相关论文: Neural network-based recognition of multiple nanob…
We report on (magneto)-transport experiments in chemically derived narrow graphene nanoribbons under high magnetic fields (up to 60 Tesla). Evidences of field-dependent electronic confinement features are given, and allow estimating the…
Modern scanning probe techniques, like scanning tunneling microscopy (STM), provide access to a large amount of data encoding the underlying physics of quantum matter. In this work, we analyze how convolutional neural networks (CNN) can be…
Graphene, the atomically-thin honeycomb carbon lattice, is a highly conducting 2D material whose exposed electronic structure offers an ideal platform for sensing. Its biocompatible, flexible, and chemically inert nature associated to the…
Graphene covered metal nanoparticles constitute a novel type of hybrid materials, which provide a unique platform to study plasmonic effects, surface-enhanced Raman scattering (SERS), and metal-graphene interactions at the nanoscale. Such a…
Recording atomic-resolution transmission electron microscopy (TEM) images is becoming increasingly routine. A new bottleneck is then analyzing this information, which often involves time-consuming manual structural identification. We have…
Structural distortions in nano-materials can induce dramatic changes in their electronic properties. This situation is well manifested in graphene, a two-dimensional honeycomb structure of carbon atoms with only one atomic layer thickness.…
In this work, we use artificial neural networks (ANNs) to recognize the material composition, sizes of nanoparticles and their concentrations in different media with high accuracy, solely from the absorbance spectrum of a macroscopic…
Graphene, the first true two-dimensional material still reveals the most remarkable transport properties among the growing class of two-dimensional materials. Although many studies have investigated fundamental scattering processes, the…
Graphene nanoribbons (GNRs) are one-dimensional nanostructures predicted to display a rich variety of electronic behaviors. Depending on their structure, GNRs realize metallic and semiconducting electronic structures with band gaps that can…
Graph Nerual Networks (GNNs) are effective models in graph embedding. It extracts shallow features and neighborhood information by aggregating neighbor information to learn the embedding representation of different nodes. However, the local…
We show how hydrogenation of graphene nanoribbons at small concentrations can open new venues towards carbon-based spintronics applications regardless of any especific edge termination or passivation of the nanoribbons. Density functional…
By applying tight binding model, we investigate the electronic and transport properties of randomly distributed Stone-Wales (SW) defects on an armchair graphene nanoribbon (AGNR). We use four different functions, as distribution functions,…
We report a linear-scaling numerical method for exploring nonequilibrium electron dynamics in systems of arbitrary complexity. Based on the Chebyshev expansion of the time evolution of the single-particle density matrix, the method gives…
Atomically precise graphene nanoribbons are a promising emerging class of designer quantum materials with electronic properties that are tunable by chemical design. However, many challenges remain in the device integration of these…
Plasmons, which are collective charge oscillations, offer the potential to use optical signals in nano-scale electric circuits. Recently, plasmonics using graphene have attracted interest, particularly because of the tunable plasmon…
More than a decade after the discovery of graphene, ballistic transport in nanostructures based on this intriguing material still represents a challenging field of research in two-dimensional electronics. The presence of rough edges in…
Graphene has shown great potentials for high-speed photodetection. Yet, the responsivities of graphene-based high-speed photodetectors are commonly limited by the weak effective absorption of atomically thin graphene. Here, we propose and…
Graphene has many unique properties that make it an ideal material for fundamental studies as well as for potential applications. Here we review the recent results on the Raman spectroscopy and imaging of graphene. Raman spectroscopy and…
Nanoelectromechanical systems (NEMSs) are emerging nanoscale elements at the crossroads between mechanics, optics and electronics, with significant potential for actuation and sensing applications. The reduction of dimensions compared to…
Quantum confinement endows two-dimensional (2D) layered materials with exceptional physics and novel properties compared to their bulk counterparts. Although certain two- and few-layer configurations of graphene have been realized and…