EXtended ACquisition Time (EXACT) NMR—A Case for ′Burst′ Non‐Uniform Sampling

Abstract

A strong case exists for the introduction of burst non-uniform sampling (NUS) in the direct dimension of NMR spectroscopy experiments. The resulting gaps in the NMR free induction decay can reduce the power demands of long experiments (by switching off broadband decoupling for example) and/or be used to introduce additional pulses (to refocus homonuclear coupling, for example). The final EXtended ACquisition Time (EXACT) spectra are accessed by algorithmic reconstruction of the missing data points and can provide higher resolution in the direct dimension than is achievable with existing non-NUS methods.

Compressed NMR: Combining compressive sampling and pure shift NMR techniques

“…Finally, we note that CS techniques can be applied to any dimension, including the detected one. [64][65][66] 1.3 | Putting it all together Figures 2 and 3 illustrate the importance of combining pure shift and CS techniques. In this case, an organometallic chemist was making an optical device component.…”

“…Without pure shift techniques, the CS framework cannot be used to reach the potential of the spectrometer, but without the CS framework, the time necessary to take full advantage of multidimensional pure shift spectra can become prohibitive. Finally, we note that CS techniques can be applied to any dimension, including the detected one …”

Pure shift 1H NMR: what is next?

“…Recently, a new approach named Extended Acquisition Time NMR was proposed where the missing data point periods are not excised but instead are algorithmically reconstructed after acquisition. [33] With this method, the full length of the FID is kept, avoiding artificial shortening of the FID, and so narrower linewidths are observed in the pure shift spectrum. In practice, the use of selective pulses longer than 15-20 ms is not recommended if spectra with good quality are desired.…”

An irregular sampler

“…The utility of irregular meshes for numerical integration (Figure ) has been shown to be enhanced using samples drawn from a quasi‐ or sub‐random sequence, leading to more rapid convergence. The properties of quasi‐random samples address the problem of large gaps between samples that motivated the development of Poisson Gap and Quantile sampling schemes.…”