No abstract

By performing density-functional calculations, we have investigated the electronic bandgap of single epitaxial and multiepitaxial graphene layers on SiC. The calculations show that a defect-free graphene layer above the carbon buffer layer is very flat and no bandgap is found in the Dirac bands. By introducing a finite density of Stone-Wales defects in the graphene layer(s), we find that a bandgap is opened and decreases as the thickness of graphene layers increases, in good agreement with experiments. The band splitting and the charge distribution vary greatly with the number of graphene layers. The bandgap opening is due to the symmetry breaking within the single graphene layer. The narrowing of the bandgap in multiple graphene layers is induced by interlayer interaction.

We predict a new two-dimensional allotrope of phosphorus, which we call red phosphorene, by restructuring the segments of the previously proposed blue and black phosphorenes. Its atomic and electronic structures as well as the thermodynamic and dynamic stabilities are systematically studied by first-principles calculations. The results indicate that the red phosphorene is dynamically stable and possesses remarkably thermodynamical stability comparable to that of the black one. Because of the sp(3)-hybridization and the formation of a localized lone pair, red phosphorene is a semiconductor with an indirect band gap of about 1.96 eV, which can be effectively modulated by in-plane strains due to its wave-like configuration. We find that the red, black and blue phosphorenes show evident distinction in their layer thicknesses, surface work functions, and possible colors, based on which one can distinguish them in future experiments.

Two new carbon allotropes (H-carbon and S-carbon) are proposed, as possible candidates for the intermediate superhard phases between graphite and diamond obtained in the process of cold compressing graphite, based on the results of first-principles calculations. Both H-carbon and S-carbon are more stable than previously proposed M-carbon and W-carbon and their bulk modulus are comparable to that of diamond. H-carbon is an indirect-band-gap semiconductor with a gap of 4.459 eV and S-carbon is a direct-band-gap semiconductor with a gap of 4.343 eV. The transition pressure from cold compressing graphite is 10.08 GPa and 5.93 Gpa for H-carbon and S-carbon, respectively, which is in consistent with the recent experimental report.

Transmission line corridor (i.e., Right-of-Ways (ROW)) clearance management plays a critically important role in power line risk management and is an important task of the routine power line inspection of the grid company. The clearance anomaly detection measures the distance between the power lines and the surrounding non-power-facility objects in the corridor such as trees, and buildings, to judge whether the clearance is within the safe range. To find the clearance hazards efficiently and flexibly, this study thus proposed an automatic clearance anomaly detection method utilizing LiDAR point clouds collected by unmanned aerial vehicle (UAV). Firstly, the terrain points were filtered out using two-step adaptive terrain filter and the pylons were detected in the non-terrain points following a feature map method. After dividing the ROW point clouds into spans based on the pylon detection results, the power line point clouds were extracted according to their geometric distribution in local span point clouds slices, and were further segmented into clusters by applying conditional Euclidean clustering with linear feature constraints. Secondly, the power line point clouds segments were iteratively fitted with 3D catenary curve model that is represented by a horizontal line and a vertical catenary curve defined by a hyperbolic cosine function, resulting in a continuous mathematical model of the discretely sampled points of the power line. Finally, a piecewise clearance calculation method which converts the point-to-catenary curve distance measurements to minimal distance calculation based on differential geometry was used to calculate the distance between the power line and the non-power-facility objects in the ROW. The clearance measurements were compared with the standard safe threshold to find the clearance anomalies in the ROWs. Multiple LiDAR point clouds datasets collected by a large-scale UAV power line inspection system were used to validate the effectiveness and accuracy of the proposed method. The detected results were validated through qualitatively visual inspection, quantitatively manual measurements in raw point clouds and on-site field survey. The experiments show that the automatic clearance anomaly detection method proposed in this paper effectively detects the clearance hazards such as tree encroachment, and the clearance measurement accuracy is decimeter level for the LiDAR point clouds collected by our UAV inspection system.