Two-dimensional (2D) Dirac materials have attracted intense research efforts due to their promise for applications ranging from field-effect transistors and low-power electronics to fault-tolerant quantum computation. One key challenge is to fabricate 2D Dirac materials hosting Dirac electrons. Here, monolayer germanene is successfully fabricated on a Ag2Ge surface alloy. Scanning tunneling spectroscopy measurements revealed a linear energy dispersion relation. The latter was supported by density functional theory calculations. These results demonstrate that monolayer germanene can be realistically fabricated on a Ag2Ge surface alloy. The finding opens the door to exploration and study of 2D Dirac material physics and device applications.

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In ice and snow weather, the surface texture characteristics of asphalt pavement change, which will significantly affect the skid resistance performance of asphalt pavement. In this study, five asphalt mixture types of AC-5, AC-13, AC-16, SMA-13, SMA-16 were prepared under three conditions of the original state, ice and snow. In this paper, a 2D-wavelet transform approach is proposed to characterize the micro and macro texture of pavement. The Normalized Energy (NE) is proposed to describe the pavement texture quantitatively. Compared with the mean texture depth (MTD), NE has the advantages of full coverage, full automation and wide analytical scale. The results show that snow increases the micro-scale texture because of its fluffiness, while the formation of the ice sheets on the surface reduces the micro-scale texture. The filling effect of snow and ice reduces the macro-scale texture of the pavement surface. In a follow-up study, the 2D-wavelet transform approach can be applied to improve the intelligent driving braking system, which can provide pavement texture information for the safe braking strategy of driverless vehicles.

In ice and snow weather, the surface texture characteristics of asphalt pavement change, which will significantly affect the skid resistance performance of asphalt pavement. In this study, five asphalt mixture types of AC-5, AC-13, AC-16, SMA-13, SMA-16 were prepared under three conditions of the original state, ice and snow. In this paper, a 2D-wavelet transform approach is proposed to characterize the micro and macro texture of pavement. The Normalized Energy (NE) is proposed to describe the pavement texture quantitatively. Compared with the mean texture depth (MTD), NE has the advantages of full coverage, full automation and wide analytical scale. The results show that snow increases the micro-scale texture because of its fluffiness, while the formation of the ice sheets on the surface reduces the micro-scale texture. The filling effect of snow and ice reduces the macro-scale texture of the pavement surface. In a follow-up study, the 2D-wavelet transform approach can be applied to improve the intelligent driving braking system, which can provide pavement texture information for the safe braking strategy of driverless vehicles.