相关论文: Neural network-based recognition of multiple nanob…
Plasmon resonance in nanopatterned single layer graphene nanoribbon (SL-GNR), double layer graphene nanoribbon (DL-GNR) and triple layer graphene nanoribbon (TL-GNR) structures is studied both experimentally and by numerical simulations. We…
Topological line defects in graphene represent an ideal way to produce highly controlled structures with reduced dimensionality that can be used in electronic devices. In this work we propose using extended line defects in graphene to…
Metal atoms adsorbed on few layer graphenes condense to form nanometer-size droplets whose growth is size limited by a competition between the surface tension and repulsive electrostatic interactions from charge transfer between the metal…
We have measured the mechanical properties of few-layer graphene and graphite flakes that are suspended over circular holes. The spatial profile of the flake's spring constant is measured with an atomic force microscope. The bending…
The method of using neural networks (NNs) for turbulent transport prediction in a simplified model of tokamak plasmas is explored. The NNs are trained on a database obtained via test-particle simulations of a transport model in the…
This review covers recent experimental progress in probing the electronic properties of graphene and how they are influenced by various substrates, by the presence of a magnetic field and by the proximity to a superconductor. The focus is…
Graphene is the first truly two-dimensional (2D) material, possessing a cone-like energy spectrum near the Fermi energy and treated as a gapless semiconductor. Its unique properties trigger researchers to find more applications of it, such…
Graphene, a two-dimensional (2D) material with unique electronic properties, appears to be an ideal object for the application of surface-science methods. Among them, a family of scanning probe microscopy methods (STM, AFM, KPFM) and the…
Graphene nanoribbons present diverse electronic properties ranging from semiconducting to half-metallic, depending on their geometry, dimensions and chemical composition. Here we present a route to control these properties via externally…
Accurate prediction of physical properties is critical for discovering and designing novel materials. Machine learning technologies have attracted significant attention in the materials science community for their potential for large-scale…
Graphene-based laminates, with ultralong and tortuous nanocapillaries formed by simply stacking graphene flakes together, have great promises in filtration and separation. However, the information on liquid water trans-membrane permeation…
We probe the local inhomogeneities in the electronic properties of exfoliated graphene due to the presence of charged impurities in the SiO2 substrate using a combined scanning tunneling and electrostatic force microscope. Contact potential…
Nanoporous membranes based on two dimensional materials are predicted to provide highly selective gas transport in combination with extreme permeability. Here we investigate membranes made from multilayer graphdiyne, a graphene-like crystal…
The chapter generalizes results on influence of uniaxial strain and adsorption on the electron states and charge transport or localization in graphene with different configurations of imperfections (point defects): resonant (neutral)…
Position measurements at the quantum level are vital for many applications, but also challenging. Typically, methods based on optical phase shifts are used, but these methods are often weak and difficult to apply to many materials. An…
Graphene is being increasingly used as an interesting transducer membrane in micro- and nanoelectromechanical systems (MEMS and NEMS, respectively) due to its atomical thickness, extremely high carrier mobility, high mechanical strength and…
We present electrical transport measurements of a van-der-Waals heterostructure consisting of a graphene nanoribbon separated by a thin boron nitride layer from a micron-sized graphene sheet. The interplay between the two layers is…
We demonstrate the local oxidation nanopatterning of graphene films by an atomic force microscope. The technique provides a method to form insulating trenches in graphene flakes and to fabricate nanodevices with sub-nm precision. We…
Convolutional neural networks (CNNs) are usually used as a backbone to design methods in biomedical image segmentation. However, the limitation of receptive field and large number of parameters limit the performance of these methods. In…
We study by density functional and large scale tight-binding transport calculations the electronic structure, magnetism and transport properties of the recently proposed graphene ribbons with edges rolled to form nanotubes. Edges with…