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

Nazaretski, Evgeny; Xu, Weihe; Yan, Hanfei; Huang, Xiaojing; Coburn, D. S.; Ge, Mingyuan; Lee, W.-K.; Gao, Yuan; Fuchs, Martin R.; Chu, Yong S.

doi:10.1080/08940886.2018.1506233

Cited by 2 publications

(1 citation statement)

References 29 publications

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“…Specifically, we focus on the nano-resolution full-field transmission X-ray microscopy (TXM) (Andrews et al, 2009), which utilizes a Fresnel zone plate as the objective lens for magnifying the X-ray image and, thus, operates at an effective spatial resolution of $ 15-30 nm (Chao et al, 2005;Liu et al, 2011). Compositional and chemical sensitivities have been demonstrated by conducting X-ray microscopy measurements with varying X-ray energy (Liu et al, 2012;Nazaretski et al, 2018). Nano-resolution tomography and, in particular, nanoresolution spectro-tomography are regarded as a powerful modality of TXM with successful case studies spanning energy materials, environmental science, geoscience, etc.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Wang

et al. 2021

J Synchrotron Rad

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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.

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Section: Introductionmentioning

confidence: 99%

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

Zhang

Wang

et al. 2021

J Synchrotron Rad

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

Shirley

Woicik

2022

Journal of Vacuum Science &Amp; Technology A

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Ag [Formula: see text] edge x-ray absorption and [Formula: see text] 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 [Formula: see text], [Formula: see text], and [Formula: see text] 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.

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

Cited by 2 publications

References 29 publications

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

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

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

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