Publications by authors named "M Yabashi"

We propose a multi-frame blind deconvolution method using an in-plane rotating sample optimized for X-ray microscopy, where the application of existing deconvolution methods is technically difficult. Untrained neural networks are employed as the reconstruction algorithm to enable robust reconstruction against stage motion errors caused by the in-plane rotation of samples. From demonstration experiments using full-field X-ray microscopy with advanced Kirkpatrick-Baez mirror optics at SPring-8, a spatial resolution of 34 nm (half period) was successfully achieved by removing the wavefront aberration and improving the apparent numerical aperture.

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SPring-8-II is a major upgrade project of SPring-8 that was inaugurated in October 1997 as a third-generation synchrotron radiation light source. This upgrade project aims to achieve three goals simultaneously: achievement of excellent light source performance, refurbishment of aged systems, and significant reduction in power consumption for the entire facility. A small emittance of 50 pm rad will be achieved by (1) replacing the existing double-bend lattice structure with a five-bend achromat one, (2) lowering the stored beam energy from 8 to 6 GeV, (3) increasing the horizontal damping partition number from 1 to 1.

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We report on the feasibility of Fourier transform holography in the hard X-ray regime using a Free Electron Laser source. Our study shows successful single and multi-pulse holographic reconstructions of the nanostructures. We observe beam-induced heating of the sample exposed to the intense X-ray pulses leading to reduced visibility of the holographic reconstructions.

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Article Synopsis
  • * Three types of neural network architectures (neural network, convolutional neural network, and U-net) were tested, with U-net proving to be the most effective for noise reduction and accurately reproducing images even with limited photon data.
  • * The U-net model was applied successfully to real experiments, where it improved the quality of nanoparticle images taken during X-ray imaging, underscoring the role of transfer learning when adapting to different particle shapes from the training data.
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Laser-driven dynamic compression experiments of plastic materials have found surprisingly fast formation of nanodiamonds (ND) via X-ray probing. This mechanism is relevant for planetary models, but could also open efficient synthesis routes for tailored NDs. We investigate the release mechanics of compressed NDs by molecular dynamics simulation of the isotropic expansion of finite size diamond from different P-T states.

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