Tag Archives: PhD opportunity

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.

Reference:

[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.

References:

[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.

Spatial-temporal models of protein structures

For a number of years I have been working on methods for validating computational models of structures [see ‘Model validation‘ on September 18th 2012] using the full potential of measurements made with modern techniques such as digital image correlation [see ‘256 shades of grey‘ on January 22nd 2014] and thermoelastic stress analysis [see ‘Counting photons to measure stress‘ on November 18th 2015].  Usually the focus of our interest is at the macroscale, for example the research on aircraft structures in the MOTIVATE project; however, in a new PhD project with colleagues at the National Tsing Hua University in Taiwan, we are planning to explore using our validation procedures and metrics [1] in structural biology.

The size and timescale of protein-structure thermal fluctuations are essential to the regulation of cellular functions. Measurement techniques such as x-ray crystallography and transmission electron cryomicroscopy (Cryo-EM) provide data on electron density distribution from which protein structures can be deduced using molecular dynamics models. Our aim is to develop our validation metrics to help identify, with a defined level of confidence, the most appropriate structural ensemble for a given set of electron densities. To make the problem more interesting and challenging the structure observed by x-ray crystallography is an average or equilibrium state because a folded protein is constantly in motion undergoing harmonic oscillations, each with different frequencies and amplitude [2].

The PhD project is part of the dual PhD programme of the University of Liverpool and National Tsing Hua University.  Funding is available in form of a fee waiver and contribution to living expenses for four years of study involving significant periods (perferably two years) at each university.  For more information follow this link.

References:

[1] Dvurecenska, K., Graham, S., Patelli, E. & Patterson, E.A., A probabilistic metric for the validation of computational models, Royal Society Open Society, 5:180687, 2018.

[2] Justin Chan, Hong-Rui Lin, Kazuhiro Takemura, Kai-Chun Chang, Yuan-Yu Chang, Yasumasa Joti, Akio Kitao, Lee-Wei Yang. An efficient timer and sizer of protein motions reveals the time-scales of functional dynamics in the ribosome (2018) https://www.biorxiv.org/content/early/2018/08/03/384511.

Image: A diffraction pattern and protein structure from http://xray.bmc.uu.se/xtal/