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 (…)
Our lab
Welcome to the Electron Spectrometry and Microscopy Laboratory (LSME). In this laboratory we address scientific problems through the development and utilization of advanced electron microscopy techniques and data processing and interpretation. Current research interests include:
– Improved elemental quantification in analytical scanning transmission electron microscopy using combined EDXS and EELS.
– 3D reconstruction of curvilinear and other objects via “single shot” data acquisition in STEM and algorithmic reconstruction for fast/low dose applications in materials and life sciences.
– Open science and “Big data” in electron microscopy: strategies for open source exporting, management and processing of hyperspectral data created by new generation spectroscopy detectors.
– Characterization of structure and structural evolution in energy technology materials (fuel cells and transparent conductive oxide layers for photovoltaic cells).
– Precision measurements of crystalline structure and electronic properties from the atomic to nano scales by aberration corrected microscopy and spectroscopy.
– Measuring near field optical properties of nanophotonic and plasmonic structures using high energy resolution low loss EELS (e.g. Au-Fe “magnetoplasmonic” nanoparticles).