About the Project
Mixture analysis is at the heart of science. Almost everything around us is a mixture: the chemical reactions we perform in the lab, the drink with have with our dinner, the river water we use to irrigate our crops, the drugs we use to treat cancer, the breast milk that feeds us as infants; the list is endless. Clearly, there is a great need for efficient methods for analysing complex mixtures.
NMR (Nuclear Magnetic Resonance) is arguably the most powerful analytical method for providing detailed chemical information. It can provide comprehensive information on molecular structure, dynamics and interactions, but only if the signals of individual chemical sites are resolved. In general, NMR still struggles to distinguish the signals from different mixture components, although much progress has been made. One of the most powerful NMR methods for the study of intact mixtures is diffusion-ordered spectroscopy (DOSY1-3), in which the different diffusion characteristics of mixture components are used to pull their spectra apart. However, for many mixtures DOSY fails to separate the component signals, either because of signal overlap or because the diffusion behaviour of the different components is too similar. Ways forward include increasing spectral resolution, e.g. using pure shift NMR methods,4,5 and exploiting differences in other NMR properties such as relaxation6 to allow advanced multiway statistical algorithms (e.g. PARAFAC7,8) to distinguish between signals.
In this project we will develop new NMR experiments for the analysis of complex mixtures, and in parallel we will develop the new algorithms, data processing strategies and accompanying software to make optimum use of the data produced. The results will be made freely available to other NMR users; previous experiments developed in our lab are now available for all modern NMR spectrometers, and both they and our processing software are used by scientists all over the world.
The work anticipated spans a range of disciplines including chemistry, pharmacy, medicine, physics, statistics/chemometrics, and programming. It will provide training in a range of research methods, allowing the student to acquire a broad set of transferable skills including practical NMR spectroscopy, spin physics, statistical data analysis, software production and scientific programming.
Contacts for further information
For enquiries about admission, qualifications etc. please email [email protected].
For enquiries about the project please email [email protected].
Applicants should have, or be about to obtain a good first class or 2:i honours degree in a relevant subject (or the overseas equivalent). Applications are particularly welcome from students with experience in NMR spectroscopy and/or instrumental methods.
(2) Morris, G. A. In Encyclopedia of Magnetic Resonance; John Wiley & Sons, Ltd, 2009.
(3) Castanar, L.; Dal Poggetto, G.; Colbourne, A. A.; Morris, G. A.; Nilsson, M. The GNAT: A new tool for processing NMR data. Magn. Reson. Chem. 2018, 56 (6), 546.
(4) Foroozandeh, M.; Castanar, L.; Martins, L. G.; Sinnaeve, D.; Dal Poggetto, G.; Tormena, C. F.; Adams, R. W.; Morris, G. A.; Nilsson, M. Ultrahigh-Resolution Diffusion-Ordered Spectroscopy. Angew. Chem. Int. Ed. 2016, 55 (50), 15579.
(5) Aguilar, J. A.; Faulkner, S.; Nilsson, M.; Morris, G. A. Pure Shift H-1 NMR: A Resolution of the Resolution Problem? Angew. Chem. Int. Ed. 2010, 49 (23), 3901.
(6) Dal Poggetto, G.; Castanar, L.; Adams, R. W.; Morris, G. A.; Nilsson, M. Relaxation-encoded NMR experiments for mixture analysis: REST and beer. Chem. Commun. 2017, 53 (54), 7461.
(7) Bjorneras, J.; Botana, A.; Morris, G. A.; Nilsson, M. Resolving complex mixtures: trilinear diffusion data. J. Biomol. NMR 2014, 58 (4), 251.
(8) Bro, R. PARAFAC. Tutorial and applications. Chemometrics Intell. Lab. Syst. 1997, 38 (2), 149.
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