Discovery of efficient Enzyme-like Metal Organic frameworks to activate biomethane at low temperature

The main objective in DEMO is to provide high-level hands-on, computational and transferable skill training to 13 Doctoral Candidates (DC) through a Joint Doctorate Program and to create a new generation of experts in hybrid catalysis. DEMO uses light (C1-C4) alkanes as example to study the conversion of a sustainable molecule (biomethane) into a relevant chemical in industry (methanol). This project combines 9 world-class research groups, experts in chemical engineering, organic chemistry, catalysis, modelling and spectroscopy from 7 countries. The 9 partners in academic (3), research centre (3), SME (2) and industrial (1) fields will provide recruited DCs with unique perspectives, preparing DCs for their personal career in research with specific skillsets. DEMO will integrate machine learning, organic chemistry, ab initio modelling, high-throughput and reactor engineering and in situ spectroscopy to discover enzyme-like species in Metal Organic Frameworks (MOFs). Specifically, DEMO will follow an interconnected strategy to discover optimal catalyst candidates: a) virtually generate a dataset with active species in MOFs and screen via Machine Learning; b) test the dataset value of a large sample dataset via experimental high-throughput engineering and modelling; c) understand testing outputs through in situ spectroscopy, titration kinetics and modelling; d) optimise protocols for synthetic materials towards biological analogies and engineer reaction conditions to search solvation phenomena. This way, DEMO expects to have a broad impact on the scientific community, EU industry and society by providing high-quality training in hybrid catalysis.