Description du sujet de thèse

SCIENTIFIC CONTEXT

The rich versatility of nanoalloys makes them suitable for a large range of prospective applications, with examples in catalysis, plasmonics, sensing, and data storage. The advantages of nanoalloys are linked to their small size that achieves high surface area available for catalysis with only small amounts of often costly materials, such as Rh, Pd, or Au. Even more so, the number of atoms at surface steps, which may represent important reactive sites, is greatly enhanced in nanoparticles (NPs). In nanoalloys, the amount of rare raw material may be further decreased by replacing the core of the particles with another material. Furthermore, the second material may be used to tune the electronic structure at the particle surface. Even more possibilities arise with multi-component particles.

The realization of the full potential of these systems requires the design of nanoalloys of controlled size, morphology, and chemical composition. Over the last decades, a considerable number of physical and chemical synthesis methods have been developed, allowing now for some control of the final product. In this context, the path dependence, i.e. the synthesis pathway, and the environment, such as the contact with a substrate, is crucial because of the inherent metastability of nanoalloys. The wealth of structural possibilities of nanoalloys calls for reliable methods for their characterization.

Alongside X-ray scattering methods, electron microscopy stands out as a powerful analysis tool. High-resolution transmission electron microscopy (HRTEM) allows for imaging at atomic resolution in case of aberration corrected microscopes. Scanning transmission electron microscopy (STEM) in the high-angle annular dark-field mode (HAADF), in particular, is useful for imaging alloyed systems because of the contrast regarding atom types. If one wants to fully analyze the distribution of sizes, shapes, and chemical orderings of a given sample, many images need to be recorded and analyzed for a representative statistical analysis. Selecting only a small portion of the sample for further analysis can also introduce some subjectivity into the final results.

PROJECT

Recent developments in electron microscopy allow the observation of nanomaterials at atomic resolution. The interpretation of images from experiments is, however, not always straightforward because of lens aberrations. Usually, image simulations are used to compare with images from experiments and to determine structural details. This process is very time-consuming for electron microscopy specialists. With this project, we want to make the analysis of large sets of electron microscopy images practical and maybe even more objective, thanks to automated systems based on deep learning (DL).

To establish a training database for the envisioned DL systems, we will rely on NP structures obtained from atomistic simulations. As we concentrate here on nano-alloys, interatomic potentials for transition metals are required as a basis for the simulations. The second moment approximation of tight-binding (TB-SMA) has been used successfully in the past for many studies on bi-metallic nano-alloys. With these efficient interatomic potentials, it is relatively easy to obtain a sufficient number of realistic structures of nanoalloy particles by sampling the correct thermodynamic ensemble. The high degree of control during the simulations makes it possible to sample particles in meta-stable configurations, e.g. core-shell systems, that are partially ordered or in Janus configuration as obtained experimentally in certain growth conditions. These structures can then be used as a basis for the generation of electron microscopy images. While these kinds of calculations, based on the multi-slice technique, have been used for a long time to generate images for comparison with images from experiments, recent software improvements make the automatic and fast generation of many realistic images more practical.

OBJECTIVES
With this project, we wish to seize the emerging opportunity of leveraging DL for the analysis of atomic resolution images of nano-objects that arises thanks to recent developments in three relevant fields: Firstly, electron microscopes (STEM and HRTEM) has reached atomic resolution. Secondly, atomistic simulations of nano-objects are now realistic and fast enough to be able to generate databases for the subsequent training of DL systems. Thirdly, while DL remains a very active field of fundamental research, it has become efficient and versatile enough to be used in practical applications.

These DL systems will lead to:

Higher quality images (denoising, super-resolution)
Automated analysis of nanoalloy properties (number, size, shape, crystallinity, (meta-)stability, etc.)
Classification of nanoalloys in terms of chemical ordering

Contexte de travail

The ICMN UMR 7374 (Interfaces, Confinement, Materials, and Nanostructures) is a joint research laboratory of the University of Orléans and the CNRS Institute of Physics (INP), with the Institute of Chemistry as a secondary affiliation, specializing in innovative materials.

ICMN's activities lie at the intersection of physics and chemistry. They focus on the study of nanostructured materials and confined environments, where the high proportion of interfaces and the finite amount of matter give rise to remarkable properties. The studied systems share the common feature of exhibiting nanoscale heterogeneities and often being organized across different size scales. They encompass a wide variety of forms (nanoparticles, porous media, confined fluids, colloids, multimaterials, self-organized media) and compositions (carbon, metallic alloys, oxides, clays, polymers).

The laboratory develops and characterizes these materials, aiming to understand and control their architecture (structure, organization, nanostructuring, porosity) and their surface/interface physico-chemistry (functionalization, topography) to modulate or optimize their properties. The heterogeneous and complex structure of the studied environments requires the development of approaches that target the intermediate scales of matter, between the nanometer and micrometer, using both experimental synthesis and characterization tools—often in situ or even operando (from laboratory and Synchrotron)—as well as modeling techniques (Monte Carlo, molecular and Brownian dynamics, machine learning). These methods enable spatially and temporally resolved investigations in real or controlled environments whenever possible.

The project falls under the "Nanostructures" research axis.

Contraintes et risques

nothing in particular