High-Throughput Continuous Automated Production of Noble Metal Nanoparticles Assisted By AI

Au croisement des Sciences et de la Technologie, le LGC  participe aux dernières avancées du Génie Chimique et développe des travaux de recherche expérimentaux et théoriques pour apporter la connaissance au cœur des procédés de transformation de la matière et de l’énergie.

Soutenu par ses trois tutelles, le CNRS, Toulouse INP et l’Université Toulouse, le LGC compte plus de 300 collaborateurs répartis dans 6 départements de recherche qui s’attachent à répondre à 5 enjeux sociétaux : Eaux et effluents, Energie, Bioraffinerie, Matériaux et Ingénierie de la santé.

As part of the PEPR DIADEM program, the 2FAST project aims to demonstrate the potential of autonomous microfluidic laboratories for synthesizing materials with controlled properties. A proof of concept has been developed for the synthesis of silver nanoparticles, whose morphology and properties are challenging to control due to the fast reaction kinetics involved. Using in-house designed microfluidic chips, ultra-fast mixing times are achieved, enabling better control and reproducibility compared to conventional batch synthesis. In a collaborative work between LGC Toulouse and CEA Marcoule, we have recently demonstrated the in-line integration of light extinction spectrometry at the microfluidic scale, for real-time synthesis monitoring. This setup enables an AI system to automatically adjust experimental parameters (e.g., reagent flow rates, temperature) to reach the desired nanoparticles properties. This automated platform also allows rapid exploration of a wide range of experimental conditions with minimal reagent consumption and time. The collected data can then be used to train AI models capable of predicting experimental outcomes.

The objectives of this internship are to:

• Upgrade the existing platform from both a hardware (pumps, autosampler, microfluidic chip, etc.) and software (control app, data management, optimization algorithm) perspective
• Use the platform to generate a series of robust experimental datasets for partner labs in the 2FAST consortium
• Explore the scale-up from microfluidics to continuous-flow production using parameters suggested by the optimization algorithm

We are looking for a highly motivated Master 2 or final year engineering student with experience in chemical synthesis laboratory work, nanoparticle characterization methods (UV-Vis, TEM, SAXS…), knowledge of programming (Python). Do not hesitate to apply even if you do not fully match this profile: we particularly value versatility, curiosity, and the ability to learn quickly in a multidisciplinary environment.