Nuclear magnetic resonance (NMR) spectroscopy, is one of the most versatile methods of chemical analysis. Traditional NMR spectroscopy is based on the rich chemical information contained in spectra. The Laplace NMR (LNMR), which comprises diffusion and relaxation measurements, reveals detailed information on the movement of molecules, making it possible to observe the different kinds of physical or chemical environments of the molecules, even if they would not be visible in the spectra. As is the case with traditional NMR spectroscopy, LNMR resolution and information content can be improved through the use of multidimensional experiments. However, these experiments are very time-consuming, and consequently, they are not suitable for the study of fast processes.
This ERC project focuses on the development of new class of NMR experiments, called ultrafast Laplace NMR (UF-LNMR). The method is based on the spatial coding of multidimensional information, making it possible to read it with one measurement. The method shortens the experiment time by one to three orders of magnitude as compared to the conventional method, offering unprecedented opportunity to study fast processes in real time. Furthermore, it enables boosting the sensitivity by several orders of magnitude by using nuclear spin hyperpolarization, which allows investigation of low-concentration samples.
The method offers groundbreaking possibilities for chemical, biochemical, geological, archaeological, and medical analysis. It allows, for example, the study of the dynamics of cancer cell metabolism in real time. The method can also reveal completely new information on the structure and characteristics of ionic liquids, gels, polymers, catalysts, and proteins, and it can be utilized in the development of biosensors.
The method is also applicable to low-field NMR sensors which are much less expensive than high field devices, and are easy to move. Their geometry also allows the study of samples of all sizes. UF-LNMR combined with the use of hyperpolarized substances increases the sensitivity and resolution of low-field devices to previously unseen levels. This means significant possibilities for low-cost mobile chemical and medical analysis.
