To gain further understanding of the luminescence properties of multiquantum wells and the factors affecting them on a microscopic level,cathodoluminescence combined with scanning transmission electron microscopy and spectroscopy was used to measure the luminescence of In_(0.15)Ga_(0.85)N five-period multiquantum wells.The lattice-composition-energy relationship was established with the help of energy-dispersive x-ray spectroscopy,and the bandgaps of In_(0.15)Ga_(0.85)N and GaN in multiple quantum wells were extracted by electron energy loss spectroscopy to understand the features of cathodoluminescence spectra.The luminescence differences between different periods of multiquantum wells and the effects of defects such as composition fluctuation and dislocations on the luminescence of multiple quantum wells were revealed.Our study establishing the direct relationship between the atomic structure of In_(x)Ga_(1-x)N multiquantum wells and photoelectric properties provides useful information for nitride applications.
Zircon is a widely-used heavy mineral in geochronological and geochemical research because it can extract important information to understand the history and genesis of rocks. Zircon has various types,and an accurate examination of zircon type is a prerequisite procedure before further analysis.Cathodoluminescence(CL) imaging is one of the most reliable ways to classify zircons. However, current CL image examination is conducted by manual work, which is time-consuming, bias-prone, and requires expertise. An automated and bias-free method for zircon classification is absent but necessary. To this end, deep convolutional neural networks(DCNNs) and transfer learning are applied in this study to classify the common types of zircons, i.e., igneous, metamorphic, and hydrothermal zircons. An atlas with over 4000 CL images of these three types of zircons is created, and three DCNNs are trained using these images. The results of this study indicate that the DCNNs can distinguish hydrothermal zircons from other zircons, as indicated by the highest accuracy of 100%. Although similar textures in igneous and metamorphic zircons pose great challenges for zircon classification, the DCNNs successfully classify 95% igneous and 92% metamorphic zircons. This study demonstrates the high accuracy of DCNNs in zircon classification and presents the great potentiality of deep learning techniques in numerous geoscientific disciplines.
Photonic topological insulators with robust boundary states can enable great applications for optical communication and quantum emission,such as unidirectional waveguide and single-mode laser.However,because of the diffraction limit of light,the physical insight of topological resonance remains unexplored in detail,like the dark line that exists with the crys-talline symmetry-protected topological edge state.Here,we experimentally observe the dark line of the Z_(2)photonic topo-logical insulator in the visible range by photoluminescence and specify its location by cathodoluminescence characteriza-tion,and elucidate its mechanism with the p-d orbital electromagnetic field distribution which calculated by numerical sim-ulation.Our investigation provides a deeper understanding of Z_(2)topological edge states and may have great signific-ance to the design of future on-chip topological devices.