Synthesis and machine learning modelling of embedded Ag nanoparticles for controlled anti-bacterial devices

Work package:
Because of their toxicity and their high propensity for oxidation and sulfidation, Ag nanoparticles (AgNPs) require to be incorporated into a matrix for many applications. For instance, incorporating Ag NPs into polymers used as base materials for many medical devices has made it possible to prevent bacterial growth via silver ions release while protecting people in contact with these nanocomposites from the Ag NPs toxicity. In this project, we intend to develop new “nano-safer by design” systems (AgNPs embedded in silica matrices) in order to provide locally controlled antimicrobial activity over an adjustable period of time (from few days up to a few months) without damaging neighbored parts. In order to achieve this goal, the properties of such AgNPs-based nanocomposites need to be carefully investigated and adapted in order to achieve the fine tuning of silver release. This can be done, on one side, by acting on the detachment of Ag+ from the AgNPs and, on the other side, on the host matrix (density, porosity, solubility) to allow easier diffusion of the Ag+ and AgNPs through the matrix. During this internship, the student will be involved in two different aspects of this project: (1) the synthesis of AgNPs-based devices by ion implantation and their characterization using High Resolution Transmission Electron Microscopy (HR-TEM) (2) the simulation of the Ag+ pathways in SiO2 by means of Molecular Dynamics using a “machine learning” based interaction potential