Category Archives: fluid mechanics

Passive nanorheology measurements

Leave a reply

What do marshmallows, jelly (or Jell-O), cream cheese and Chinese soup dumplings have in common?  They are often made with gelatin.  Gelatin is derived from the skin and bones of cattle and pigs through the partial hydrolysis of collagen.  Gelatin is a physical hydrogel meaning that it consists of a three-dimensional network of polymer molecules in which a large amount of water is absorbed, as much as 90% in gelatin.  These polymer molecules are cross-linked by hydrogen bonds, hydrophobic interactions and chain entanglements.  External stimuli, such as temperature, can change the level of cross-linking causing the material to transition between its solid, liquid and gel states.  This is why jelly sets in the fridge and melts when it’s heated up – the cross-links holding the molecules together break down.  This type of responsive behaviour allows the properties of hydrogels to be controlled at the micro and sub-micron scale for a host of applications including tissue engineering, drug delivery, water treatment, wearable technologies, and supercapacitors.  However, the design and manufacture of soft hydrogels can be challenging due to the lack of technology for measuring the local properties.  Current quantitative techniques for measuring the properties of hydrogels usually focus on bulk properties and provide little data about local variations or real-time responses to external stimuli.  My colleagues and I have used gold nanoparticles as probes in hydrogels to map the properties at the microscale of thermosensitive hydrogels undergoing real-time transition from the solid to gel phases [see ‘Passive nanorheological tool to characterise hydrogels’].  This is an extension, or perhaps more accurately an application, of our earlier work on tracking nanoparticles through the vitreous humour of the eye [see ‘Nanoparticle motion-through heterogeneous hydrogels’ on November 6th, 2024].  The novel technique, which yields passive nanorheological measurements, allows us to evaluate local viscosity, identify time-varying heterogeniety and monitor dynamic phase transitions at the micro through to nano scale.  The significant challenges of other techniques, such as weak signals due to high water content and the dynamism of hydrogels, are overcome with a fast, inexpensive and user-friendly technology.  Although, even with these advantages, you are unlikely to use it when you are making jelly or roasting marshmallows over the campfire; however, it is really useful for understanding the transport of drugs through biological hydrogels or designing manufacturing processes for artificial tissue.

Reference

Moira Lorenzo Lopez, Victoria R. Kearns, Eann A. Patterson & Judith M. Curran, Passive nanorheological tool to characterise hydrogels, Nanoscale, 2025,17, 15338-15347.

Image: Figure 5 from the above reference showing a hydrogel transitioning to a gel phase as result of an increase in temperature with 100 nm diameter gold nanoparticles with some particles (yellow arrows) at the interface between phases.  The image was taken in an inverted optical microscope set up for tracking the nanoparticles.

Highest mountain, deepest lake, smallest church and biggest liar

Leave a reply

Last month we took a short vacation in the Lake District and stayed in Wasdale whose tag-line is highest mountain, deepest lake.  The mountain is Scafell Pike, the highest mountain in England at 978 m, which we never saw because the clouds never lifted high enough to reveal it.  The lake is Wast Water, the deepest lake in England at 74 m, which rose slowly during our week due to the almost continuous rain falling on the surrounding hills.  But that’s typical Lake District weather because the area protrudes to the west of England so it is the first landfall for rainstorms moving east after they have replenished with water over the Irish Sea.  We spent our time reading in our cottage and venturing out to walk in lowlands when the lake was a calm presence, occasionally reflecting the surrounding mountains but more often dark reflecting the low clouds.  We were not tempted to test its temperature but I would expect it to have been around 4 °C because this is the temperature of the water in the depths of all deep lakes all year around.  Hence, in winter the surface layers of water will usually be colder than 4 °C and in summer warmer than 4 °C reflecting the air temperature, so in spring when we visited it would probably have been around 4 °C.  Water expands when it freezes which is possible on the surface of bodies of water where it can expand into the air; however, at depths in deep lakes the pressure prevents the expansion required for the freezing process and equilibrium between opposing processes occurs at about 4 °C.  Thus, the water at the bottom of all deep lakes remains at 4 °C all year with a gradient of increasing temperatures towards the surface in summer and of decreasing temperatures in winter.

Wasdale also claims the smallest church, St Olaf’s and the biggest liar, Will Ritson (1808-1890) who was a landlord of the Wastwater Hotel.  He won the annual world’s biggest liar competition by saying, when it was his turn, that he was withdrawing from the competition because having heard the other competitors he could not tell a bigger lie.

Image: Wast Water with clouds sitting on Great Gable at the east end of the lake.

Reliable predictions of non-Newtonian flows of sludge

1 Reply

Regular readers of this blog will be aware that I have been working for many years on validation processes for computational models of structures employed in a wide range of sectors, including aerospace engineering [see ‘The blind leading the blind’ on May 27th, 2020] and nuclear energy [see ‘Million to one’ on November 21st, 2018].  Validation is determining the extent to which predictions from a model are representative of behaviour in the real-world [see ‘Model validation’ on September 18th, 2012].  More recently, I have been working on model credibility, which is the willingness of people, besides the modeller, to use the predictions from models in decision-making [see, for example, ‘Credible predictions for regulatory decision-making’ on December 9th, 2020].  I have started to consider the complex world of predictive modelling of fluid flow and I am hoping to start a collaboration with a new colleague on the flow of sludges.  Sludges are more common than you might think but we are interested in modelling the flow of waste, both wastewater (sewage) and nuclear wastes.  We have a PhD studentship available sponsored jointly by the GREEN CDT and the National Nuclear Laboratory.  The project is interdisciplinary in two dimensions because it will combine experiments and simulations as well as uniting ideas from solid mechanics and fluid mechanics.  The integration of concepts and technologies across these boundaries brings a level of adventure to the project which will be countered by building on well-established research in solid mechanics on quantitative comparisons of measurements and predictions and by employing current numerical and experimental work on wastewater sludges.  If you are interested or know someone who might want to join our research then you can find out more here.

Image: Sewage sludge disposal in Germany: Andrea Roskosch / UBA

Diving into three-dimensional fluids

Leave a reply

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.