Related papers: Graphite in Science and Nuclear Technology
Machine learning methods have a long history of applications in high energy physics (HEP). Recently, there is a growing interest in exploiting these methods to reconstruct particle signatures from raw detector data. In order to benefit from…
Monoatomic layers of graphite can be electrically contacted and used as building blocks for new promising devices. These experiment are today possible thanks to the fact that very thin graphite can be identified on a dielectric substrate…
Connected networks are a fundamental structure of neurobiology. Understanding these networks will help us elucidate the neural mechanisms of computation. Mathematically speaking these networks are `graphs' - structures containing objects…
This review presents the state of the art in strain and ripple-induced effects on the electronic and optical properties of graphene. It starts by providing the crystallographic description of mechanical deformations, as well as the…
Amongst the wide spectrum of potential applications of graphene, ranging from transistors and chemical-sensors to nanoelectromechanical devices and composites, the field of photonics and optoelectronics is believed to be one of the most…
Isolated, atomically thin conducting membranes of graphite, called graphene, have recently been the subject of intense research with the hope that practical applications in fields ranging from electronics to energy science will emerge.…
Graphene Oxide and reduced Graphene Oxide are intriguing materials for photonics and electronic devices both for intrinsic characteristics and as precursors for the synthesis of graphene. Whatever the application and the engineering…
Laboratory measurements are used to constrain the dielectric tensor for graphite, from microwave to X-ray frequencies. The dielectric tensor is strongly anisotropic even at X-ray energies. The discrete dipole approximation is employed for…
Theoretical study of graphite (graphene) edge is done. The most stable edge orientation is calculated to be a zigzag [110] edge. Possible applications of the result to the formation of different graphitic structures are discussed.
Pattern recognition problems in high energy physics are notably different from traditional machine learning applications in computer vision. Reconstruction algorithms identify and measure the kinematic properties of particles produced in…
Pristine monocrystalline graphene is claimed to be the strongest material known with remarkable mechanical and electrical properties. However, graphene made with scalable fabrication techniques is polycrystalline and contains inherent…
Based on density functional calculations, optimized structures of graphite oxide are found for various coverage by oxygen and hydroxyl groups, as well as their ratio corresponding to the minimum of total energy. The model proposed describes…
It is promising to apply quantum-mechanically confined graphene systems in field-effect transistors. High stability, superior performance, and large-scale integration are the main challenges facing the practical application of graphene…
Graphite has been used as neutron moderator or reflector in many nuclear reactors. The irradiation of graphite in a nuclear reactor results in a complex population of defects. Heating of the irradiated graphite at high temperatures results…
The nuclear interactions of atomic and low energy antiprotons are studied. Measurements of level shifts and widths in the lightest elements are analyzed and compared with new results obtained in heavy nuclei. Simple geometric properties of…
Atomic-scale fabrication is an outstanding challenge and overarching goal for the nanoscience community. The practical implementation of moving and fixing atoms to a structure is non-trivial considering that one must spatially address the…
Graphs are central to the chemical sciences, providing a natural language to describe molecules, proteins, reactions, and industrial processes. They capture interactions and structures that underpin materials, biology, and medicine. This…
Many physical systems can be best understood as sets of discrete data with associated relationships. Where previously these sets of data have been formulated as series or image data to match the available machine learning architectures,…
Dynamically changing graphs are used in many applications of graph algorithms. The scope of these graphs are in graphics, communication networks and in VLSI designs where graphs are subjected to change, such as addition and deletion of…
One of the hot topics in machine learning is the field of GNN. The complexity of graph data has imposed significant challenges on existing machine learning algorithms. Recently, many studies on extending deep learning approaches for graph…