Last week I wrote about research we have been carrying out over the last decade that is being applied to large scale structures in the aerospace industry (see ‘Slowly crossing the valley of death‘ on January 27th, 2021). I also work on very much smaller ‘structures’ that are only tens of nanometers in diameter, or about a billion times smaller than the test samples in last week’s post (see ‘Toxic nanoparticles?‘ on November 13th, 2013). The connection is the use of light to measure shape, deformation and motion; and then utilising the measurements to validate predictions from theoretical or computational models. About three years ago, we published research which demonstrated that the motion of very small particles (less than about 300 nanometres) at low concentrations (less than about a billion per millilitre) in a fluid was dominated by the molecules of the fluid rather than interactions between the particles (see Coglitore et al, 2017 and ‘Slow moving nanoparticles‘ on December 13th, 2017). This data confirmed results from earlier molecular dynamic simulations that contradicted predictions using the Stokes-Einstein equation, which was derived by Einstein in his PhD thesis for a ‘Stokes’ particle undergoing Brownian motion. The Stokes-Einstein equation works well for large particles but the physics of motion changes when the particles are very small and far apart so that Van der Waals forces and electrostatic forces play a dominant role, as we have shown in a more recent paper (see Giorgi et al, 2019). This becomes relevant when evaluating nanoparticles as potential drug delivery systems or assessing the toxicological impact of nanoparticles. We have shown recently that instruments based on dynamic scattering of light from nanoparticles are likely to be inaccurate because they are based on fitting measurement data to the Stokes-Einstein equation. In a paper published last month, we found that asymmetric flow field flow fractionation (or AF4) in combination with dynamic light scattering when used to detect the size of nanoparticles in suspension, tended to over-estimate the diameter of particles smaller than 60 nanometres at low concentrations by upto a factor of two (see Giorgi et al, 2021). Someone commented recently that our work in this area was not highly cited but perhaps this is unsurprising when it undermines a current paradigm. We have certainly learnt to handle rejection letters, to redouble our efforts to demonstrate the rigor in our research and to present conclusions in a manner that appears to build on existing knowledge rather than demolishing it.
Sources:
Coglitore, D., Edwardson, S.P., Macko, P., Patterson, E.A. and Whelan, M., 2017. Transition from fractional to classical Stokes–Einstein behaviour in simple fluids. Royal Society open science, 4(12), p.170507.
Giorgi, F., Coglitore, D., Curran, J.M., Gilliland, D., Macko, P., Whelan, M., Worth, A. and Patterson, E.A., 2019. The influence of inter-particle forces on diffusion at the nanoscale. Scientific reports, 9(1), pp.1-6.
Giorgi, F., Curran, J.M., Gilliland, D., La Spina, R., Whelan, M.P. & Patterson, E.A. 2021, Limitations of nanoparticles size characterization by asymmetric flow field-fractionation coupled with online dynamic light scattering, Chromatographia, doi.org/10/1007/s10337-020-03997-7.
Image is a photograph of a fast flowing mountain river taken in Yellowstone National Park during a roadtrip across the USA in 2006.
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.
Realize Engineering 