Cécile Hébert

Cécile Hébert was born in France in 1970. She obtained her Ingeneer degree (physics) and her PhD degree (design of a new energy filter for transmission electron microscopy, under the direction of Prof. B. Jouffrey) at the Ecole Centrale in Paris.

As a post doc with Prof. P. Schattschneider, she was working on the calculation of the fine structure of ionisation edges in EELS. As a research assistant at the Vienna University of Technology, she was one of the main participant to the CHIRALTEM project dealing with the measurement of magnetic circular dichroism in the electron microscope.

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related units

Courses taught by this person

Related research domains

Related publications

People doing similar research

Biography

Official source

About this result

Related units (12)

Related units

Courses taught by this person (3)

Courses taught by this person

Related research domains (79)

Transmission electron microscopy (TEM) is a microscopy technique in which a beam of electrons is transmitted through a specimen to form an image. The specimen is most often an ultrathin section les

Electron energy loss spectroscopy (EELS) is a form of electron microscopy in which a material is exposed to a beam of electrons with a known, narrow range of kinetic energies. Some of the electrons

An electron microscope is a microscope that uses a beam of electrons as a source of illumination. They use electron optics that are analogous to the glass lenses of an optical light microscope. As

Related research domains

Related publications (168)

Related publications

People doing similar research (94)

People doing similar research

Transmission electron microscopy (TEM) is a microscopy technique in which a beam of electrons is transmitted through a specimen to form an image. The specimen is most often an ultrathin section les

Electron energy loss spectroscopy (EELS) is a form of electron microscopy in which a material is exposed to a beam of electrons with a known, narrow range of kinetic energies. Some of the electrons

An electron microscope is a microscope that uses a beam of electrons as a source of illumination. They use electron optics that are analogous to the glass lenses of an optical light microscope. As

PHYS-101(h): General physics : mechanics (SV I)

Le but du cours de physique générale est de donner à l'étudiant les notions de base nécessaires à la compréhension des phénomènes physiques. L'objectif est atteint lorsque l'étudiant est capable de prévoir quantitativement les conséquences de ces phénomènes avec des outils théoriques appropriés.

PHYS-105: Advanced physics II (thermodynamics)

Ce cours présente la thermodynamique en tant que théorie permettant une description d'un grand nombre de phénomènes importants en physique, chimie et ingéniere, et d'effets de transport. Une introduction à la physique statistique renforce les notions acquises grâce à une modélisation microscopique.

PHYS-637: Electron Matter Interactions in Transmission Electron Microscopy

This course will present the fundamentals of electronâmatter interactions, as occuring in the energy range available in modern transmission electron microscopes, namely 60-300 keV electrons. Diffraction and high-resolution image formation as well as electron energy-loss spectrometry will be covere

espm: A Python library for the simulation of STEM-EDXS datasets

Duncan Alexander, Hui Chen, Cécile Hébert, Nathanaël Perraudin

We present two open-source Python packages: "electron spectro-microscopy"(espm) and "electron microscopy tables"(emtables). The espm software enables the simulation of scanning transmission electron microscopy energy-dispersive X-ray spectroscopy datacubes, based on user-defined chemical compositions and spatial abundance maps of constituent phases. The simulation process uses X-ray emission cross-sections generated via state-of-the-art calculations made with emtables. These tables are designed to be easily modifiable, either manually or using espm. The simulation framework is designed to test the application of decomposition algorithms for the analysis of STEM-EDX spectrum images with access to a known ground truth. We validate our approach using the case of a complex geology-related sample, comparing raw simulated and experimental datasets and the outputs of their non-negative matrix factorization. In addition to testing machine learning algorithms, our packages will also help experimental design, for instance, predicting dataset characteristics or establishing minimum counts needed to measure nanoscale features.

ELSEVIER2023

Investigating Magma Ocean Solidification on Earth Through Laser‐Heated Diamond Anvil Cell Experiments

James Badro, Charles-Edouard Boukaré, Marco Cantoni, Philippe Gillet, Cécile Hébert, Farhang Nabiei

We carried out a series of silicate fractional crystallization experiments at lower mantle pressures using the laser-heated diamond anvil cell. Phase relations and the compositional evolution of the cotectic melt and equilibrium solids along the liquid line of descent were determined and used to assemble the melting phase diagram. In a pyrolitic magma ocean, the first mineral to crystallize in the deep mantle is iron-depleted calcium-bearing bridgmanite. From the phase diagram, we estimate that the initial 33%–36% of the magma ocean will crystallize to form such a buoyant bridgmanite. Substantial calcium solubility in bridgmanite is observed up to 129 GPa, and significantly delays the crystallization of the calcium silicate perovskite phase during magma ocean solidification. Residual melts are strongly iron-enriched as crystallization proceeds, making them denser than any of the coexisting solids at deep mantle conditions, thus supporting the terrestrial basal magma ocean hypothesis (Labrosse et al., 2007).

2021

Structure-aware Multi-view 3D Reconstruction of Dislocations in TEM with Message Passing Neural Networks

Okan Altingövde, Pascal Fua, Gulnaz Ganeeva, Cécile Hébert, Anastasiia Mishchuk, Emadeddin Oveisi

Efficient analysis of the three-dimensional (3D) shape and distribution of curvilinear crystal defects, namely dislocations, is an open research topic in material science and computer vision. In order to determine the structural and opto-electrical characteristics of the material, accurate 3D reconstructions of dislocations are required. In its current state, an established way to obtain 3D information of dislocations is electron tomography which relies on acquiring dozens of images from tilted sample for wide range of angles. Although tomography yields sufficient results in many cases, it is manually demanding to acquire, register and process dozens of images. In previous works [1, 2], it is shown that a classical stereo reconstruction approach may be applied as an alternative in order to increase reconstruction throughput while providing good performance. A later work [3] employing modern data-driven computer vision models showed that this stereo approach can be largely automatized and be used in various imaging conditions. In this work, we further improve the performance and memory consumption of previous convolutional neural network (CNN) based stereo reconstruction approach and extend it to be applicable in cases where more than two images are available. Our proposed multi-view approach directly incorporates the structural prior of dislocations to the model"s architecture and estimates 3D line segments approximating 3D shape of dislocations. In its core, our method has two stages which are structural connectivity estimation and depth estimation. In first stage, we employ a CNN to estimate affinity of sampled image locations on dislocation cores. This affinity information is used in the later depth estimation stage to eliminate the depth discontinuities on dislocations (Figure1). Unlike previous deep data-driven method, our new approach uses structural knowledge to separate depth estimations of different dislocation segments and yields more accurate reconstructions. In Figure 2, an example affinity estimation is shown for 4 different image locations on the same dislocation. It may be seen that dislocation points that are connected by dislocation segment in 3D results in higher affinity. This information is crucial to connect 3D point estimations and obtain linear structures in 3D.

2021