Category Archives: fluid mechanics

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.

Million to one

‘All models are wrong, but some are useful’ is a quote, usually attributed to George Box, that is often cited in the context of computer models and simulations.  Working out which models are useful can be difficult and it is essential to get it right when a model is to be used to design an aircraft, support the safety case for a nuclear power station or inform regulatory risk assessment on a new chemical.  One way to identify a useful model to assess its predictions against measurements made in the real-world [see ‘Model validation’ on September 18th, 2012].  Many people have worked on validation metrics that allow predicted and measured signals to be compared; and, some result in a statement of the probability that the predicted and measured signal belong to the same population.  This works well if the predictions and measurements are, for example, the temperature measured at a single weather station over a period of time; however, these validation metrics cannot handle fields of data, for instance the map of temperature, measured with an infrared camera, in a power station during start-up.  We have been working on resolving this issue and we have recently published a paper on ‘A probabilistic metric for the validation of computational models’.  We reduce the dimensionality of a field of data, represented by values in a matrix, to a vector using orthogonal decomposition [see ‘Recognizing strain’ on October 28th, 2015].  The data field could be a map of temperature, the strain field in an aircraft wing or the topology of a landscape – it does not matter.  The decomposition is performed separately and identically on the predicted and measured data fields to create to two vectors – one each for the predictions and measurements.  We look at the differences in these two vectors and compare them against the uncertainty in the measurements to arrive at a probability that the predictions belong to the same population as the measurements.  There are subtleties in the process that I have omitted but essentially, we can take two data fields composed of millions of values and arrive at a single number to describe the usefulness of the model’s predictions.

Our paper was published by the Royal Society with a press release but in the same week as the proposed Brexit agreement and so I would like to think that it was ignored due to the overwhelming interest in the political storm around Brexit rather than its esoteric nature.


Dvurecenska K, Graham S, Patelli E & Patterson EA, A probabilistic metric for the validation of computational models, Royal Society Open Science, 5:1180687, 2018.

Robots with a delicate touch

whitesgroup demoCan a robot pick up an egg or a baby cactus without damaging either? If it is a conventional ‘hard’ robot then the answer is almost certainly ‘no’. But if it is a ‘soft’ robot then the answer is definitely ‘yes’. They can pick ripe tomatoes from the plant, too. And play the piano with a light touch.

These are all examples used by Professor George Whitesides to illustrate the capability of soft robots during a lecture that I attended last week. The occasion was a scientific discussion meeting on Bio-inspiration of New Technologies which was held to celebrate 350 years to publishing the Philosophical Transactions of the Royal Society. While I was in London listening live to Prof Whitesides and the other eight speakers, other people were listening via video links to Bangalore, India and Sao Paulo, Brazil.

Professor Whitesides’ ingenious robots have ‘fingers’ built from the same soft rubber that is used in implants. They are constructed with a solid layer on one face that is curled around the object being picked up by the inflation of compartments on the reverse face. The inflation of the compartments on the reverse face cause the face to lengthen and the ‘finger’ bends to accommodate the change in length. Careful design of the inflated compartments allows the fingers to conform to the shape being picked up and the use of microfluidics ensures it is not damaged.

Professor Whiteside identified star fish as the source of inspiration for the design of his soft robots. I don’t feel that this short piece has done justice to his work. If, nevertheless, you feel inspired to work for him then there’s probably a queue and since he is professor at Harvard it is almost certainly a long one. His research group has also spun out a company, Soft Robotics Inc. so you could buy some soft robots and explore their capabilities…

Seeing the invisible

Track of the Brownian motion of a 50 nanometre diameter particle

Track of the Brownian motion of a 50 nanometre diameter particle in a fluid.

Nanoparticles are being used in a myriad of applications including sunscreen creams, sports equipment and even to study the stickiness of snot!  By definition, nanoparticles should have one dimension less than 100 nanometres, which is one thousandth of the thickness of a human hair.  Some nanoparticles are toxic to humans and so scientists are studying the interaction of nanoparticles with human cells.  However, a spherical nanoparticle is smaller than the wavelength length of visible light and so is invisible in a conventional optical microscope used by biologists.  We can view nanoparticles using a scanning electron microscope but the electron beam damages living cells so this is not a good solution.  An alternative is to adjust an optical microscope so that the nanoparticles produce caustics [see post entitled ‘Caustics’ on October 15th, 2014] many times the size of the particle.  These ‘adjustments’ involve closing an aperture to produce a pin-hole source of illumination and introducing a filter that only allows through a narrow band of light wavelengths.  An optical microscope adjusted in this way is called a ‘nanoscope’ and with the addition of a small oscillator on the microscope objective lens can be used to track nanoparticles using the technique described in last week’s post entitled ‘Holes in liquid‘.

The smallest particles that we have managed to observe using this technique were gold particles of diameter 3 nanometres , or about 1o atoms in diameter dispersed in a liquid.


Image of 3nm diameter gold particle in a conventional optical microscope (top right), in a nanoscope (bottom right) and composite images in the z-direction of the caustic formed in the nanoscope (left).

Image of 3nm diameter gold particle in a conventional optical microscope (top right), in a nanoscope (bottom right) and composite images in the z-direction of the caustic formed in the nanoscope (left).


‘Scientists use gold nanoparticles to study the stickiness of snot’ by Rachel Feldman in the Washington Post on October 9th, 2014.

J.-M. Gineste, P. Macko, E.A. Patterson, & M.P. Whelan, Three-dimensional automated nanoparticle tracking using Mie scattering in an optical microscope, Journal of Microscopy, Vol. 243, Pt 2 2011, pp. 172–178

Patterson, E.A., & Whelan, M.P., Optical signatures of small nanoparticles in a conventional microscope, Small, 4(10): 1703-1706, 2008.