Category: LSME publication
New multiscale Bayesian approach to quantification and denoising of energy-dispersive X-ray data
In a new publication in Machine Learning: Science and Technology, we describe a multiscale Bayesian approach for quantifying and denoising EDX spectrum images. The approach is innovated by LSME postdoc Pau Torruella working in collaboration with Abderrahim Halimi from Heriot-Watt University. Building on previous LSME developments for EDXS data processing, this approach leverages both the (…)
Engineering Symmetry Breaking Interfaces by Nanoscale Structural-Energetics in Orthorhombic Perovskite Thin Films
In our new research article published in ACS Nano, LSME scientists use detailed, quantitative scanning transmission electron microscopy analyses to identify and characterize a new type of structural interface created in epitaxially strained orthorhombic perovksite thin films. This study, which comes from a multi-year collaboration with functional oxide growth experts at the University of Geneva, (…)
Physics-guided NMF for phase separation and quantification of STEM-EDXS data
In a strong collaboration with the Swiss Data Science Center, we announce our latest publication From STEM-EDXS data to phase separation and quantification using physics-guided NMF in Machine Learning Science and Technology. In this paper, we present a detailed description of the theoretical principles and functioning of our new algorithm EsPM-NMF in the Python-based espm (…)
Electron beam writing of crystal structure at oxide interfaces
In collaboration with researchers from the Department of Quantum Matter Physics at the University of Geneva, we study the gap between strontium titanate membranes and Nb-doped strontium titanate carrier substrates onto which they have been transferred. In thermally annealed samples, raster scanning an intense STEM electron beam causes the residual Sr, Ti and O atoms (…)