Description du sujet de thèse

The goal of this project is to create high-performance, optically resonant silicon particles embedded in hydrogels, using self-driving platforms and machine learning to discover optimized particle syntheses. We plan to create efficient optimization of nanoparticle syntheses. Automated synthesis will produce large data sets over a wide parameter space, characterized by comparison of experimental spectra to an idealized, simulated spectrum. The majority of samples will be rapidly eliminated by in situ analysis. A first level of characterization should be carried out automatically, using software to analyze the properties of spectra obtained in situ (e.g. spectral position, peak intensity, FWHM). Particles with high conformity to the target spectrum will be characterized ex situ (e.g. TEM, SEM). A synthesis of specialty particles conforming to predetermined specifications can be found through the coupling of simulation, synthesis, in situ characterization and machine learning. We will resolve the spectral overlap that currently limits the identification of ideal conditions via combing multiple detection techniques. Concerning the different contributions to the extinction spectrum, it is possible to fit spectra with overlapping peaks with relatively few parameters. By combining several in situ characterization techniques (e.g. Raman, UV-Vis and DLS spectroscopies), it will be possible to target the source of overlapping peaks, be it polydispersity in terms of particle size, chemical composition or shape. We will thus apply self-driving platforms to new and unexplored particle syntheses. Finally, we will use silicon particles to create multicolored hydrogels. Colloidal inks can provide stable, vivid colors. By using oppositely charged particles, e.g. silicon particles functionalized with polyelectrolytes, hydrogels can be prepared to create 3D printable colloidal inks with intense, tunable colors.

Contexte de travail

This position is funded by a MITI project, as part of a collaborative research program between the University of Toronto and the Centre National de la Recherche Scientifique (CNRS). The PhD student will work with a PhD student in Canada. He or she will be asked to go to Canada once per year.
The application will include a complete CV, a letter of motivation, and 2 of the candidate's publications. Letters of recommendation can be optionally included in a single PDF with the letter of motivation. The application should be written in English and submitted through the CNRS website. A first contact directly with Dr. Drisko is highly recommended (glenna.drisko@icmcb.cnrs.fr).

Contraintes et risques

Risks related to products (chemicals including CMR, asbestos, etc.), emissions (smoke, wood dust, etc.), waste (sewage, stagnant water, etc.).
Risks related to work equipment (dangerous, pressurized equipment, vibrating tools, restrictive Personal Protective Equipment - PPE, etc.).
Noise-related risks (loud noise)
Working on screens (ergonomics, etc.)