相关论文: Peripheral neuron survival and outgrowth on graphe…
We examine the 1/N expansion, where N is the number of two-component Dirac fermions, for Coulomb interactions in graphene with a gap of magnitude $\Delta = 2 m$. We find that for $N\alpha\gg1$, where $\alpha$ is graphene's "fine structure…
The use of graphene in surface plasmon resonance sensors, covering a metallic (plasmonic) film, has a number of demonstrated advantages, such protecting the film against corrosion/oxidation and facilitating the introduction of functional…
The successful fabrication of single layer graphene has greatly stimulated the progress of the research on graphene. In this article, focusing on the basic electronic and transport properties of graphene nanoribbons (GNRs), we review the…
Technologically useful and robust graphene-based interfaces for devices require the introduction of highly selective, stable, and covalently bonded functionalities on the graphene surface, whilst essentially retaining the electronic…
Surface-assisted polymerization of molecular monomers into extended chains can be used as the seed of graphene nanoribbon (GNR) formation, resulting from a subsequent cyclo-dehydrogenation process. By means of valence-band photoemission and…
Graph are a ubiquitous data representation, as they represent a flexible and compact representation. For instance, the 3D structure of RNA can be efficiently represented as $\textit{2.5D graphs}$, graphs whose nodes are nucleotides and…
Although the corresponding carbon-metal interactions can be very different, a similar nonlinear growth behavior of graphene has been observed for different metal substrates. To understand this interesting experimental observation, a…
Graphene is a unique material to study fundamental limits of plasmonics. Apart from the ultimate single-layer thickness, its carrier concentration can be tuned by chemical doping or applying an electric field. In this manner the…
We report on the efficient photodetection (PD) properties of graphene based p-i-n photodetector, where all the three layers are either single or multilayer graphene sheets. We report the bandwidth and responsivity performance of the device.…
We report macroscopic sheets of highly conductive bilayer graphene with exceptionally high hole concentrations of ~ $10^{15}$ $cm^{-2}$ and unprecedented sheet resistances of 20-25 {\Omega} per square over macroscopic scales, and obtained…
Neural networks (NN)-based learning algorithms are strongly affected by the choices of initialization and data distribution. Different optimization strategies have been proposed for improving the learning trajectory and finding a better…
Recent intensive research on two-dimensional materials (2DMs) rekindle the interest in the intercalation of various atoms and molecules into layered compounds as a tool to manufacture 2DMs and tune their optoelectronic, magnetic and…
Tuning the macroscopic dielectric response on demand holds potential for actively tunable metaphotonics and optical devices. In recent years, graphene has been extensively investigated as a tunable element in nanophotonics. Significant…
Graphene is a promising candidate to succeed silicon based devices and doping holds the key to graphene electronics. Conventional doping methods through surface functionalization or lattice modification are effective in tuning carrier…
Data-driven neural networks are increasingly used as surrogate forward models in geophysics, but it remains unclear whether they recover only the data mapping or also the underlying physical sensitivity structure. Here we test this question…
A central question in the field of graphene-related research is how graphene behaves when it is patterned at the nanometer scale with different edge geometries. Perhaps the most fundamental shape relevant to this question is the graphene…
This paper builds on the connection between graph neural networks and traditional dynamical systems. We propose continuous graph neural networks (CGNN), which generalise existing graph neural networks with discrete dynamics in that they can…
The presence of wrinkles in Graphene Nanoribbons (GNR) and other two-dimensional (2D) materials significantly alter their mechanical, electronic, optical properties, which can be either beneficial or detrimental. Experimentally, it has been…
Graph neural networks (GNNs) are a class of deep models that operate on data with arbitrary topology represented as graphs. We introduce an efficient memory layer for GNNs that can jointly learn node representations and coarsen the graph.…
For carbon-based materials, in contrast to metal surfaces, the relationship between strain and reactivity is not yet established, even though there are literature reports on strained graphene. Knowledge of such relationships would be…