2 Works

Supplement to: Machine learning classification for field distributions of photonic modes

Carlo Barth & Christiane Becker
Electromagnetic modes of photonic nanostructures can exhibit increased near-field energy densities which can be applied in many fields such as biosensing, quantum dot solar cells or photon upconversion. Optimizing such systems enforces to systematically analyze large amounts of numerically obtained three-dimensional field distribution data, as in the presented dataset. The simulated system is a silicon photonic crystal slab on glass (subspace) with a hexagonal lattice of cylindrical holes. The holes are filled with a medium...

Neutron study of the topological flux model of hydrogen ions in water ice

J.-U. Hoffmann, K. Siemensmeyer, S. Isakov, D. J. P. Morris, B. Klemke, I. Glavatskyi, K. Seiffert, D. A. Tennant, S. Sondhi & R. Moessner
The familiarity of water ice means we often overlook its non-trivial character illustrated, for example, by the many snowflake morphologies resulting from disordered combinations of covalent and hydrogen bonds between hydrogen and oxygen atoms in water ice’s most common phase (Ih) that keep the H_2 O molecular character. Using neutron diffraction on the flat-cone diffractometer E2 at BER-II, Helmholtz-Zentrum Berlin, we probe the atomic scale configuration in the Ih phase of water ice to test...

Registration Year

  • 2018
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  • Dataset
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