PhD Position: Low-Dose STEM-EELS & Data Analysis of Beam-Sensitive Materials
We are seeking a highly motivated PhD candidate to join our research team in developing state-of-the-art Low-Dose Electron Microscopy and Electron Energy-Loss Spectroscopy methods to characterize beam-sensitive functional materials.Specifically, the project will investigate photocatalytic semiconductors and their interfaces with biopolymers that modulate their energy-conversion performance. This multidisciplinary project is conducted in close collaboration between EPFL (…)
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, (…)
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 QuantificationIn 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 spectral (…)
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 EDX (…)