Controlling doping in graphene through a SiC substrate: A first-principles study

Controlling the type and density of charge carriers by doping is the key step for developing graphene electronics. However, direct doping of graphene is rather a challenge. Based on first-principles calculations, a concept of overcoming doping difficulty in graphene via substrate is reported.We find that doping could be strongly enhanced in epitaxial graphene grown on silicon carbide substrate. Compared to free-standing graphene, the formation energies of the dopants can decrease by as much as 8 eV. The type and density of the charge carriers of epitaxial graphene layer can be effectively manipulated by suitable dopants and surface passivation. More importantly, contrasting to the direct doping of graphene, the charge carriers in epitaxial graphene layer are weakly scattered by dopants due to the spatial separation between dopants and the conducting channel. Finally, we show that a similar idea can also be used to control magnetic properties, for example, induce a half-metallic state in the epitaxial graphene without magnetic impurity doping

Unconventional Superconducting Symmetry in a Checkerboard Antiferromagnet

We use a renormalized mean field theory to study the Gutzwiller projected BCS states of the extended Hubbard model in the large $U$ limit, or the $t$-$t'$-$J$-$J'$ model on a two-dimensional checkerboard lattice. At small $t'/t$, the frustration due to the diagonal terms of $t'$ and $J'$ does not alter the $d_{x^2-y^2}$-wave pairing symmetry, and the negative (positive) $t'/t$ enhances (suppresses) the pairing order parameter. At large $t'/t$, the ground state has an extended s-wave symmetry. At the intermediate $t'/t$, the ground state is $d+id$ or $d+is$-wave with time reversal symmetry broken.Comment: 6 pages, 6 figure

Charge Ordered RVB States in the Doped Cuprates

We study charge ordered d-wave resonating valence bond states (dRVB) in the doped cuprates, and estimate the energies of these states in a generalized $t-J$ model by using a renormalized mean field theory. The long range Coulomb potential tends to modulate the charge density in favor of the charge ordered RVB state. The possible relevance to the recently observed $4 \times 4$ checkerboard patterns in tunnelling conductance in high $T_c$ cuprates is discussed.Comment: 4 pages, 4 figures, 3 table

Ordered Semiconducting Nitrogen-Graphene Alloys

The interaction between substitutional nitrogen atoms in graphene is studied by performing first principles calculations. The nearest neighbor interaction between nitrogen dopants is highly repulsive because of the strong electrostatic repulsion between nitrogen atoms, which prevents the full phase separation in nitrogen doped graphene. Interestingly, there are two relatively stable nitrogen-nitrogen pairs due to the anisotropy charge redistribution induced by nitrogen doping. We reveal two stable semiconducting ordered N doped graphene structures C3N and C12N through the cluster expansion technique and particle swarm optimization method. In particular, C12N has a direct band gap of 0.98 eV. The heterojunctions between C12N and graphene nanoribbons might be promising organic solar cells

Feature Shrinkage Pyramid for Camouflaged Object Detection with Transformers

Vision transformers have recently shown strong global context modeling capabilities in camouflaged object detection. However, they suffer from two major limitations: less effective locality modeling and insufficient feature aggregation in decoders, which are not conducive to camouflaged object detection that explores subtle cues from indistinguishable backgrounds. To address these issues, in this paper, we propose a novel transformer-based Feature Shrinkage Pyramid Network (FSPNet), which aims to hierarchically decode locality-enhanced neighboring transformer features through progressive shrinking for camouflaged object detection. Specifically, we propose a nonlocal token enhancement module (NL-TEM) that employs the non-local mechanism to interact neighboring tokens and explore graph-based high-order relations within tokens to enhance local representations of transformers. Moreover, we design a feature shrinkage decoder (FSD) with adjacent interaction modules (AIM), which progressively aggregates adjacent transformer features through a layer-bylayer shrinkage pyramid to accumulate imperceptible but effective cues as much as possible for object information decoding. Extensive quantitative and qualitative experiments demonstrate that the proposed model significantly outperforms the existing 24 competitors on three challenging COD benchmark datasets under six widely-used evaluation metrics. Our code is publicly available at https://github.com/ZhouHuang23/FSPNet.Comment: CVPR 2023. Project webpage at: https://tzxiang.github.io/project/COD-FSPNet/index.htm