Floquet engineering of Dirac cones on the surface of a topological insulator

We propose to Floquet-engineer Dirac cones at the surface of a three-dimensional topological insulator. We show that a large tunability of the Fermi velocity can be achieved as a function of the polarization, direction and amplitude of the driving field. Using this external control, the Dirac cones in the quasienergy spectrum may become elliptic or massive, in accordance to experimental evidences. These results help us to understand the interplay of surface states and external ac driving fields in topological insulators. In our work we use the full Hamiltonian for the three-dimensional system instead of effective surface Hamiltonians, which are usually considered in the literature. Our findings show that the Dirac cones in the quasienergy spectrum remain robust even in the presence of bulk states and, therefore, they validate the usage of effective surface Hamiltonians to explore the properties of Floquet-driven topological boundaries. Furthermore, our model allows us to introduce new out-of-plane field configurations, which cannot be accounted for by effective surface Hamiltonians

A single-crystal neutron diffraction study of wardite, NaAl3(PO4)2(OH)4·2H2O

The crystal structure and crystal chemistry of wardite, ideally NaAl3(PO4)2(OH)4\ub72H2O, was investigated by single-crystal neutron diffraction (data collected at 20 K) and electron microprobe analysis in wavelength-dispersive mode. The empirical formula of the sample used in this study is: (Na0.91Ca0.01)\u3a3 = 0.92(Al2.97Fe3+0.05Ti0.01)\u3a3 = 3.03(P2.10O8)(OH)4\ub71.74H2O. The neutron diffraction data confirm that the crystal structure of wardite can be described with a tetragonal symmetry (space group P41212, a = b = 7.0577(5) and c = 19.0559(5) \uc5 at 20 K) and consists of sheets made of edge-sharing Na-polyhedra and Al-octahedra along with vertex-sharing Al-octahedra, parallel to (001), connected by P-tetrahedra and H bonds to form a (001) layer-type structure, which well explains the pronounced {001} cleavage of the wardite crystals. The present data show that four crystallographically independent H sites occur in the structure of wardite, two belonging to a H2O molecule (i.e., H1\u2013O6\u2013H2) and two forming hydroxyl groups (i.e., O5\u2013H3 and O7\u2013H4). The location of the hydrogen atoms allows us to define the extensive network of H bonds: the H atoms belonging to the H2O molecule form strong H bonds, whereas both the H atoms belonging to the two independent hydroxyl groups form weak interactions with bifurcated bonding schemes. As shown by the root-mean-square components of the displacement ellipsoids, oxygen and hydrogen atoms have slightly larger anisotropic displacement parameters compared to the other sites (populated by P, Al and Na). The maximum ratio of the max and min root-mean-square components of the displacement ellipsoids is observed for the protons of the hydroxyl groups, which experience bifurcated H-bonding schemes. A comparative analysis of the crystal structure of wardite and fluorowardite is also provided

A DEEP LEARNING APPROACH FOR THE RECOGNITION OF URBAN GROUND PAVEMENTS IN HISTORICAL SITES

Urban management is a topic of great interest for local administrators, particularly because it is strongly connected to smart city issues and can have a great impact on making cities more sustainable. In particular, thinking about the management of the physical accessibility of cities, the possibility of automating data collection in urban areas is of great interest. Focusing then on historical centres and urban areas of cities and historical sites, it can be noted that their ground surfaces are generally characterised by the use of a multitude of different pavements. To strengthen the management of such urban areas, a comprehensive mapping of the different pavements can be very useful. In this paper, the survey of a historical city (Sabbioneta, in northern Italy) carried out with a Mobile Mapping System (MMS) was used as a starting point. The approach here presented exploit Deep Learning (DL) to classify the different pavings. Firstly, the points belonging to the ground surfaces of the point cloud were selected and the point cloud was rasterised. Then the raster images were used to perform a material classification using the Deep Learning approach, implementing U-Net coupled with ResNet 18. Five different classes of materials were identified, namely sampietrini, bricks, cobblestone, stone, asphalt. The average accuracy of the result is 94%