Objective
Motivated by the adverse environmental and societal effects of fossil resources (global warming, pollution, loss of habitat due to mining, health issues), efforts to defossilize the energy, mobility and chemical industries have sought alternatives based on, among others, solar power. Solar-to-X technologies have therefore emerged as promising, since they integrate the energy and chemical conversion steps within a single device, thereby being in principle able to reach high efficiencies, while also having the potential to be deployed in a decentralized manner. Yet, developing such technologies and designing pertinent devices is challenging due to the complex, multiscale nature of the phenomena underpinning their function. Thus motivated, PREDICT aims to elucidate fundamental mechanisms underlying the performance of photo(electro)chemical (PEC) CO2 reduction devices using advanced computational materials science and multiscale modeling techniques. By advancing quantum chemistry approaches for calculating excited state structures and dynamics, as well as bridging electronic, atomistic, mesoscopic, and macroscopic scales, this project will deliver a transferable theoretical and computational framework for the simulation of PEC devices, such as artificial leaves and beyond. The PREDICT modeling framework will be demonstrated via theoretical explorations of novel materials and processes for PEC CO2 reduction, enabling the fundamental understanding of phenomena crucial to the performance of PEC devices, and providing guidelines for optimizing the design to achieve superior performance. The framework and models will be further validated through collaborations with successful experimental projects from Area 1 of the call. Expected outcomes encompass improved device architectures, optimized materials properties, as well as a holistic and transferable multiscale modeling framework, driving the next generation of high-efficiency, integrated solar-to-X devices.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
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Keywords
Programme(s)
- HORIZON.3.1 - The European Innovation Council (EIC) Main Programme
Call for proposal
(opens in new window) HORIZON-EIC-2024-PATHFINDERCHALLENGES-01
See other projects for this callFunding Scheme
HORIZON-EIC - HORIZON EIC GrantsCoordinator
OX1 2JD Oxford
United Kingdom