Periodic Reporting for period 1 - CHEMREACTIONSCAN (Scanner for novel chemical reactions)
Periodo di rendicontazione: 2017-11-01 al 2019-04-30
Funded by the ERC-Starting Grant COMBIPATTERNING, we have developed a novel nanolayer-based synthesis based on patterning different materials with laser radiation in form of a pan-cake spots in array format. Our synthesis robot can do it for many different materials, for >20.000 spots per glass slide, in exactly defined stoichiometries, and for <200 € costs. The robot uses short laser pulses to transfer <1ng “punched-out” material per spot from a donor foil to a synthesis slide. Then, the reactants embedded in the nano-thin polymer pan-cake spots can mix with each other and thus undergo chemical reactions by heating the synthesis slide. We believe that the nanolayer-based synthesis developed by us might be a basis for future chemistry stations linking scientists and accelerating their studies to screen for novel chemical reactions in a high throughput manner. Instead of studying the interaction between reactants in a single test glass, the chemists will just spin-coat their educts on microscope slides and supply them together with a file for desired reactant combinations to the server - chemical reaction scanner. In the scope of the ERC-POC Grant CHEMRACTIONSCAN, we investigated the innovation potential of nanolayer-based synthesis. The focus of the project was on studying technical and chemical upscaling of the nanolayer synthesis method as well the possibility of the high-throughput detection of the reaction products in high-density array format. The nanolayer-synthesis was successfully tested for a variety of chemical reactions such as solid phase peptoid synthesis and the synthesis of medically relevant fluorescent small compounds from the non-fluorescent components. The screening for the reactions’ product in high-density array format was demonstrated. According to the plan of activities, the team was granted with additional funding (Helmholtz Enterprise) to perform the product development phase. The project team initiated cooperation with industry partners in the field of AI to design optimum molecular libraries as well the methods of their efficient screening.