Chemical reactions are the universal path to new molecules, and are of central importance in science. The description of reactions is largely obtained once they are completed, with a key role of nuclear magnetic resonance (NMR), t...
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Información proyecto UNMIX
Duración del proyecto: 73 meses
Fecha Inicio: 2023-12-15
Fecha Fin: 2030-01-31
Fecha límite de participación
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Descripción del proyecto
Chemical reactions are the universal path to new molecules, and are of central importance in science. The description of reactions is largely obtained once they are completed, with a key role of nuclear magnetic resonance (NMR), through the structural information found in the spectra of purified products. Structural information is, however, elusive for ongoing reactions. While time-resolved NMR provides kinetics for known compounds, compound identification from a reaction mixture is not generally feasible today. Such identification would be transformative for the discovery, understanding and optimisation of reactions.
The UNMIX project aims at providing unprecedented levels of structural and quantitative information from ongoing reactions in experimentally relevant conditions. Its central, novel concept is the unmixing of compound-specific data by time-resolved diffusion NMR in continuous flow.
Diffusion NMR is a method with the potential to separate the spectra of mixture components. Until recently, diffusion NMR was limited to reactions performed in an NMR tube, a setup that often fails to replicate experimental conditions of interest. In contrast, emerging continuous-flow approaches give real-time monitoring in relevant conditions.
In 2022, the PI has shown the possibility to obtain high-quality diffusion NMR data for flowing samples. This opens the path to the UNMIX project, and its specific aims to:
1) develop novel accurate, fast, sensitive and flow-compatible diffusion NMR methods;
2) develop an original and broadly applicable data-analysis workflow to yield compound-specific 1D/2D spectra and concentration time curves from ongoing reactions;
3) address challenging applications such as the identification of intermediates in multi-step batch reactions, and reaction discovery with an autonomous flow reactor.
We expect these general methods will unravel transformative information on reactions and have broad impact in (bio)chemical science and engineering.