We are looking for postdoctoral fellows and PhD candidates to carry on the quest to improve the sensitivity and ease of use of magnetic resonance spectroscopy. Besides optimal control, we will exploit artificial intelligence and machine learning approaches to streamline the interpretation of spectroscopic data. For details about positions, see the advertisement leaflet under this link.
On this site we summarize our experience with application of optimal control in solid state NMR studies of protein samples.
Currently, we have developed tm-SPICE pulse sequences for the traditional (uni-axial, \(I_x \rightarrow S_x\)) magnetization transfer, and TROP pulse sequences performing transverse mixing (simultaneous \(I_x \rightarrow S_x\) and \(I_y \rightarrow S_y\)) that allow to systematically enhance sensitivity by \(\sqrt{2}\) for each indirect dimension (both for hetero- and homo-nuclear correlations) .
In case of tm-SPICE N-CA and N-CO magnetization transfers, we obtain 1.5-times more signal compared to carefully optimized ramp-CP experiments performed in the range of MAS frequencies 13-20 kHz. Our tm-SPICE shapes can be adapted to any MAS frequency in this range. [3]
In case of TROP transfers, N-dimensional experiments are done in the echo/anti-echo manner using dedicated pulse programs [4]. Experiments show that it is possible to obtain sensitivity gains that go beyond the systematic \(\sqrt{2}\) per indirect dimension due to compensation of RF field inhomogeneity.
Homonuclear TROP transfers between CA and CO carbons are reported in our most recent publication [5].