Study of methodologies for calculating pKa values of amines based on a quantum chemistry/machine learning approaches
Direction Physico-chimie et Mécanique appliquées


Type de contrat
Stage
Début
Entre mars et juin 2025
Durée
6 mois
Région
Ile de France
Indemn / Rém
Oui

ref R175

IFP Energies nouvelles (IFPEN) est un organisme public de recherche, d’innovation et de formation dont la mission est de développer des technologies performantes, économiques, propres et durables dans les domaines de l’énergie, du transport et de l’environnement. 

IFPEN met à disposition de ses chercheurs un environnement de recherche stimulant, avec des équipements de laboratoire et des moyens de calcul très performants.

Dans le cadre de la mission d’intérêt général confiée par les pouvoirs publics, IFPEN concentre ses efforts sur :

  • l’apport de solutions aux défis sociétaux de l’énergie et du climat, en favorisant la transition vers une mobilité durable et l’émergence d’un mix énergétique plus diversifié ;
  • la création de richesse et d’emplois, en soutenant l’activité économique française et européenne et la compétitivité des filières industrielles associées.

Study of methodologies for calculating pKa values of amines based on a quantum chemistry/machine learning approaches

Advanced amine-based processes like DMXTM are a promising option for post-combustion CO2 capture, and they play an important role in achieving the targets on short-term CO2 emissions. These processes are based on a combination of amines solutions which absorbs CO2 from flue gas. The use of amines is a key aspect because of their reactivity with CO2 to form carbamates, their contribution to bicarbonate formation, and their regenerative properties.

The reactivity of amines in CO2 capture is closely linked to their basicity, characterized by the pKB (or pKA) value, which governs the protonation state of the amine in solution. For tertiary amines, pKA estimation is particularly important because it directly affects the rate of bicarbonate formation and the efficiency of the CO2 capture process. For that reason, accurately determining the pKA of these amines ensures optimal solvent formulation, enhancing both absorption and regeneration efficiency.

In this context, the objective of this internship is to study and evaluate different available methods for predicting the pKA constants of amines. The project will begin with a literature review of existing pKA estimation methods, including empirical correlations and quantum chemistry/MLP approaches, to identify the most suitable techniques for estimating the dissociation constants of amines. Following this, selected predictive models will be applied to estimate the pKA values of candidate amines, with adjustments based on experimental data where available. Additionally, the estimated pKA values will be integrated into process simulations to assess their impact on solvent performance in CO2 absorption and regeneration.

The student will collaborate with two researchers from the Thermodynamics and Molecular Simulation Department at IFPEN, utilizing both commercial and internal software tools. Additionally, the student will have the opportunity to explore and incorporate supplementary tools based on their research needs.

The outcomes of this internship are expected to provide valuable insights into the thermodynamics of amine-based solvents for CO2 capture, specifically in relation to IFPEN’s carbon capture technologies.

Desired Profile:

Internship to obtain Master 2 diploma

  • Technical Skills: physical chemistry, knowledge in theoretical chemistry and/or molecular modeling.
  • General Skills: dynamism, motivation for research and the valorization of results (writing scientific articles and reports), and the ability to work in a team.

Duration of Internship: 6 months
Start Date: Spring 2025

Administrative Information

Host Institution: IFPEN
Address: 1 & 4 Avenue de Bois Préau - 92500 Rueil-Malmaison
Department: Thermodynamics and Molecular Simulation
Internship Supervisors: José F. ROMERO YANES; Theodorus DE BRUIN