Advanced NMR Methods for Mixture Analysis

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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.