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Stefano Marchesini

Researcher at Lawrence Berkeley National Laboratory

Publications -  172
Citations -  12678

Stefano Marchesini is an academic researcher from Lawrence Berkeley National Laboratory. The author has contributed to research in topics: Diffraction & Ptychography. The author has an hindex of 49, co-authored 167 publications receiving 11520 citations. Previous affiliations of Stefano Marchesini include University of California, Berkeley & French Alternative Energies and Atomic Energy Commission.

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Journal ArticleDOI

Surface characterization of colossal magnetoresistive manganites La1-xSrxMnO3 using photoelectron spectroscopy

TL;DR: Mannella et al. as discussed by the authors studied the temperature and time dependence of the surface chemical composition and atomic structure of in situ fractured colossal magnetoresistive perovskites La 1− x Sr x MnO (x ǫ = 0.3, 0.4) using core-level photoelectron spectroscopy and diffraction, simultaneous with observing marked changes in both core and valence electronic structure on going above the Curie temperature.
Journal ArticleDOI

Ab initio compressive phase retrieval

TL;DR: In this paper, the authors used the fact that an image of a natural object is compressible to image molecules replicated in a crystal structure to obtain structural information about an object, which can be obtained from diffraction amplitude measurements that account for either one of these traits.
Journal ArticleDOI

Shaping coherent x-rays with binary optics

TL;DR: In this paper, a binary zone plate design strategy was proposed to generate arbitrary illuminations for coherent multiplexing, structured illumination, and wavefront shaping experiments, where the duty cycle, harmonic order, and zone placement were adjusted to vary both the amplitude and phase of the wavefront at the lens.
Book ChapterDOI

High Performance Partial Coherent X-Ray Ptychography

TL;DR: This paper proposes the first high performance implementation of GDP for partial coherence X-ray ptychography, and exploits an efficient data layout and multi-gpu parallelism to achieve massive acceleration and efficient scaling.