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Theory and Methodology for Nuclear Spins Diffusing in Porous Media

  • Full or part time
  • Application Deadline
    Monday, February 24, 2020
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Supervisor: Giuseppe Pileio
Co-supervisor Marcel Utz

Project description

Applications are invited for a PhD Studentship in nuclear magnetic resonance (NMR) to work on a project aimed at developing new methodologies to enable the non-invasive structural characterisation of industrially and medically relevant porous media such as battery electrodes and 3D-printed scaffolds for tissue regeneration. The project is conducted in the research group of Dr. Giuseppe Pileio and is funded by The Leverhulme Trust.

Porous media are ubiquitous in nature and largely used in industry: bones, brain matter, supercapacitors, battery electrodes, etc., all fall in this category. The ability to measure structural parameters such as porosity, pore-size distribution and, in particular, tortuosity is fundamental for the understanding of such materials and the design of improved ones. Diffusion-NMR, the technique at the core of this project, has unique advantages in the study of porous media but yet several applications are limited by the finite lifetime of nuclear spin states. This limitation is exacerbated in porous media, where spin relaxation is accelerated by local variations of the magnetic fields on a microscopic scale.

In this project we propose to overcome these limitations by combining the time enhancement available through the use of singlet NMR methodology with field-cycling NMR methods. We also seek to develop a theory and simulation framework to predict nuclear spin relaxation rates in porous media.
The candidate will be part of a team which includes Giuseppe Pileio as project coordinator, Prof. Marcel Utz as co-investigator, and two research fellows. The project is developed in partnership with Bruker and the scaffolds will be provided by a research group at the QUT in Brisbane.

The PhD student will have an active role in developing the simulation framework described in the project and in complementing calculations with NMR experiments run in conjunction with the team. Simulations include finite element simulations of local magnetic fields in porous media, Monte Carlo simulations of diffusion in beads packing and spin dynamics simulations of NMR pulse sequences.

The University of Southampton ranks in the top 1% of world universities and among the 15 UK’s top research-intensive universities. The Magnetic Resonance Section offers a huge expertise that spans from theory of spin dynamics to NMR methodology in solid and liquid state, microfluidics NMR, protein NMR in liquid and solid state and NMR at cryogenic temperatures. We offer a variety of facilities including a 300MHz, two 400MHz, a 500MHz, three 600MHz and a 700MHz NMR magnets with equipment that allows solid-state, liquid state and micro-imaging applications. The proposed start date is 2nd of March 2020 but later dates will be considered.

Entry Requirements
A good undergraduate degree in Chemistry, Physics or allied subjects/related disciplines (at least a UK 2:1 honour degree, or its international equivalent).

Closing date: applications should be received no later than 24 February 2020 for standard admissions, but later applications may be considered.

Funding: full tuition for EU/UK Students plus an enhanced stipend of £15,009 tax-free per annum for 3 years.

How To Apply

Applications should be made online at (https://www.southampton.ac.uk/courses/how-to-apply/postgraduate-applications.page) selecting “Research” under “Programme Type”, entering “Chemistry” under “Search Text”.

Applications should include:
Curriculum Vitae
Two reference letters
Degree Transcripts to date

For further information on how to apply please contact:

How good is research at University of Southampton in Chemistry?

FTE Category A staff submitted: 44.80

Research output data provided by the Research Excellence Framework (REF)

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