CE07 - Chimie moléculaire

Machine learning approach to develop interfacial ionic liquid-based catalysts for low temperature CO2 reduction – LICORN

Submission summary

Efficient carbon dioxide conversion using green H2 coming from renewable sources can contribute to reduce CO2 emission in order to limit global warming. But finding efficient catalysts able to operate under mild conditions is critical. This project aims to rationally design a new generation of innovative supported catalysts for CO2 hydrogenation, combining supported metal nanoparticles and a task-specific ionic liquid, in order to achieve interfacial ionic liquid-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 that 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 done for the AI optimization will be tested and involved in long-term stability tests.

Project coordination

Romuald POTEAU (Institut National des Sciences Appliquées Toulouse)

The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.

Partner

LPCNO Institut National des Sciences Appliquées Toulouse
LCC LABORATOIRE DE CHIMIE DE COORDINATION
SOLVIONIC
LPCNO Institut National des Sciences Appliquées Toulouse

Help of the ANR 503,328 euros
Beginning and duration of the scientific project: December 2022 - 48 Months

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