AI-assisted high-pressure microfluidics for CO2 capture, transport and utilization
Description of the PhD project
Achieving carbon neutrality by 2050 requires the rapid deployment of reliable carbon capture, storage and utilization (CCUS) technologies. A major limitation is the lack of experimental data acquired under controlled but industrially relevant conditions, especially for reactive, corrosive and multiphase fluids. This PhD project will develop a robust high-pressure microfluidic platform for the quantitative study and monitoring of CCUS processes. The device will be designed to operate up to 100 bar, between -25 deg C and 80 deg C, with dense or supercritical CO2 and highly corrosive fluids, including concentrated H2SO4 and HNO3 when required. It will be coupled to in situ analytical tools, in particular micro-Raman spectroscopy, optical imaging,pressure/flow/temperature control and machine-learning algorithms for data analysis and autonomous optimization.
Microfluidics is particularly well suited to this objective because it enables precise control of flow rate, pressure, temperature and composition, while generating large datasets on fast and localized phenomena such as reaction kinetics, mass transfer and precipitation. Three use cases will demonstrate the versatility and industrial relevance of the platform. First, the project will address corrosion risk during CO2 transport by producing and analysing microdroplets generated by reactions between SOx/NOx impurities, water and CO2. Raman spectra and machine-learning regression or classification models will be used to infer droplet composition and assess corrosion-relevant operating windows. Second, the platform will be used for online monitoring of solvent loading in CO2 capture processes. Absorption and desorption of CO2 in amine-based solvents, such as MEA, DEA and alternative formulations, will be studied as a function of pressure, temperature and solvent composition. Spectroscopic data will be used to calibrate process-following models and kinetic descriptions, with the objective of improving solvent selection and reducing regeneration energy. Third, the project will investigate controlled CaCO3 precipitation for CO2 valorization. Supersaturation, pH, temperature, mixing and residence time, additives will be tuned in microfluidic devices to control precipitate morphology, from platelets to rods, and to identify routes toward high-value materials such as fillers for polymers. The structure will be characterized using Xray and electronic microscopy.
The expected outcomes are a validated experimental platform, reproducible protocols for harsh CCUS conditions, interpretable datasets, machine-learning tools for process monitoring, and design rules connecting microfluidic measurements to industrial operating constraints.
Keywords
CO2 capture, utilization and storage ; high-pressure microfluidics ; dense and supercritical CO2 ; corrosive fluids ; in situ Raman spectroscopy ; amine solvents ; corrosion risk ; CaCO3 precipitation ; machine learning ; process monitoring.
Research Unit, UMR number and acronym
ESPCi - Chimie Biologie Innovation, UMR 8231, CBI ; MIE team (Materiaux Innovants pour l’Energie),
Description of the research Unit/subunit
CBI (Chimie Biologie Innovation, UMR 8231, ESPCI Paris-PSL/CNRS) develops research at the interface between chemistry, biology, physics, analytical sciences and soft matter. The MIE team (Materiaux Innovants pour l’Energie) focuses on complex fluids, microfluidics, interfacial transport and materials/processes for energy. It combines original experimental devices, quantitative imaging, spectroscopy, electrochemistry, rheology and modelling. The team offers a strong environment for a PhD project linking fundamental physics of transport and reactions with industrial problems in decarbonization and process monitoring.
Name of the supervisor
Annie Colin (annie.colin@espci.fr)
Name of the co-supervisor
Enric Santanach Carreras (enric.santanach-carreras@totalenergies.com)
3i Aspects of the proposal
The PhD student will be supervised by Annie Colin, HDR, with regular involvement of Enric Santanach Carreras from TotalEnergies as industrial co-supervisor. A weekly meeting with the academic supervisor will ensure close scientific follow-up, experimental planning and risk assessment. The PhD candidate will also spend part of the project in Pau (one third), as part of the joint TotalEnergies–ESPCI laboratory. The student will present results in MIE/CBI meetings and in dedicated steering meetings with milestones on device design, high-pressure operation, spectroscopy, machine learning and validation on the three use cases. A thesis committee will be set up according to PSL rules.
Intersectoriality
The project has a strong intersectoral dimension through its cofunding and co-supervision by TotalEnergies. The industrial partner will contribute relevant CCUS use cases, operating constraints, safety requirements, process questions and criteria for transfer from model microfluidic devices to industrial monitoring. The collaboration will help prioritize measurements that are both scientifically rigorous and practically useful : corrosion risk in CO2 transport, online monitoring of solvent loading, and CO2 valorization through controlled CaCO3 precipitation. The PhD student will be trained in academic research while being exposed to industrial R&D issues such as robustness, scale-up, quality of data, intellectual property, safety procedures and decision-oriented modelling.
International
The project will include the compulsory international secondment of at least one month required by PRISM. Two possible destinations are currently envisaged, and the final choice will be left to the PhD student, according to their scientific interests and the development of the project. A first option is a stay at the University of Amsterdam, in the laboratories of Prof. Noushine Shahidzadeh and Prof. Daniel Bonn. This secondment would focus on the CaCO₃ precipitation case study and would benefit from their expertise in crystallization, confined fluids and complex interfaces. A second option is a stay in Toronto in the laboratory of David Sinton, dedicated to CO₂ electroreduction. This would provide the student with access to additional experimental approaches and an international research environment. The secondment will be prepared once the ESPCI platform and the preliminary experimental protocols have been established. Both laboratories have agreed to host the student.
Interdisciplinarity
The proposal integrates microfluidics, soft-matter physics, physical chemistry, spectroscopy, chemical engineering, materials science and machine learning. The central scientific challenge is to connect flow, pressure, temperature, reactive chemistry, phase behaviour, precipitation and spectroscopic signatures in confined geometries. Raman and imaging data will be analysed using quantitative models and machine-learning tools, while the three use cases connect fundamental transport/reaction mechanisms to industrial decarbonization needs. This interdisciplinary integration is essential : neither process engineering, spectroscopy nor data science alone can provide a predictive framework for harsh CCUS conditions.
Expected profile of the candidate
The candidate should have a strong background in physics, physical chemistry, chemical engineering, materials science, microfluidics or process engineering. Experience with experimental work, spectroscopy, high-pressure devices, image analysis, data treatment or machine learning will be appreciated. The project requires careful experimental practice, interest in coupled transport and reaction phenomena, and willingness to work under strict safety protocols with corrosive fluids and pressurized systems. Programming skills in Python, Matlab, LabVIEW, COMSOL or equivalent tools would be useful. Good written and oral English is expected.
Important dates
Call for applications : from September 1st to October 31st 2026
Eligibility check results : November
3i Committee evaluation results : December
Interviews from the shortlisted candidates with the Selection Committee : January 2027
Start of the PhD : March 1st 2027