Efficient CO2 conversion using green H2 coming from renewable sources can contribute to
reduce CO2 emission in order to limit global warming. However, finding efficient catalysts
able to operate under mild conditions is critical. The LICORN project is financed by the
ANR and aims at rationally designing a new generation of innovative supported catalysts
for CO2 hydrogenation, combining supported metal nanoparticles and a task-specific ionic
liquid (IL), in order to achieve interfacial IL-based catalysis for low temperature thermal
CO2 reduction. Ionic liquids specifically optimized for this purpose will be designed with a
supervised machine learning (ML) framework, which will link relevant theoretical and
experimental descriptors with experimental catalytic activity and product selectivity. The
catalysts resulting from ML as well as the most promising catalysts from the screening
will be tested and involved in long-term stability tests.
The recruited PhD will work on the preparation of supported catalyst by conventional as
well as by “chimie douce”, their characterization by several techniques (TEM, XPS, XRD,
TP-methods,…) and their use in the CO2 reduction reaction. The obtained results will be
used for training the ML algorithm. In a second step the best catalysts resulting from the
screening phase of ML will be prepared and tested.
The thesis work is part of a collaborative ANR project involving an industrial partner. The
project involves another doctoral student (theoretical chemistry & ML) and one post-
doctoral student (organic chemistry and ILs), all located in Toulouse (LCC, LPCNO and
Solvionic).
Interested candidates should send a CV, a letter of motivation, at least one
recommendation letter, and the names of 1-2 references to philippe.serp@ensiacet.fr and
ksoulant@insa-toulouse.fr.
Experience acquired
This project will offer to the PhD student the opportunity to acquire a solid experience in
(i) catalyst preparation and characterization, (ii) heterogeneous catalysis, (iii)
nanoparticles synthesis by colloidal methods. Furthermore, knowledge in organic
chemistry and machine learning will be acquired through the strong interactions of the
hired PhD with the PhD working in theoretical chemistry & ML and the post-doc working
on IL.
Application
The ideal candidate should have solid background in inorganic chemistry or materials
science, as well as experience in heterogeneous catalysis.
Starting date: 1st October 2023
Deadline for application: July 1st, 2023.
Employer: CNRS.
Gross salary: 2135 € per month (net salary: 1777 €).