Automated synthesis of nanoparticles
|Contact: Levenstein Mark, , email@example.com, +33 1 69 08 57 34
This internship focuses on advancing nanoparticle synthesis through real-time monitoring using in situ small-angle X-ray scattering (SAXS). The goal is to establish a precise control system for nanoparticle properties by creating an automated synthesis setup with a feedback loop between SAXS measurements and synthesis parameters. The intern will work on synthesizing model nanoparticles (SiO2) with sub-nanometer accuracy, analyzing SAXS patterns, understanding the impact of operational parameters on nucleation and aggregation rates, and ultimately building a feedback control loop for producing nanoparticles with predetermined sizes.
|Possibility of continuation in PhD: Oui
|Deadline for application:01/04/2024
Owing to size reduction, nanoparticles have outstanding properties suitable for a broad range of applications, like optics, energy production and storage, and medicine to name a few. Such applications often require very precise control over the size, structure, and aggregation state of the nanoparticles. But currently, this control is only approximate and essentially relies on trial-and-error approaches.
In this context, we are developing an approach where the synthesis of nanoparticles in solution is monitored in real time by in situ small-angle X-ray scattering (SAXS). The long-term objective is to precisely dictate the properties of the final nanoparticles by making an automated synthesis set-up, with a feedback loop between the size, number, and aggregation state of the nanoparticles as measured by SAXS and the operational parameters of the synthesis (e.g., injection of reactant, pH, temperature).
The aim of this internship is to build and validate the setup by synthesizing model nanoparticles (SiO2), with sub-nanometer accuracy on the size, and no aggregation. It will consist in 1) solution-based synthesis of SiO2 nanoparticles, 2) completing the real-time comparison of SAXS patterns with physical models, 3) understanding the dependency of the rates of nucleation, growth, and aggregation on operational parameters by using or improving current theories, and 4) using this fundamental understanding to build the feedback control loop and produce nanoparticles with pre-determined size.
|Technics/methods used during the internship:
Solution chemistry, small-angle X-ray scattering, Python coding, nucleation/growth theories, transmission electron microscopy, machine learning.
|Tutor of the internship