The dissociative electron attachment to the gas phase nucleobase adenine is studied using two different experiments. A double focusing sector field mass spectrometer is utilized for measurements requiring high mass resolution, high sensitivity, and relative ion yields for all the fragment anions and a hemispherical electron monochromator instrument for high electron energy resolution. The negative ion mass spectra are discussed at two different electron energies of 2 and 6 eV. In contrast to previous gas phase studies a number of new negative ions are discovered in the mass spectra. The ion efficiency curves for the negative ions of adenine are measured for the electron energy range from about 0 to 15 eV with an electron energy resolution of about 100 meV. The total anion yield derived via the summation of all measured fragment anions is compared with the total cross section for negative ion formation measured recently without mass spectrometry. For adenine the shape of the two cross section curves agrees well, taking into account the different electron energy resolutions; however, for thymine some peculiar differences are observed.

No abstract

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.

No abstract

Motivated by the potentially adverse effects of dissociative electron attachment (DEA) in focused electron beam induced processing (FEBIP), we have conducted a gasphase DEA study on the common FEBIP precursor molecule, cobalt tricarbonyl nitrosyl. We have determined the absolute DEA cross-sections and the branching ratios for the individual fragmentation processes in the energy range from about 0-9 eV. We further report the adiabatic electron affinities (EAs) of the corresponding neutral radicals. Finally, we propose a fragmentation mechanism, which we believe is valid for DEA to metal-carbonyl compounds in general.Initially discovered as an unwelcome side effect in electron microscopy, [1] FEBIP has quickly been embraced as a clean and precise tool for manipulating and controlling matter on a small scale. A focused, high-energy electron beam is used to locally dissociate adsorbed precursor molecules. Ideally, a chemically and structurally well-defined deposit is left behind while volatile fragments are pumped away. Minimization of the spatial resolution and eliminating contamination of the deposited structures remain the two main challenges of FEBIP. [2,3] Albeit a highly focused primary electron beam, the width of the deposits is typically a multiple of the incident beam diameter.[4] Elastic and inelastic scattering events are unavoidable when irradiating samples with high-energy electron beams. Consequently, secondary (SE) and backscattered electrons (BSE) are emitted from the sample surface and the deposit, creating electron flux outside the focus point of the primary beam. Simulations as well as experiments show that the SE energy distribution typically peaks at low energies, that is, < 15 eV, and their intensities are far from negligible. [2,4,5] At these low energies, a new fragmentation pathway becomes available, that is, DEA. In contrast to fragmentation by direct electron impact, where excess energy of several electron volts is required, DEA can occur close to zero eV threshold. Furthermore, DEA reactions can exhibit fairly large cross-sections of 10 À18 to 10 À16 m 2 and are highly bond selective with regards to the electron energy. [6,7] Because of the abundance of low-energy SEs and BSEs generated in FEBIP and the potentially high cross-sections, DEA may play a significant role in FEBIP broadening. Additionally, the high bond selectivity in DEA may contribute to the deposition of incompletely decomposed precursor molecules and thus to an increased nonmetallic fraction of the deposit. Presently, no DEA cross-section data has been reported on relevant FEBIP precursor molecules and current simulations either neglect dissociation caused by low-energy electrons altogether or have to rely on an educated guess for the cross-sections.[4]Herein we report absolute DEA cross-sections and branching ratios for cobalt tricarbonyl nitrosyl. We report the electron affinities for the neutral radical fragments and we propose a general DEA mechanism for metal-carbonyl compounds.The present experiments were performed in...