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 (…)
EPFL news on enhanced chemical analysis at the nanoscale
In a front-page news article, EPFL profiles the latest publication from LSME, part of the lab’s ongoing work on innovating novel machine learning approaches for the improved analysis of analytical TEM data: AI enhances chemical analysis at the nanoscale
Improving X-ray analytics: new publication in Nano Letters!
Just out in Nano Letters is our 100% lab-driven work on: Leveraging Machine Learning for Advanced Nanoscale X-ray Analysis: Unmixing Multicomponent Signals and Enhancing Chemical Quantification In this letter, LSME introduces a new method for processing STEM-EDX spectroscopy data sets, that we term non-negative matrix factorization based pan-sharpening (PSNMF). Leveraging the Poisson nature of EDX (…)
Welcoming two new collaborators!
This November, we were pleased to have two new collaborators join the LSME team. During his studies of applied physics and renewable energy studies, Sebastian Cozma discovered a deep interest in microstructure characterization and analytical techniques. To pursue this interest, Sebastian is beginning a Ph.D. with Prof. Cécile Hébert on segmentation and quantification of STEM (…)
New in ACS Nano: EELS mapping of dielectric photonic nanocavities
In a new article published in ACS Nano, working with Dr Valentin Flauraud and Dr Frank Demming-Janssen, LSME scientist Dr Duncan Alexander uses advanced electron spectroscopy and finite element simulations to analyse the spatial and spectral signatures of different optical excitations supported in patterned silicon photonic nanocavities. By sampling nanocavities of different shapes and sizes, (…)
New in Acta Materialia: growth of grain triplets in ZnO thin films
Just published in Acta Materialia, using the detailed analysis of transmission electron microscopy (TEM) data, researchers from the LSME identify a novel growth mechanism of grain triplets in polycrystalline thin films of ZnO. The study primarily depends on the mining of data acquired using automated crystal orientation mapping with scanning nanobeam electron diffraction, which is (…)
Welcome to Adrien Teurtrie!
We are pleased to announce the arrival of Dr Adrien Teurtrie, who joins the LSME as a post-doctoral researcher on projects of Opening Up Hyperspectral TEM Data, as supported by the EPFL Open Science Fund, and new approaches to hyperspectral data decomposition using machine learning methods. Adrien recently completed a PhD in the STEM Group (…)