Project description
Fast-tracking the photolysis process to optimise hydrogen yield
Green hydrogen is a promising eco-friendly substitute for fossil fuels in many industries. It can be produced through photocatalytic water splitting using various inorganic, noble or nano-covalent organic frameworks as (photo)catalysts. However, to optimise hydrogen yield, numerous factors must be considered, such as co-catalyst selection, catalyst-to-co-catalyst ratios, and suitable pH and viscosity levels. Manually testing multiple combinations of parameters is time-consuming, while self-driving laboratories can leverage advanced robotics, computational power and AI to achieve results much faster. Supported by the Marie Skłodowska-Curie Actions programme, the SDL-MFHYD project aims to effectively accelerate the photolysis process beyond current capabilities. To do so, it will use a multi-fidelity Bayesian optimisation algorithm that reduces the frequency of critical time-intensive steps in photocatalysis.
Objective
The fossil fuel sector is projected to emit 200 million tons of CO2 equivalent by 2050. Hydrogen is emerging as a crucial energy carrier, essential for achieving net-zero emissions (NZE) by 2050. The European Commission is actively funding initiatives for decarbonization and green hydrogen production. Green hydrogen can primarily be produced through photocatalytic water splitting, involving either proton reduction or overall water oxidation. While several photocatalysts, predominantly inorganic or noble materials have been reported, recent advances in environmentally friendly nano-covalent organic frameworks (Nano-COFs) catalysts offer tunability and significant synthetic diversity. However, photocatalysts alone are insufficient for substantial hydrogen production. Multiple components must be integrated, such as co-catalyst selection, catalyst-to-co-catalyst ratios, and physicochemical parameters like pH and viscosity, to optimize hydrogen yield. The complexity of optimizing these parameters is challenging for manual testing, especially as the search space expands exponentially. Self-driving laboratories (SDLs) are poised to revolutionize this field by leveraging advancements in robotics, computational power, and artificial intelligence (AI). SDLs can achieve scientific objectives hundreds of times faster than traditional automation, integrating hardware for experiment execution and software for data analysis and subsequent experiment design. Despite these advancements, the time-intensive steps of photolysis and gas analysis remain bottlenecks. This proposal addresses the challenge of accelerating the photolysis process beyond current SDL capabilities. By employing a multi-fidelity Bayesian optimization algorithm, I aim to reduce the frequency of crucial yet time-intensive steps in photocatalysis. This novel approach, untested in real photolysis experiments, has the potential to extend broadly to other areas of electrochemistry, including CO2/N2 electrolysis.
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: The European Science Vocabulary.
This project's classification has been human-validated.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
This project's classification has been human-validated.
- natural sciences chemical sciences catalysis photocatalysis
- natural sciences chemical sciences electrochemistry electrolysis
- engineering and technology electrical engineering, electronic engineering, information engineering electronic engineering robotics
- natural sciences computer and information sciences artificial intelligence machine learning
- engineering and technology environmental engineering energy and fuels energy conversion
Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
MAIN PROGRAMME
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-MSCA-2024-PF-01
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
L69 7ZX LIVERPOOL
United Kingdom
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.