Sr0.6 Ba0.4 Nb2 O6 micro-rods are prepared by the molten-salt method with K2 SO4,KCl-K2 SO4,and KCl as fluxes.It reveals that the Sr0.6 Ba0.4 Nb2 O6 synthesized with KCl as a flux exhibits a single phase with tetragon...Sr0.6 Ba0.4 Nb2 O6 micro-rods are prepared by the molten-salt method with K2 SO4,KCl-K2 SO4,and KCl as fluxes.It reveals that the Sr0.6 Ba0.4 Nb2 O6 synthesized with KCl as a flux exhibits a single phase with tetragonal tungsten bronze structure.The measurement of X-ray diffraction indicates that the Sr0.6 Ba0.4 Nb2 O6 micro-rods synthesized at 1 300℃are anisotropic.The morphology of the powers is examined by transmission electron microscope.It reveals that the length-diameter ratio of Sr0.6 Ba0.4 Nb2 O6 micro-rods increases with increasing annealing temperature from 900℃to 1 300℃.At 1 300℃,the rod possesses a large length-diameter ratio of 8∶1.Moreover,the analysis of the piezoelectric properties of single micro-rods using apiezo-response force microscope indicates that the domains of the material are arranged along its radial direction.展开更多
This study proposes a multi-scale simplified residual convolutional neural network(MS-SRCNN)for the precise prediction of Mg-Nd binary alloy compositions from scanning electron microscope(SEM)images.A multi-scale data...This study proposes a multi-scale simplified residual convolutional neural network(MS-SRCNN)for the precise prediction of Mg-Nd binary alloy compositions from scanning electron microscope(SEM)images.A multi-scale data structure is established by spatially aligning and stacking SEM images at different magnifications.The MS-SRCNN significantly reduces computational runtime by over 90%compared to traditional architectures like ResNet50,VGG16,and VGG19,without compromising prediction accuracy.The model demonstrates more excellent predictive performance,achieving a>5%increase in R^(2) compared to single-scale models.Furthermore,the MS-SRCNN exhibits robust composition prediction capability across other Mg-based binary alloys,including Mg-La,Mg-Sn,Mg-Ce,Mg-Sm,Mg-Ag,and Mg-Y,thereby emphasizing its generalization and extrapolation potential.This research establishes a non-destructive,microstructure-informed composition analysis framework,reduces characterization time compared to traditional experiment methods and provides insights into the composition-microstructure relationship in diverse material systems.展开更多
基金supported by the National Natural Science Foundation of China(No.11475086)
文摘Sr0.6 Ba0.4 Nb2 O6 micro-rods are prepared by the molten-salt method with K2 SO4,KCl-K2 SO4,and KCl as fluxes.It reveals that the Sr0.6 Ba0.4 Nb2 O6 synthesized with KCl as a flux exhibits a single phase with tetragonal tungsten bronze structure.The measurement of X-ray diffraction indicates that the Sr0.6 Ba0.4 Nb2 O6 micro-rods synthesized at 1 300℃are anisotropic.The morphology of the powers is examined by transmission electron microscope.It reveals that the length-diameter ratio of Sr0.6 Ba0.4 Nb2 O6 micro-rods increases with increasing annealing temperature from 900℃to 1 300℃.At 1 300℃,the rod possesses a large length-diameter ratio of 8∶1.Moreover,the analysis of the piezoelectric properties of single micro-rods using apiezo-response force microscope indicates that the domains of the material are arranged along its radial direction.
基金funded by the National Natural Science Foundation of China(No.52204407)the Natural Science Foundation of Jiangsu Province(No.BK20220595)the China Postdoctoral Science Foundation(No.2022M723689).
文摘This study proposes a multi-scale simplified residual convolutional neural network(MS-SRCNN)for the precise prediction of Mg-Nd binary alloy compositions from scanning electron microscope(SEM)images.A multi-scale data structure is established by spatially aligning and stacking SEM images at different magnifications.The MS-SRCNN significantly reduces computational runtime by over 90%compared to traditional architectures like ResNet50,VGG16,and VGG19,without compromising prediction accuracy.The model demonstrates more excellent predictive performance,achieving a>5%increase in R^(2) compared to single-scale models.Furthermore,the MS-SRCNN exhibits robust composition prediction capability across other Mg-based binary alloys,including Mg-La,Mg-Sn,Mg-Ce,Mg-Sm,Mg-Ag,and Mg-Y,thereby emphasizing its generalization and extrapolation potential.This research establishes a non-destructive,microstructure-informed composition analysis framework,reduces characterization time compared to traditional experiment methods and provides insights into the composition-microstructure relationship in diverse material systems.
