Advanced NMR Methods for Mixture Analysis

用于混合物分析的先进 NMR 方法

• 批准号: 2905913
• 金额: --
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2905913
• 关键词: Advanced; NMR; Methods; Mixture; Analysis

It is hard to overstate the importance of mixture analysis: it underpins progress in much of chemistry, biology and pharmacy. Paradoxically, the best method we have for obtaining information on chemical structure, NMR, struggles with all but the simplest mixtures. NMR spectra are relatively easily interpreted by experienced chemists, but only for pure compounds. However, NMR has great potential for efficient mixture analysis; it is both non-destructive and non-invasive, allowing the study of completely intact mixtures. The limited success enjoyed by NMR in mixture analysis to date stems not so much from its inherent limitations, as from failure to integrate experiment and data analysis optimally. When component spectra are partially overlapped, valuable information can be by analysing a single well resolved peak from each compound (univariate analysis) to provide e.g. relaxation or diffusion data. To obtain information from several, or all, peaks (multivariate analysis) one of the most powerful methods diffusion NMR. It works by separating components spectra by virtue of their different diffusion behaviour. It works well for simple mixtures with discrete diffusing coefficients, but a lot more work needs to be done to allow more complicated mixtures and systems with a distribution of diffusion coefficients (such as polymers). For very complicated systems with extensive signal overlap and/or low signal-to-noise ratio we need more information that diffusion can provide to separate the spectra. One way to substantially improve this chemical resolution is to harness the power of multivariate data analysis, and in particular multiway methods. If we extend the dimensionality of the data, by adding orthogonal sources of variation, we can use specific multiway methods to extract the true, unmixed, component spectra, thanks to the inherent uniqueness of multiway models1,2. These methods use all the available data, so instead of using individual signals, we fit all the spectra at the same time. This can allow us to study systems with very low concentrations3. To further enhance the spectral resolution, we can use high-resolution techniques such as HSQC, as was done in a study of the kinetics of protein fibril formation4. Recently we showed that it is possible to add a range of orthogonal dimensions, by designing NMR pulse sequences to produce suitable data5.In this studentship we will study mixtures using new NMR experiments. These include those that produce univariate, multivariate and multilinear data; novel complementary NMR pulse sequences and decomposition algorithms will be developed. References(1) Harshman, R. Foundations of the PARAFAC procedure. UCLA Working Papers in Phonetics. 1970, 16, 1.(2) Bro, R. PARAFAC. Tutorial and applications. Chemometrics Intell. Lab. Syst. 1997, 38 (2), 149.(3) Khajeh, M.; Botana, A.; Bernstein, M. A.; Nilsson, M.; Morris, G. A. Reaction Kinetics Studied Using Diffusion-Ordered Spectroscopy and Multiway Chemometrics. Anal. Chem. 2010, 82 (5), 2102.(4) Jensen, K. S.; Linse, S.; Nilsson, M.; Akke, M.; Malmendal, A. Revealing Well-Defined Soluble States during Amyloid Fibril Formation by Multilinear Analysis of NMR Diffusion Data. Journal of the American Chemical Society 2019, 141 (47), 18649.(5) Dal Poggetto, G.; Castanar, L.; Adams, R. W.; Morris, G. A.; Nilsson, M. Dissect and Divide: Putting NMR Spectra of Mixtures under the Knife. Journal of the American Chemical Society 2019, 141 (14), 5766.

混合物分析的重要性怎么强调都不为过：它是化学、生物学和药剂学许多领域进步的基础。矛盾的是，我们拥有的获取化学结构信息的最佳方法——核磁共振，除了最简单的混合物外，对所有物质都束手无策。有经验的化学家相对容易解释核磁共振光谱，但仅限于纯化合物。然而，核磁共振在高效混合物分析方面具有很大的潜力；它是非破坏性和非侵入性的，允许研究完全完整的混合物。迄今为止，核磁共振在混合分析方面取得的有限成功与其说是由于其固有的局限性，不如说是由于未能将实验和数据分析最佳地结合起来。当成分光谱部分重叠时，可以通过分析每个化合物的单个分辨良好的峰（单变量分析）来提供有价值的信息，例如弛豫或扩散数据。从几个或所有峰（多变量分析）中获取信息是最有效的方法之一。它的工作原理是利用不同的扩散行为来分离组分光谱。它适用于具有离散扩散系数的简单混合物，但要允许具有扩散系数分布的更复杂的混合物和系统（如聚合物），还需要做更多的工作。对于具有广泛信号重叠和/或低信噪比的非常复杂的系统，我们需要扩散可以提供的更多信息来分离光谱。有效提高化学分辨率的一种方法是利用多元数据分析的力量，特别是多路方法。如果我们扩展数据的维数，通过添加正交变异源，我们可以使用特定的多路方法提取真实的、未混合的成分光谱，这要归功于多路模型的固有唯一性1,2。这些方法使用所有可用的数据，所以我们不是使用单个信号，而是同时拟合所有光谱。这使我们能够研究浓度很低的体系。为了进一步提高光谱分辨率，我们可以使用高分辨率技术，如HSQC，就像在蛋白质纤维形成动力学的研究中所做的那样。最近我们表明，通过设计核磁共振脉冲序列来产生合适的数据，可以添加一系列正交维度。在本课程中，我们将使用新的核磁共振实验来研究混合物。这些包括那些产生单变量、多变量和多线性数据的数据；将开发新的互补核磁共振脉冲序列和分解算法。参考文献(1)Harshman， R. PARAFAC程序的基础。加州大学洛杉矶分校语音学工作论文。1970,16,1。(2)帕拉法克兄弟。教程和应用程序。化学计量学智能。实验室。系统1997,38(2),149。(3)卡杰，M.；Botana, a;伯恩斯坦，文学硕士；尼尔森,m;用扩散有序光谱和多路化学计量学研究反应动力学。分析的。化学，2010,32(5),2102。(4) Jensen, k.s.；林斯,美国;尼尔森,m;Akke m;通过核磁共振扩散数据的多线性分析揭示淀粉样蛋白纤维形成过程中明确的可溶性状态。化学学报，2019,41(7),18649。(5) Dal Poggetto， G.；Castanar l;亚当斯，r.w.；莫里斯，g.a.；解剖与分割：将混合物的核磁共振光谱置于刀下。化学学报，2019,41(14),5766。