sp 2 hybridized carbons constitute a broad class of solid phases composed primarily of elemental carbon and can be either synthetic or naturally occurring. Some examples are graphite, chars, soot, graphene, carbon nanotubes, pyrolytic carbon, and diamond-like carbon. They vary from highly ordered to completely disordered solids and detailed knowledge of their internal structure and composition is of utmost importance for the scientific and engineering communities working with these materials. Multiwavelength Raman spectroscopy has proven to be a very powerful and non-destructive tool for the characterization of carbons containing both aromatic domains and defects and has been widely used since the 1980s. Depending on the material studied, some specific spectroscopic parameters (e.g., band position, full width at half maximum, relative intensity ratio between two bands) are used to characterize defects. This paper is addressed first to (but not limited to) the newcomer in the field, who needs to be guided due to the vast literature on the subject, in order to understand the physics at play when dealing with Raman spectroscopy of graphene-based solids. We also give historical aspects on the development of the Raman spectroscopy technique and on its application to sp 2 hybridized carbons, which are generally not presented in the literature. We review the way Raman spectroscopy is used for sp 2 based carbon samples containing defects. As graphene is the building block for all these materials, we try to bridge these two worlds by also reviewing the use of Raman spectroscopy in the characterization of graphene and nanographenes (e.g., nanotubes, nanoribbons, nanocones, bombarded graphene). Counterintuitively, because of the Dirac cones in the electronic structure of graphene, Raman spectra are driven by electronic properties: Phonons and electrons being coupled by the double resonance mechanism. This justifies the use of multiwavelength Raman spectroscopy to better characterize these materials. We conclude with the possible influence of both phonon confinement and curvature of aromatic planes on the shape of Raman spectra, and discuss samples to be studied in the future with some complementary technique (e.g., high resolution transmission electron microscopy) in order to disentangle the influence of structure and defects.

We present an ultrafast neural network (NN) model, QLKNN, which predicts core tokamak transport heat and particle fluxes. QLKNN is a surrogate model based on a database of 300 million flux calculations of the quasilinear gyrokinetic transport model QuaLiKiz. The database covers a wide range of realistic tokamak core parameters. Physical features such as the existence of a critical gradient for the onset of turbulent transport were integrated into the neural network training methodology. We have coupled QLKNN to the tokamak modelling framework JINTRAC and rapid control-oriented tokamak transport solver RAPTOR. The coupled frameworks are demonstrated and validated through application to three JET shots covering a representative spread of H-mode operating space, predicting turbulent transport of energy and particles in the plasma core. JINTRAC-QLKNN and RAPTOR-QLKNN are able to accurately reproduce JINTRAC-QuaLiKiz T i,e and n e profiles, but 3 to 5 orders of magnitude faster. Simulations which take hours are reduced down to only a few tens of seconds. The discrepancy in the final source-driven predicted profiles between QLKNN and QuaLiKiz is on the order 1%-15%. Also the dynamic behaviour was well captured by QLKNN, with differences of only 4%-10% compared to JINTRAC-QuaLiKiz observed at mid-radius, for a study of density buildup following the L-H transition. Deployment of neural network surrogate models in multi-physics integrated tokamak modelling is a promising route towards enabling accurate and fast tokamak scenario optimization, Uncertainty Quantification, and control applications.

Highly oriented poly crystalline graphite (HOPG), boron-doped diamond (BDD), nanocrystalline diamond (NCD), ultra-nano-crystalline diamond (uNCD), fullerenes C60 and C70 and Diamond Like Carbon (DLC) surfaces are exposed to low pressure hydrogen plasma in a 13.56 MHz plasma reactor. Relative yields of surface produced Hions due to bombardment of positive ions from the plasma are measured by an energy analyzer cum quadrupole mass spectrometer. Irrespective of plasma conditions (0.2 and 2 Pa), HOPG surfaces show the highest yield at room temperature (RT), while at high temperature (HT), the highest yield (~ 5 times compared to HOPG surface at room temperature) is observed on BDD surfaces. The shapes of ion distribution functions (IDFs) are compared at RT and HT to demonstrate the mechanism of ion generation at the surface. Raman spectroscopy analyses of the plasma exposed samples reveal surface modifications influencing Hproduction yields, while further analyses strongly suggest that the hydrogen content of the material and the sp3/sp2 ratio are the key parameters in driving surface ionization efficiency of carbon materials under the chosen plasma conditions.