Characterization of Electrical Properties of Electrolytes: Combining AI-Powered Microfluidics with Thermodynamics

The global energy transition towards the electrification of transportation has triggered an unprecedented demand in battery production, leading to a critical need for the sustainable recovery of strategic metals such as lithium or cobalt. While hydrometallurgy is a highly effective recycling pathway, it remains resource-intensive and environmentally challenging due to high toxic reagent consumption. Optimizing these processes requires a deep understanding of the solvents involved, such as highly concentrated aqueous or non-aqueous electrolytes.
Accurate modeling of these systems relies specifically on the knowledge of electrical conductivity, which is a measure of ion dissociation along with dielectric permittivity, which governs electrostatic interactions and ion solvation. Traditional "trial-and-error" experimental methods are no longer sufficient to explore the vast chemical space of recycling streams. To overcome this, "AI-Powered Microfluidics" - the synergy between microscale fluidics and Artificial Intelligence - offers a powerful high-throughput platform to generate the high-quality experimental data needed for advanced thermodynamic modeling. This PhD project aims to bridge the gap between experimental data high throughput generation and predictive modeling by measuring the conductivity and permittivity of diverse electrolytes. The research will be structured into four key phases: (i) the design, fabrication, and validation of innovative microfluidic chips; (ii) the coupling of these microchips with in situ analytical techniques; (iii) the AI-optimized acquisition of experimental data; and (iv) the refinement of thermodynamic models. This thesis will specifically contribute to the development of the predictive ePPC-SAFT equation of state (in coordination with another PhD project) enabling an explicit description of ionic solvation and complexation. Furthermore, it offers the opportunity to work at the interface of chemical engineering, thermodynamics, and data science, to address challenges in the circular economy.

Keywords: Intelligent microfluidics, Strategic metal recovery, Thermodynamic modeling

• Academic supervisor    Dr Samuel MARRE, samuel.marre@icmcb.cnrs.fr, ICMCB, ORCID : 0000-0001-8889-187X
• Doctoral School    ED 040 EDSC, Université de Bordeaux 
• IFPEN supervisor    Dr Eric LÉCOLIER, ORCID : 0000-0003-4506-3550
• PhD location    ICMCB (Bordeaux), and IFP Energies nouvelles (Rueil-Malmaison), France
• Duration and start date    3 years, starting in the fourth quarter 2026 (Novembre 2)
• Employer    IFPEN
• Academic requirements    University Master degree in physical chemistry, physics, chemical engineering
• Language requirements    English level B2 (CEFR), Willingness to learn French
• Other requirements    Rigorous scientific attitude and collaborative spirit

To apply, please send your cover letter and CV to the IFPEN supervisor indicated here below.