Impact of local stacking on the graphene-impurity interaction: theory and experiments

We investigate the graphene-impurity interaction problem by combining experimental - scanning tunneling microscopy (STM) and spectroscopy (STS) - and theoretical - Anderson impurity model and density functional theory (DFT) calculations - techniques. We use graphene on the SiC(000-1)(2x2)_C reconstruction as a model system. The SiC substrate reconstruction is based on silicon adatoms. Graphene mainly interacts with the dangling bonds of these adatoms which act as impurities. Graphene grown on SiC(000-1)(2x2)_C shows domains with various orientations relative to the substrate so that very different local graphene/Si adatom stacking configurations can be probed on a given grain. The position and width of the adatom (impurity) state can be analyzed by STM/STS and related to its local environment owing to the high bias electronic transparency of graphene. The experimental results are compared to Anderson's model predictions and complemented by DFT calculations for some specific local environments. We conclude that the adatom resonance shows a smaller width and a larger shift toward the Dirac point for an adatom at the center of a graphene hexagon than for an adatom just on top of a C graphene atom.Comment: 13 pages, 6 figures, Accepted for publication in Phys. Rev.

Quasiparticle Chirality in Epitaxial Graphene Probed at the Nanometer Scale

Graphene exhibits unconventional two-dimensional electronic properties resulting from the symmetry of its quasiparticles, which leads to the concepts of pseudospin and electronic chirality. Here we report that scanning tunneling microscopy can be used to probe these unique symmetry properties at the nanometer scale. They are reflected in the quantum interference pattern resulting from elastic scattering off impurities, and they can be directly read from its fast Fourier transform. Our data, complemented by theoretical calculations, demonstrate that the pseudospin and the electronic chirality in epitaxial graphene on SiC(0001) correspond to the ones predicted for ideal graphene.Comment: 4 pages, 3 figures, minor change

Graphene on the C-terminated SiC (000 1ˉ\bar{1}) surface: An ab initio study

The atomic and electronic structures of a graphene layer on top of the $(2\times2)$ reconstruction of the SiC (000$\bar{1}$) surface are studied from ab initio calculations. At variance with the (0001) face, no C bufferlayer is found here. Si adatoms passivate the substrate surface so that the very first C layer presents a linear dispersion characteristic of graphene. A small graphene-substrate interaction remains in agreement with scanning tunneling experiments (F.Hiebel et al. {\it Phys. Rev. B} {\bf 78} 153412 (2008)). The stacking geometry has little influence on the interaction which explains the rotational disorder observed on this face.Comment: 4 pages, 3 figures, additional materia

Quasiparticle scattering off phase boundaries in epitaxial graphene

We investigate the electronic structure of terraces of single layer graphene (SLG) by scanning tunneling microscopy (STM) on samples grown by thermal decomposition of 6H-SiC(0001) crystals in ultra-high vacuum. We focus on the perturbations of the local density of states (LDOS) in the vicinity of edges of SLG terraces. Armchair edges are found to favour intervalley quasiparticle scattering, leading to the (\surd3\times\surd3)R30{\deg} LDOS superstructure already reported for graphite edges and more recently for SLG on SiC(0001). Using Fourier transform of LDOS images, we demonstrate that the intrinsic doping of SLG is responsible for a LDOS pattern at the Fermi energy which is more complex than for neutral graphene or graphite, since it combines local (\surd3\times\surd3)R30{\deg} superstructure and long range beating modulation. Although these features were already reported by Yang et al. Nanoletters 10, 943 (2010), we propose here an alternative interpretation based on simple arguments classically used to describe standing wave patterns in standard two-dimensional systems. Finally, we discuss the absence of intervalley scattering off other typical boundaries: zig-zag edges and SLG/bilayer graphene junctions

Electron states of mono- and bilayer graphene on SiC probed by STM

We present a scanning tunneling microscopy (STM) study of a gently-graphitized 6H-SiC(0001) surface in ultra high vacuum. From an analysis of atomic scale images, we identify two different kinds of terraces, which we unambiguously attribute to mono- and bilayer graphene capping a C-rich interface. At low temperature, both terraces show $(\sqrt{3}\times \sqrt{3})$ quantum interferences generated by static impurities. Such interferences are a fingerprint of $\pi$-like states close to the Fermi level. We conclude that the metallic states of the first graphene layer are almost unperturbed by the underlying interface, in agreement with recent photoemission experiments (A. Bostwick et al., Nature Physics 3, 36 (2007))Comment: 4 pages, 3 figures submitte

Role of pseudospin in quasiparticle interferences in epitaxial graphene probed by high-resolution scanning tunneling microscopy

Pseudospin, an additional degree of freedom related to the honeycomb structure of graphene, is responsible of many of the outstanding electronic properties found in this material. This article provides a clear understanding of how such pseudospin impacts the quasiparticle interferences of monolayer (ML) and bilayer (BL) graphene measured by low temperature scanning tunneling microscopy and spectroscopy. We have used this technique to map, with very high energy and space resolution, the spatial modulations of the local density of states of ML and BL graphene epitaxialy grown on SiC(0001), in presence of native disorder. We perform a Fourier transform analysis of such modulations including wavevectors up to unit-vectors of the reciprocal lattice. Our data demonstrate that the quasiparticle interferences associated to some particular scattering processes are suppressed in ML graphene, but not in BL graphene. Most importantly, interferences with 2qF wavevector associated to intravalley backscattering are not measured in ML graphene, even on the images with highest resolution. In order to clarify the role of the pseudospin on the quasiparticle interferences, we use a simple model which nicely captures the main features observed on our data. The model unambiguously shows that graphene's pseudospin is responsible for such suppression of quasiparticle interferences features in ML graphene, in particular for those with 2qF wavevector. It also confirms scanning tunneling microscopy as a unique technique to probe the pseudospin in graphene samples in real space with nanometer precision. Finally, we show that such observations are robust with energy and obtain with great accuracy the dispersion of the \pi-bands for both ML and BL graphene in the vicinity of the Fermi level, extracting their main tight binding parameters

Major patterns in the introgression history of Heliconius butterflies

Gene flow between species, although usually deleterious, is an important evolutionary process that can facilitate adaptation and lead to species diversification. It also makes estimation of species relationships difficult. Here, we use the full-likelihood multispecies coalescent (MSC) approach to estimate species phylogeny and major introgression events in Heliconius butterflies from whole-genome sequence data. We obtain a robust estimate of species branching order among major clades in the genus, including the 'melpomene-silvaniform' group, which shows extensive historical and ongoing gene flow. We obtain chromosome-level estimates of key parameters in the species phylogeny, including species divergence times, present-day and ancestral population sizes, as well as the direction, timing, and intensity of gene flow. Our analysis leads to a phylogeny with introgression events that differ from those obtained in previous studies. We find that Heliconius aoede most likely represents the earliest-branching lineage of the genus and that 'silvaniform' species are paraphyletic within the melpomene-silvaniform group. Our phylogeny provides new, parsimonious histories for the origins of key traits in Heliconius, including pollen feeding and an inversion involved in wing pattern mimicry. Our results demonstrate the power and feasibility of the full-likelihood MSC approach for estimating species phylogeny and key population parameters despite extensive gene flow. The methods used here should be useful for analysis of other difficult species groups with high rates of introgression

Single 3dd transition metal atoms on multi-layer graphene systems: electronic configurations, bonding mechanisms and role of the substrate

The electronic configurations of Fe, Co, Ni, and Cu adatoms on graphene and graphite have been studied by x-ray magnetic circular dichroism and charge transfer multiplet theory. A delicate interplay between long-range interactions and local chemical bonding is found to influence the adatom equilibrium distance and magnetic moment. The results for Fe and Co are consistent with purely physisorbed species having, however, different 3$d$-shell occupancies on graphene and graphite ($d^{n+1}$ and $d^n$, respectively). On the other hand, for the late 3$d$ metals Ni and Cu a trend towards chemisorption is found, which strongly quenches the magnetic moment on both substrates.Comment: 7 pages, 4 figure

Multi-scale building maps from aerial imagery

Nowadays, the extraction of buildings from aerial imagery is mainly done through deep convolutional neural networks (DCNNs). Buildings are predicted as binary pixel masks and then regularized to polygons. Restricted by nearby occlusions (such as trees), building eaves, and sometimes imperfect imagery data, these results can hardly be used to generate detailed building footprints comparable to authoritative data. Therefore, most products can only be used for mapping at smaller map scale. The level of detail that should be retained is normally determined by the scale parameter in the regularization algorithm. However, this scale information has been already defined in cartography. From existing maps of different scales, neural network can be used to learn such scale information implicitly. The network can perform generalization directly on the mask output and generate multi-scale building maps at once. In this work, a pipeline method is proposed, which can generate multi-scale building maps from aerial imagery directly. We used a land cover classification model to provide the building blobs. With the models pre-trained for cartographic building generalization, blobs were generalized to three target map scales, 1:10,000, 1:15,000, and 1:25,000. After post-processing with vectorization and regularization, multi-scale building maps were generated and then compared with existing authoritative building data qualitatively and quantitatively. In addition, change detection was performed and suggestions for unmapped buildings could be provided at a desired map scale. . © 2020 International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives