1. Context of the PhD

The proposed PhD thesis is part of the BiMAn project (Bottom-up Innovation for Magnetic Anisotropic Nanomaterials), which belongs to the national PEPR DIADEM program, aimed at developing rare-earth-free magnetic materials for sustainable energy and digital technologies. The project brings together chemists, physicists, engineers, and AI scientists to create high- performance, low-environmental-impact magnetic materials by designing nanoparticles with controlled anisotropy. Among the major scientific challenges addressed by BiMAn is the development of advanced synthesis strategies capable of producing iron-based nanoparticles with finely tuned shape and size, which in turn determine their magnetic properties.
This PhD project focuses on the development and implementation of microfluidic systems for the synthesis of anisotropic iron-rich nanoparticles. Microfluidics offers unprecedented control over reaction parameters, such as temperature gradients, flow rates, mixing, and residence time, and is particularly well-suited for reproducible and scalable nanoparticle synthesis. The use of such platforms will not only enable precise screening of synthesis conditions but will also generate high-quality datasets that can be used to train artificial intelligence (AI) models to optimize the production process. In particular, this work will serve as a central experimental platform to support AI-guided synthesis loops within the BiMAn project, feeding and validating models that predict nanoparticle properties and their corresponding SAXS (Small-Angle X-ray Scattering) profiles.

2. Objectives of the PhD

The PhD aims to develop a microfluidic approach for the synthesis of anisotropic iron-based nanoparticles, with a particular focus on nanorods and elongated structures. The student will design, fabricate, and operate microfluidic reactors to perform seed-mediated growth synthesis under tightly controlled conditions. These reactors will be used to systematically investigate the effects of key parameters including seed concentration, precursor composition, temperature, residence time, and ligands ratio on the growth and morphology of nanoparticles. The synthesized particles will be characterized using advanced techniques such as transmission electron microscopy (TEM) and in situ/ ex situ SAXS-WAXS.
A major goal of the PhD is to establish a robust experimental workflow capable of producing structured and reproducible datasets that will serve as input to AI models developed in parallel by collaborators. These models will be used to predict optimal synthesis conditions and simulate SAXS curves based on particle structure. The student will participate in the integration of the experimental system with real-time SAXS measurements and collaborate closely with AI researchers to test and validate the performance of predictive models. Ultimately, the work will contribute to the implementation of a closed-loop, AI-driven laboratory for the design of functional nanomaterials.

3. Working Program

During the first year, the student will focus on the design and fabrication of microfluidic platforms capable of executing complex synthesis sequences, including precursor mixing, thermal control, and multistep flow processes. These devices will be designed to handle air- sensitive organometallic precursors and will integrate multiple zones dedicated to pre-heating, nucleation, growth, serial dilution and quenching. The reproducibility and stability of the system will be validated using the synthesis of cobalt nanorods as a benchmark.
In the second year, the PhD work will shift toward extensive screening of synthesis parameters for the production of anisotropic nanoparticles. The student will systematically vary experimental conditions and recover samples for offline characterization using TEM, SAXS and WAXS. In parallel, the microfluidic system will be coupled with in situ SAXS analysis at synchrotron beamlines such as SWING (SOLEIL) or ID02 (ESRF). This coupling will allow real-time monitoring of particle growth and shape evolution, enabling dynamic feedback on reaction progress.
The third year of the PhD will be dedicated to the integration of the experimental system with AI tools developed by partners in the BiMAn project. The student will work closely with AI scientists to provide annotated experimental datasets and to evaluate the performance of predictive models. These models will be used to propose new synthesis conditions, which will be tested experimentally to assess their validity and efficiency. This final stage will aim to demonstrate a functional, AI-guided synthesis loop capable of autonomously identifying optimal conditions for producing anisotropic magnetic nanoparticles with targeted properties.
 

4. Profile of the Candidate

We are looking for a highly motivated candidate with a strong background in either physics, physical chemistry, materials science, or chemical engineering, holding a Master’s degree (Master 2 or engineering diploma). The ideal candidate will have experience or a strong interest in microfluidics, colloid chemistry, or nanoparticle synthesis, and should demonstrate a willingness to engage with multidisciplinary tools and concepts, including in situ characterization techniques and AI-based data analysis.
Good communication skills and the ability to work collaboratively with chemists, physicists, and data scientists are essential. A solid grounding in experimental work, curiosity, and adaptability will be key to the success of this project.

5. Hosting Laboratories

The PhD will be hosted primarily at the Laboratoire de Génie Chimique (LGC, UMR CNRS 5503) in Toulouse. LGC is a leading research center in chemical engineering with recognized expertise in microfluidics, process intensification, and nanomaterials synthesis under flow conditions. The student will be integrated into the "Colloids and complex Fluid" group, where they will have access to state-of-the-art microfabrication, synthesis, and characterization equipment.
The student will also collaborate closely with the Laboratoire de Physique et Chimie des Nano-Objets (LPCNO, UMR CNRS 5215), also based in Toulouse. LPCNO is a joint research unit between CNRS, INSA Toulouse, and Université Paul Sabatier. The lab has strong expertise in the synthesis, optical and magnetic characterization of nanomaterials, and in the development of machine learning models applied to nanoscience.
This interdisciplinary environment will provide the PhD student with a unique opportunity to work at the interface of chemistry, physics, and data science, and to contribute to the development of next-generation nanomaterials and self-driving laboratories.