Nanocatalysts for photostimulated green hydrogen production: molecular design and advanced characterization assisted by machine learning methods
From 13.10.2021 till 31.12.2023
Grant holder: Alexander Soldatov
Responsible: Aram Bugaev
Members: Ilia Pankin, Vera Butova, Alexander Guda, Andrei Tereshchenko, Sergey Guda, Oleg Usoltsev, Alina Skorynina, Elizaveta Kozyr, Bulgakov Aleksei
External members: University of Turin
Hydrogen economy is an attractive green energy concept due to the possibility for hydrogen production from different primary energy sources with net zero CO2 emission and the lack of greenhouse gas emission. Currently, the main technologies for hydrogen production involve fossil fuels. An attractive alternative is the photocatalytic water splitting or organics photoreforming on nanomaterials to generate hydrogen by employing solar energy with low environmental impact. Although great efforts have been devoted to developing new materials with high photoactivity, the most active catalysts are still based on expensive noble metal nanoparticles (NPs) supported on semiconducting oxides (mainly TiO2). The main objective of the project is the development of a strategy for the minimization of noble metals. The rational design of noble-metal free photocatalyst requires a detailed understanding of the structure-activity relationship, that enables improving its catalytic performance and avoid fast degradation. To advance in solving this problem, a multi-disciplinary and multi-technique approach is suggested combining the expertise of team members and physics, chemistry, computer modelling and data science. The first task is to synthesize the active nanostructured photocatalysts with welldefined structural properties starting from noble metals and moving towards earth-abundant metals and complement the synthetic results by quantum-chemistry modelling. At the next stage, a multi-technique operando laboratory and synchrotron characterization will provide the important structural properties of the photocatalysts under working conditions. Finally, machine learning and DFT-assisted in-depth analysis of operando data will be applied to establish structure-reactivity relationships and provide the routs for rational design of efficient photocatalytic systems for hydrogen production. Important to note that long-standing collaboration between Italian and Russian partners resulted in successful solving of important and industrially relevant problems in catalysis, which ensures efficient employment of their complementary expertise within the project timescale.