Layered double hydroxides (LDHs) have a special structure and atom composition, which are expected to be an excellent electromagnetic wave (EMW) absorber. However, it is still a problem that obtaining excellent EMW-absorbing materials from LDHs. Herein, we designed heterostructure NiCo-LDHs@ZnO nanorod and then subsequent heat treating to derive NiCo@C/ZnO composites. Finally, with the synergy of excellent dielectric loss and magnetic loss, an outstanding absorption performance could be achieved with the reflection loss of − 60.97 dB at the matching thickness of 2.3 mm, and the widest absorption bandwidth of 6.08 GHz was realized at 2.0 mm. Moreover, this research work provides a reference for the development and utilization of LDHs materials in the field of microwave absorption materials and can also provide ideas for the design of layered structural absorbers.

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With the significant progress of urbanization, cities and towns are suffering from air pollution, heat island effects, and other environmental problems. Urban vegetation, especially trees, plays a significant role in solving these ecological problems. To maximize services provided by vegetation, urban tree species should be properly selected and optimally arranged. Therefore, accurate classification of tree species in urban environments has become a major issue. In this paper, we reviewed the potential of light detection and ranging (LiDAR) data to improve the accuracy of urban tree species classification. In detail, we reviewed the studies using LiDAR data in urban tree species mapping, especially studies where LiDAR data was fused with optical imagery, through classification accuracy comparison, general workflow extraction, and discussion and summarizing of the specific contribution of LiDAR. It is concluded that combining LiDAR data in urban tree species identification could achieve better classification accuracy than using either dataset individually, and that such improvements are mainly due to finer segmentation, shadowing effect reduction, and refinement of classification rules based on LiDAR. Furthermore, some suggestions are given to improve the classification accuracy on a finer and larger species level, while also aiming to maintain classification costs.

AlN thick films were grown on c-plane sapphire substrates by hydride vapor phase epitaxy at high temperature. The evolution of the strain state and crystal quality of AlN with increase of thickness were investigated by transmission electron microscopy, field-emission scanning electron microscopy, Raman spectra and atomic force microscopy (AFM). As the thickness increased, the stress in the epilayers decreased gradually, which was attributed to the reaction of dislocations at the first several microns in thickness. When the thickness was more than 20 μm, the stress was almost fully relaxed due to the formation of cracks. Wet etching experiments indicated that the dislocation density decreased with the increase of thickness. The AFM images showed that the density of dark spots on the surface obviously decreased and the atomic steps became straight as the thickness increased.