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).
Special guest at LSME
We are very pleased to welcome Dr Juan Carlos Idrobo from the Center for Nanophase Materials Science at the Oak Ridge National Laboratory for a two day visit. Dr Idrobo has been invited to present a seminar in the Quantum Science and Condensed Matter series, where he gives “A Glimpse into Electron Microscopy in the (…)
Teaching award for LSME director!
Congratulations to LSME director Prof. Cécile Hébert for being awarded the 2019 prize of best teacher in the Physics Section at the recent “Magistrale” Graduation Day!
Poster prize at Microscopy Conference 2019!
Congratulations to LSME PhD student Hui Chen, who received a Best Poster Award for Materials Science at the recent Microscopy Conference 2019 in Berlin. At the conference, Chen presented a poster on her ongoing research into improved STEM hyperspectral data segmentation and quantification entitled “STEM EDS/(EELS) for Deep-Mantle Rock Assemblages Analyses Assisted by Machine Learning”.