoxy.Such a difference in mechanical performance was investigated by comparing the morphologies and fracture surfaces of the epoxy resins containing linear and BBP additives,confirming that the nanocavitation effect and void formation play a key role in strengthening the BBP-modified epoxy resins.展开更多
We report a novel deep learning(DL)method for classifying inorganic compounds using 3D electron density data.We transform Density Functional Theory(DFT)-derived CHGCAR files from the Materials Project(MP)and experimen...We report a novel deep learning(DL)method for classifying inorganic compounds using 3D electron density data.We transform Density Functional Theory(DFT)-derived CHGCAR files from the Materials Project(MP)and experimental data from the Inorganic Crystal Structure Database(ICSD)into point clouds and sparse tensors,optimized for use in DLmodels such as PointNet and Sparse 3DCNN.This approach effectively overcomes the limitations of handling the dense 3D data,a common challenge in DL.Contrasting with traditional 1D or 2D X-ray diffraction(XRD)patterns that necessitate complex reciprocal space analysis,our method utilizes 3D density data for direct interpretation in real lattice space.This shift significantly enhances classification accuracy,outperforming traditional XRD-driven DL methods.We achieve accuracies of 97.28%,90.77%,and 90.10%for crystal system,extinction group,and space group classifications,respectively.Our 3D electron density-based DL approach not only showcases improved accuracy but also contributes a more intuitive and effective framework for materials discovery.展开更多
基金by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.2021R1A2C2006167)by the Global Frontier R&D Program(No.2013M3A6B1078869)on Center for Hybrid Interface Materials(HIM)funded by MSITalso by the Creative Materials Discovery Program through NRF grant funded by MSIT(No.2018M3D1A1058536).
文摘Herein,we designed a core-shell structured bottlebrush copolymer(BBP),which is composed of rubbery poly(butyl acrylate)(PBA)core and an epoxy miscible/reactive poly(glycidyl methacrylate)(PGMA)shell,as an epoxy tougheni ng age nt.The PGMA shell allows BBP to be uniformly dispersed within the epoxy matrix and to react with the epoxy groups,while the rubbery PBA block simultaneously induced nanocavitation effect,leading to improvement of mechanical properties of the epoxy resin.The mechanical properties were measured by the adhesion performance test,and the tensile and fracture test using universal testing machine.When BBP additives were added to the epoxy resin,a sign ifica nt improveme nt in the adhesion stren gth(2-fold increase)and fracture toughness(2-fold in crease in Kic and 5-fold in crease in Gic)compared to the neat epoxy was observed.In contrast,linear additives exhibited a decrease in adhesion strength and no improvement of fracture toughness over the neat epoxy.Such a difference in mechanical performance was investigated by comparing the morphologies and fracture surfaces of the epoxy resins containing linear and BBP additives,confirming that the nanocavitation effect and void formation play a key role in strengthening the BBP-modified epoxy resins.
基金supported by the Alchemist Project(20012196)funded by MOTIE,Koreapartly by the National Research Foundation of Korea(NRF)(2021R1A2C1009144 and RS-2024-00446825).
文摘We report a novel deep learning(DL)method for classifying inorganic compounds using 3D electron density data.We transform Density Functional Theory(DFT)-derived CHGCAR files from the Materials Project(MP)and experimental data from the Inorganic Crystal Structure Database(ICSD)into point clouds and sparse tensors,optimized for use in DLmodels such as PointNet and Sparse 3DCNN.This approach effectively overcomes the limitations of handling the dense 3D data,a common challenge in DL.Contrasting with traditional 1D or 2D X-ray diffraction(XRD)patterns that necessitate complex reciprocal space analysis,our method utilizes 3D density data for direct interpretation in real lattice space.This shift significantly enhances classification accuracy,outperforming traditional XRD-driven DL methods.We achieve accuracies of 97.28%,90.77%,and 90.10%for crystal system,extinction group,and space group classifications,respectively.Our 3D electron density-based DL approach not only showcases improved accuracy but also contributes a more intuitive and effective framework for materials discovery.
