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Fast time-resolved NMR with non-uniform sampling

Journal article
Authors D. Gołowicz
P. Kasprzak
Vladislav Orekhov
K. Kazimierczuk
Published in Progress in Nuclear Magnetic Resonance Spectroscopy
Volume 116
Pages 40-55
ISSN 0079-6565
Publication year 2020
Published at Department of Chemistry and Molecular Biology
Pages 40-55
Language en
Keywords Compressed sensing, MDD, Multi-dimensional decomposition, Non-stationary signals, Non-uniform sampling, Time-resolved NMR, Macromolecules, Nuclear magnetic resonance spectroscopy, Macromolecular structures, Multi dimensional, Multidimensional NMR, Nonstationary signals, Nonuniform sampling, Time resolved studies, Time-dependent process, Time-resolved, Signal sampling
Subject categories Spectroscopy


NMR spectroscopy is a versatile tool for studying time-dependent processes: chemical reactions, phase transitions or macromolecular structure changes. However, time-resolved NMR is usually based on the simplest among available techniques – one-dimensional spectra serving as “snapshots” of the studied process. One of the reasons is that multidimensional experiments are very time-expensive due to costly sampling of evolution time space. In this review we summarize efforts to alleviate the problem of limited applicability of multidimensional NMR in time-resolved studies. We focus on techniques based on sparse or non-uniform sampling (NUS), which lead to experimental time reduction by omitting a significant part of the data during measurement and reconstructing it mathematically, adopting certain assumptions about the spectrum. NUS spectra are faster to acquire than conventional ones and thus better suited to the role of “snapshots”, but still suffer from non-stationarity of the signal i.e. amplitude and frequency variations within a dataset. We discuss in detail how these instabilities affect the spectra, and what are the optimal ways of sampling the non-stationary FID signal. Finally, we discuss related areas of NMR where serial experiments are exploited and how they can benefit from the same NUS-based approaches. © 2019 Elsevier B.V.

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