Innovating Photocatalysis with Sulphide Perovskite Materials
Innovating Photocatalysis with Sulphide Perovskite Materials
批准号:
EP/Z000343/1
负责人:
Ludmilla Steier
金额:
$220.57万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2024
资助国家:
英国
项目状态:
未结题
起止时间:
2024 至 --
中文摘要
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英文摘要
The implementation of the European Green Deal is one of the highest priorities and biggest challenges of theEuropean Union today. Accelerating the decarbonisation of our economy requires the cost-effective generationof green hydrogen, large-scale and long-term (seasonal) hydrogen storage solutions and the development ofnew ways to utilise hydrogen as a chemical feedstock. PHOTOCAT3.0 will address these challenges bypioneering the development of next-generation sulphide perovskite photocatalysts that efficiently producegreen hydrogen, store it in form of ammonia and utilise it in the synthesis of chemicals from CO2These next-gen photocatalysts will excel in light-harvesting across the visible light spectrum as well as in theselective breaking and making of chemical bonds targeting high-value products. For the design of thephotocatalysts, this project takes inspiration from nature's approach to tackling some of the same chemicaltransformations with metallo-sulphur cluster-containing enzymes (hydrogenase, nitrogenase and carbonmonoxide and formate dehydrogenases), and, on the other hand, from high-performant photovoltaic materialssuch as copper indium gallium sulphides and selenides and lead halide perovskites. The objectives ofPHOTOCAT3.0 are i) to pioneer the development of low-temperature atomic layer deposition routes to highqualityhighly defined sulphide perovskite photocatalysts, ii) to establish an unprecedented understanding ofoptoelectronic and surface catalytic properties of this emerging class of materials through advancedcharacterisation techniques, iii) to benchmark their performance in photocatalytic transformations of smallmolecules with enormous economic and environmental importance and iv) to accelerate material discoveryusing Bayesian optimisation to predict optimum catalytic performance from a minimum set of experimentsand samples. This approach will produce the urgently needed efficiency breakthroughs of photocatalyticsystems.
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