SOURCE-LINKED INTELLIGENCE

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

CORDIS · observation · Publication date unknown

rch 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. Machine Learning,

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recordType

award

status

SIGNED

region

EU

value

3248503.2

unit

EUR