Innovating Photocatalysis with Sulphide Perovskite Materials

欧洲绿色协议的实施是当今欧洲联盟的最高优先事项和最大挑战之一。加速我们经济的脱碳需要经济高效地生产绿色氢气，大规模和长期（季节性）储氢解决方案，以及开发利用氢气作为化学原料的新方法。PHOTOCAT3.0将通过开发下一代硫化物钙钛矿光催化剂来解决这些挑战，这种光催化剂可以有效地产生绿色氢气，以氨的形式储存氢气，并将其用于从CO2合成化学品。对于光催化剂的设计，该项目从自然界的方法中获得灵感，用含金属硫簇的酶（氢化酶，固氮酶，一氧化碳和甲酸酯酶）处理一些相同的化学转化，另一方面，从高性能光伏材料，如铜铟镓硫化物和硒化物和卤化铅钙钛矿。PHOTOCAT3.0的目标是：i）开创低温原子层沉积路线的发展，以获得高质量的高度限定的硫化物钙钛矿光催化剂，ii）通过先进的表征技术，建立对这类新兴材料的光电和表面催化性能的前所未有的理解，iii）对它们在具有巨大经济和环境重要性的小分子的光催化转化中的性能进行基准测试，以及iv）加速材料发现使用贝叶斯优化预测最佳催化性能从最小的一组实验和样品。这种方法将产生迫切需要的光催化系统的效率突破。

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.