Tag Archives: PhD opportunity

Diving into three-dimensional fluids

My research group has been working for some years on methods that allow straightforward comparison of large datasets [see ‘Recognizing strain’ on October 28th 2015].  Our original motivation was to compare maps of predicted strain over the surface of engineering structures with maps of measurements.  We have used these comparison methods to validate predictions produced by computational models [see ‘Million to one’ on November 21st 2018] and to identify and track changes in the condition of engineering structures [see ‘Out of the valley of death into a hype cycle’ on February 24th 2021].  Recently, we have extended this second application to tracking changes in the environment including the occurance of El Niño events [see ‘From strain measurements to assessing El Niño events’ on March 17th, 2021].  Now, we are hoping to extend this research into fluid mechanics by using our techniques to compare flow patterns.  We have had some success in exploring the use of methods to optimise the design of the mesh of elements used in computational fluid dynamics to model some simple flow regimes.  We are looking for a PhD student to work on extending our model validation techniques into fluid mechanics using volumes of data from measurement and predictions rather than fields, i.e., moving from two-dimensional to three-dimensional datasets.  If you are interested or know someone who might be interested then please get in touch.

There is more information on the PhD project here.

Seeing things with nanoparticles

Photograph showing optical microscope and ancilliary equipment set up on an optical benchLast week brought excitement and disappointment in approximately equal measures for my research on tracking nanoparticles [see ‘Slow moving nanoparticles‘ on December 13th, 2017 and ‘Going against the flow‘ on February 3rd, 2021]. The disappointment was that our grant proposal on ‘Optical tracking of virus-cell interaction’ was not ranked highly enough to receive funding from Engineering and Physical Sciences Research Council. Rejection is an occupational hazard for academics seeking to win grants and you learn to accept it, learn from the constructive criticism and look for ways of reworking the ideas into a new proposal. If you don’t compete then you can’t win. The excitement was that we have moved our apparatus for tracking nanoparticles into a new laboratory, which has been set up for it, so that we can start work on a pilot study looking at the ‘Interaction of bacteria and viruses with cellular and hard surfaces’.  We are also advertising for a PhD student to start in September 2021 to work on ‘Developing pre-clinical models to optimise nanoparticle based drug delivery for the treatment of diabetic retinopathy‘.  This is an exciting development because it represents our first step from fundamental research on tracking nanoparticles in biological media towards clinical applications of the technology. Diabetic retinopathy is an age-related condition that threatens your sight and currently is managed by delivery of drugs to the inside of the eye which requires frequent visits to a clinic for injections into the vitreous fluid of the eye.  There is potential to use nanoparticles to deliver drugs more efficiently and to support these developments we plan that the PhD student will use our real-time, non-invasive, label-free tracking technology to quantify nanoparticle motion through the vitreous fluid and the interaction of nanoparticles with the cells of the retina.


Digital twins and seeking consensus

A couple of weeks ago I wrote about our work on a proof-of-concept for a digital twin of a fission nuclear reactor and its extension to fusion energy [‘Digitally-enabled regulatory environment for fusion power plants‘ on March 20th, 2019].  In parallel with this work and together with a colleague in the Dalton Nuclear Institute, I am supervising a PhD student who is studying the potential role of virtual reality and social network analysis in delivering nuclear infrastructure projects.  In a new PhD project, we are aiming to extend this research to consider the potential provided by an integrated nuclear digital environment [1] in planning the disposal of nuclear waste.  We plan to look at how provision of clear, evidence-based information and in the broader adoption of digital twins to enhance public confidence through better engagement and understanding.  This is timely because the UK’s Radioactive Waste Management (RWM) have launched their new consent-based process for siting a Geological Disposal Facility (GDF). The adoption of a digital environment to facilitate a consent-based process represents a new and unprecedented approach to the GDF or any other nuclear project in the UK. So this will be an challenging and exciting research project requiring an innovative and multi-disciplinary approach involving both engineering and social sciences.

The PhD project is fully-funded for UK and EU citizens as part of a Centre for Doctoral Training and will involve a year of specialist training followed by three years of research.  For more information following this link.


[1] Patterson EA, Taylor RJ & Bankhead M, A framework for an integrated nuclear digital environment, Progress in Nuclear Energy, 87:97-103, 2016.

Image: Artist’s impression of geological disposal facility from https://www.gov.uk/government/news/geological-disposal-understanding-our-work


Assessing nanoparticle populations in historic nuclear waste

Together with colleagues at the JRC Ispra, my research group has shown that the motion of small nanoparticles at low concentrations is independent of their size, density and material [1], [see ‘Slow moving nanoparticles‘ on December 13th, 2017].  This means that commercially-available instruments for evaluating the size and number of nanoparticles in a solution will give erroneous results under certain conditions.  In a proposed PhD project, we are planning to extend our work to develop an instrument with capability to automatically identify and size nanoparticles, in the range from 1 to 150 nanometres, using the three-dimensional optical signature, or caustic, which particles generate in an optical microscope, that can be several orders of magnitude larger than the particle [2],  [see ‘Toxic nanoparticles?‘ on November 13th, 2013].  The motivation for the work is the need to characterise particles present in solution in legacy ponds at Sellafield.  Legacy ponds at the Sellafield site have been used to store historic radioactive waste for decades and progress is being made in reducing the risks associated with these facilities [3].  Over time, there has been a deterioration in the condition of the ponds and their contents that has resulted in particles being present in solution in the ponds.  It is important to characterise these particles in order to facilitate reductions in the risks associated with the ponds.  We plan to use our existing facilities at the University of Liverpool to develop a novel instrument using simple solutions probably with gold nanoparticles and then to progress to non-radioactive simulants of the pond solutions.  The long-term goal will be to transition the technology to the Sellafield site perhaps with an intermediate step involving a demonstration of  the technology on pond solutions using the facilities of the National Nuclear Laboratory.

The PhD project is fully-funded for UK and EU citizens as part of a Centre for Doctoral Training and will involve a year of specialist training followed by three years of research.  For more information following this link.


[1] Coglitore, D., Edwardson, S.P., Macko, P., Patterson, E.A., & Whelan, M.P., Transition from fractional to classical Stokes-Einstein behaviour in simple fluids, Royal Society Open Science, 4:170507, 2017.

[2] Patterson, E.A., Whelan, P., 2008, ‘Optical signatures of small nanoparticles in a conventional microscopeSmall, 4(10):1703-1706.

[3] Comptroller and Auditor General, The Nuclear Decommissioning Authority: progress with reducing risk at Sellafield, National Audit Office, HC 1126, Session 2017-19, 20 June 2018.