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快速时间分辨的非均匀采样 NMR。

Fast time-resolved NMR with non-uniform sampling.

机构信息

Centre of New Technologies, University of Warsaw, Banacha 2C, Warsaw 02-097, Poland; Faculty of Chemistry, University of Warsaw, Pasteura 1, Warsaw 02-093, Poland.

Centre of New Technologies, University of Warsaw, Banacha 2C, Warsaw 02-097, Poland; Department of Mathematical Methods in Physics, Faculty of Physics, University of Warsaw, Pasteura 5, 02-093 Warsaw, Poland.

出版信息

Prog Nucl Magn Reson Spectrosc. 2020 Feb;116:40-55. doi: 10.1016/j.pnmrs.2019.09.003. Epub 2019 Sep 25.

Abstract

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.

摘要

NMR 光谱学是研究时变过程的通用工具:化学反应、相变或大分子结构变化。然而,时间分辨 NMR 通常基于可用技术中最简单的技术 - 一维谱作为研究过程的“快照”。原因之一是多维实验由于进化时间空间的昂贵采样而非常耗时。在这篇综述中,我们总结了减轻多维 NMR 在时间分辨研究中应用有限的问题的努力。我们专注于基于稀疏或非均匀采样 (NUS) 的技术,通过在测量过程中省略数据的重要部分并根据关于光谱的某些假设对其进行数学重建,从而减少实验时间。NUS 光谱比传统光谱更快地获取,因此更适合作为“快照”,但仍然受到信号非平稳性的影响,即在数据集内幅度和频率的变化。我们详细讨论了这些不稳定性如何影响光谱,以及对非平稳 FID 信号进行最佳采样的方法。最后,我们讨论了 NMR 中利用串行实验的相关领域,以及它们如何受益于相同的基于 NUS 的方法。

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