Cécile Hébert

Biographie

Cécile Hébert est née en France, en 1970. Elle a obtenu son diplôme d'ingénieure (option physique) puis son doctorat ("Etude d'un nouveau filtre d'énergie des électrons pour le microscope électronique à transmission") à l'Ecole centrale de Paris. Doctorante dans le laboratoire du professeur Jouffrey, elle a créé un nouveau filtre d'énergie des électrons, pour le microscope électronique à transmission, qui a été breveté par le CNRS.

En tant que post-doctorante dans le laboratoire du professeur Schattschneider, elle a mené à bien une étude marquante sur la simulation des structures fines des seuils de perte d'énergie des électrons en comparaison avec l'expérience. En 2005, elle a été l'une des actrices principales dans l'élaboration du projet européen CHIRALTEM visant à développer dans le microscope électronique à transmission une méthode similaire à la méthode XMCD (X Ray Magnetic Circular Dichroism) connue en spectrométrie d'absorption des rayons X.

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https://people.epfl.ch/cecile.hebert?lang=fr

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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

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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

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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

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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

2021