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使用扩散排序光谱法和多元化学计量学研究反应动力学。

Reaction kinetics studied using diffusion-ordered spectroscopy and multiway chemometrics.

机构信息

School of Chemistry, University of Manchester, Oxford Road, Manchester M13 9PL, UK.

出版信息

Anal Chem. 2010 Mar 1;82(5):2102-8. doi: 10.1021/ac100110m.

DOI:10.1021/ac100110m
PMID:20131871
Abstract

Nuclear magnetic resonance (NMR) spectroscopy is frequently used in the monitoring of reaction kinetics, due to its nondestructive nature and to the wealth of chemical information that can be obtained. However, when spectra of different mixture components overlap, as is common, the information available is greatly reduced, sometimes to the point where the identification of individual chemical species is not possible. In such cases, the resolution of component spectra and their concentration timecourses can be greatly improved by recording DOSY (diffusion-ordered spectroscopy) data for each time point during the reaction. Adding this additional degree of freedom to the experimental data, allowing the signals of different species to be distinguished through their different rates of diffusion, makes the data trilinear and, therefore, susceptible to analysis by powerful multiway (here, more specifically multilinear) model-free decomposition methods such as PARAFAC (parallel factor analysis). This approach is shown to produce high quality data even for species with near-degenerate spectra. Another important limitation of NMR is its inherently low sensitivity. Here, we show that the combination of DOSY and PARAFAC is surprisingly robust with respect to input data with low signal-to-noise ratio. High quality component spectra and kinetic profiles are obtained from a data set in which the signal-to-noise ratios of the reaction components in the spectra for individual time points are below the detection level.

摘要

核磁共振(NMR)光谱学由于其非破坏性和可获得丰富的化学信息,经常用于监测反应动力学。然而,当不同混合物成分的光谱重叠时,通常会大大减少可用信息,有时甚至无法识别个别化学物质。在这种情况下,通过在反应过程中的每个时间点记录扩散有序光谱(DOSY)数据,可以大大提高组分光谱的分辨率及其浓度时程。通过将这个额外的自由度添加到实验数据中,允许通过不同的扩散速率来区分不同物种的信号,使得数据成为三线性的,因此容易受到强大的多向(这里更具体地说是多线性)无模型分解方法的分析,如 PARAFAC(平行因子分析)。即使对于光谱近简并的物质,这种方法也被证明可以产生高质量的数据。NMR 的另一个重要限制是其固有的低灵敏度。在这里,我们表明 DOSY 和 PARAFAC 的组合对于低信噪比的输入数据具有惊人的稳健性。从一组数据中获得了高质量的组分光谱和动力学曲线,其中单个时间点的光谱中反应组分的信噪比低于检测水平。

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