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New NMR methods for mixture analysis

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  • Full or part time
    Dr M Nilsson
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

Applications are invited from students with independent funding for PhD research in the development and application of novel methods in NMR spectroscopy.

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 cure our sick, the breast milk that feeds us as infants; the list is endless. Clearly, there is a strong need for efficient methods to analyse 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 struggles to distinguish the signals from different mixture components, but some progress has been made. One of the most potent NMR methods for the study of intact mixtures is diffusion-ordered spectroscopy (DOSY1,2), 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,3,4 and exploiting differences in other NMR properties such as relaxation5, using advanced multiway statistical algorithms (e.g. PARAFAC6,7) to distinguish between signals.

In this project we will develop new NMR experiments for mixture analysis, tailored for use with multiway statistical analysis. In parallel we will develop new algorithms, data processing strategies and accompanying software.

This ground-breaking project spans a range of disciplines including chemistry, physics, statistics/chemometrics, and programming. It will provide extensive training in a range of research methods, allowing the student to acquire a broad set of skills including practical NMR spectroscopy, spin physics, statistical data analysis, software production and scientific programming.

https://www.research.manchester.ac.uk/portal/mathias.nilsson.html
http://nmr.chemistry.manchester.ac.uk/

Qualifications
Applicants should have or expect to obtain a good 1st or II(i) honours degree (or an equivalent degree) in a relevant subject. Applications are particularly welcome from students with experience in NMR spectroscopy and/or instrumental methods.

Contacts for further information
For enquiries about admission, qualifications etc. please email [email protected]

For enquiries about the project please email [email protected]

Funding Notes

Applications are invited from self-funded students. For UK/EU tuition fees are £8,500 and International are £24,500 for 2018/19 academic year.

References

(1) Nilsson, M. The DOSY Toolbox: A new tool for processing PFG NMR diffusion data. J. Magn. Reson. 2009, 200 (2), 296.

(2) Morris, G. A. In Encyclopedia of Magnetic Resonance; John Wiley & Sons, Ltd, 2009.

(3) 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. Edit. 2016, 55 (50), 15579.

(4) Aguilar, J. A.; Faulkner, S.; Nilsson, M.; Morris, G. A. Pure Shift H-1 NMR: A Resolution of the Resolution Problem? Angew. Chem.-Int. Edit. 2010, 49 (23), 3901.

(5) 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.

(6) Björnerås, J.; Botana, A.; Morris, G. A.; Nilsson, M. Resolving complex mixtures: trilinear diffusion data. J. Biomol. NMR 2014, 58 (4), 251.

(7) Bro, R. PARAFAC. Tutorial and applications. Chemometr. Intell. Lab. Syst. 1997, 38 (2), 149.

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