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Materials Data on CsSi by Materials Project

SiCs crystallizes in the cubic P-43n space group. The structure is three-dimensional. there are two inequivalent Cs sites. In the first Cs site, Cs is bonded in a 6-coordinate geometry to six Si atoms. There are a spread of Cs–Si bond distances ranging from 3.58–4.03 Å. In the second Cs site, Cs is bonded in a 6-coordinate geometry to six Si atoms. There are three shorter (3.70 Å) and three longer (4.03 Å) Cs–Si bond lengths. There are two inequivalent Si sites. In the first Si site, Si is bonded in a 9-coordinate geometry to six Cs and three equivalent Si atoms. There are two shorter (2.43 Å) and one longer (2.45 Å) Si–Si bond lengths. In the second Si site, Si is bonded in a 9-coordinate geometry to six Cs and three equivalent Si atoms. All Si–Si bond lengths are 2.44 Å.

36 MATERIALS SCIENCE↗

Materials Data on CsSi by Materials Project

SiCs crystallizes in the tetragonal I4_1/acd space group. The structure is three-dimensional. there are two inequivalent Cs sites. In the first Cs site, Cs is bonded in a 8-coordinate geometry to eight equivalent Si atoms. There are a spread of Cs–Si bond distances ranging from 3.75–4.08 Å. In the second Cs site, Cs is bonded in a 6-coordinate geometry to six equivalent Si atoms. There are a spread of Cs–Si bond distances ranging from 3.81–3.99 Å. Si is bonded in a 10-coordinate geometry to seven Cs and three equivalent Si atoms. There are two shorter (2.43 Å) and one longer (2.44 Å) Si–Si bond lengths.

36 MATERIALS SCIENCE↗

Multislice forward modeling of coherent surface scattering imaging on surface and interfacial structures

To study nanostructures on substrates, surface-sensitive reflection-geometry scattering techniques such as grazing incident small angle X-ray scattering are commonly used to yield an averaged statistical structural information of the surface sample. Grazing incidence geometry can probe the absolute three-dimensional structural morphology of the sample if a highly coherent beam is used. Coherent surface scattering imaging (CSSI) is a powerful yet non-invasive technique similar to coherent X-ray diffractive imaging (CDI) but performed at small angles and grazing-incidence reflection geometry. A challenge with CSSI is that conventional CDI reconstruction techniques cannot be directly applied to CSSI because the Fourier-transform-based forward models cannot reproduce the dynamical scattering phenomenon near the critical angle of total external reflection of the substrate-supported samples. To overcome this challenge, we have developed a multislice forward model which can successfully simulate the dynamical or multi-beam scattering generated from surface structures and the underlying substrate. The forward model is also demonstrated to be able to reconstruct an elongated 3D pattern from a single shot scattering image in the CSSI geometry through fast-performing CUDA-assisted PyTorch optimization with automatic differentiation.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

Radiation shielding analysis of the small-angle X-ray scattering flight tube end station of APS upgrade project

The CSSI beamline of the APSU will have a 20.5 m long, 2.75 m diameter SS end station designed to accept pink beam. In this work, the radiation shielding analysis of this end station is analyzed using STAC8 and FLUKA codes. Effect of mirror properties such as reflectivity, coating, inclination and roughness as well as variation in the steel composition are also studied. 8 mm thick SS is found to be adequate if the source is defined by a pinhole and the white beam is reflected with two mirrors inclined at 3.0 mradian or more. The dose rates from monochromatic beams are found to be below the desired levels when the XOP calculated bandwidth (BW) are used while the use of a flat 0.1% BW requires the mirror inclinations to be above 2.5 mradian or more. STAC8 results are consistently higher than the FLUKA results by about 1.5–2.0 times.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Inversion of coherent surface scattering images via deep learning network

Coherent surface scattering imaging is a powerful tool for imaging a surface/interface of a thin nanostructure deposited on an opaque substrate. A mathematical conversion of an object image from a scattering pattern is essential for coherent surface scattering imaging to visualize structures of specimens. It has been achieved by using phase retrieval algorithms requiring oversampling in scattering patterns and employing alternating projection approaches. It is a computationally challenging and time-consuming process. In this paper, we demonstrate CSSI-NN, which is a deep learning neural network model to predict images of objects from scattering intensities in coherent surface scattering imaging. This model allowing for the instant outcome from scattering patterns would be tremendously beneficial not only for effective experiments but also for data analysis of phase retrieval.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗