Microscopy Instrumentation and Nanopositioning at NSLS-II: Current Status and Future Directions

Partial densities of states from x-ray absorption and Auger electron spectroscopy: Use of core-hole memory

Ag L ₃ edge x-ray absorption and L ₃ - M _4,5 M _4,5 Auger electron spectra have been measured and simulated for a variety of x-ray electric-field polarization and Auger electron emission directions. The theory relies on density-functional theory, use of the Bethe-Salpeter equation, atomic multiplet theory, and a simplified model for the Auger line shape and its dependence on photon energy. We also demonstrate that partial densities of states for d T _2g , d E_g , and s A_g symmetry partial-wave channels at the Ag site in the solid can be deduced from the experimental measurements with only atomic theoretical input, i.e., with no solid-state theory other than assuming a Fermi edge.

Deep-learning-based image registration for nano-resolution tomographic reconstruction

Nano-resolution full-field transmission X-ray microscopy has been successfully applied to a wide range of research fields thanks to its capability of non-destructively reconstructing the 3D structure with high resolution. Due to constraints in the practical implementations, the nano-tomography data is often associated with a random image jitter, resulting from imperfections in the hardware setup. Without a proper image registration process prior to the reconstruction, the quality of the result will be compromised. Here a deep-learning-based image jitter correction method is presented, which registers the projective images with high efficiency and accuracy, facilitating a high-quality tomographic reconstruction. This development is demonstrated and validated using synthetic and experimental datasets. The method is effective and readily applicable to a broad range of applications. Together with this paper, the source code is published and adoptions and improvements from our colleagues in this field are welcomed.