Tag Archives: mechanics

Nanoparticle motion through heterogeneous hydrogels

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Over the last couple of years, we have been transitioning a technique, which we developed for tracking the motion of nanoparticles using caustics [see ‘Slow moving nanoparticles‘ on December 13th 2017], from its initial use in exploring mechanics at the nanoscale to applications in nanobiology [See ‘Label-free real-time tracking of individual bacterium‘ on January 25th, 2023] where it has the advantages of functioning in real-time and being label-free (chemical labels can impact motion, protein interactions and cell behaviour).  In the summer, we had couple of articles published in consecutive issues of the Nature journal, Scientific Reports which describe our recent work.  In the first, we have explored the diffusion of nanoparticles through a synthetic analogue of the vitreous humour in order to support the design of novel therapeutics for retinal diseases.  Retinal diseases, such as macular degeneration and diabetic retinopathy, affects millions of people globally and treatment often involves frequent intravitreal injections of anti-vascular endothelium growth factor agents and corticoids.  Delivery of the appropriate dose to the retinal cell layer is challenging due to the complex nature of the vitreous and functionalised nanoparticles offer a potential solution.  In vivo animal testing is inappropriate because of the ethical concerns and poor representation of human eyes and ex vivo testing of cadaveric eyes is unreliable due to the instability of biomechanical and biochemical properties of the vitreous humour.  Hence, we used agar-hyaluronic acid hydrogels as an in vitro model of the vitreous and employed the caustic technique to track the motion of nanoparticles through the hydrogels.  The hydrogels had been validated as a representative model of the vitreous humour by other research groups.  Our tracking technique revealed that the electric charge on the nanoparticles did not affect their diffusion through the hydrogel; however, both the diameter of the particles and the heterogeneous nature of the gel influenced the diffusion.  Nanoparticles with diameters of 200, 100 and 50 nm moved progressively more quickly and over a larger area.  The diffusion rates in hydrogels with a high viscosity (about 450  Pa.s) were consistent throughout the gel implying that the gel was homogeneous, while gels with medium (about 40 Pa.s) to low (about 3 Pa.s) viscosity generated diffusion rates that were distributed bi-modally suggesting a heterogeneous gel with zones of low and high density in which the particles moved more or less freely.  The heterogeneity of a gel renders a global value for viscosity somewhat meaningless and makes comparisons difficult with the vitreous humour because it is also heterogeneous; however, global values of viscosity for porcine vitreous humour are typically 1 Pa.s.  We are continuing this research; however, our published work has demonstrated that the use of caustics in an optical microscope is a reproducible and inexpensive technique for exploring the design of novel nanoscale drug delivery systems for the eye.

Source: Lorenzo Lopez M, Kearns VR, Curran JM, Patterson EA. Diffusion of nanoparticles in heterogeneous hydrogels as vitreous humour in vitro substitutes. Scientific reports. 2024 Jul 29;14(1):1744.

Image: Random track of a nanoparticle superimposed on its image generated in the microscope using a pin-hole and narrowband filter.

More on fairy lights and volume decomposition (with ice cream included)

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Explanation in textLast June, I wrote about representing five-dimensional data using a three-dimensional stack of transparent cubes containing fairy lights whose brightness varied with time and also using feature vectors in which the data are compressed into a relatively short string of numbers [see ‘Fairy lights and decomposing multi-dimensional datasets’ on June 14th, 2023].  After many iterations, we have finally had an article published describing our method of orthogonally decomposing multi-dimensional data arrays using Chebyshev polynomials.  In this context, orthogonal means that components of the resultant feature vector are statistically independent of one another.  The decomposition process consists of fitting a particular form of polynomials, or equations, to the data by varying the coefficients in the polynomials.  The values of the coefficients become the components of the feature vector.  This is what we do when we fit a straight line of the form y=mx+c to set of values of x and y and the coefficients are m and c which can be used to compare data from different sources, instead of the datasets themselves.  For example, x and y might be the daily sales of ice cream and the daily average temperature with different datasets relating to different locations.  Of course, it is much harder for data that is non-linear and varying with w, x, y and z, such as the intensity of light in the stack of transparent cubes with fairy lights inside.  In our article, we did not use fairy lights or icecream sales, instead we compared the measurements and predictions in two case studies: the internal stresses in a simple composite specimen and the time-varying surface displacements of a vibrating panel.

The image shows the normalised out-of-plane displacements as the colour as a function of time in the z-direction for the surface of a panel represented by the xy-plane.

Source:

Amjad KH, Christian WJ, Dvurecenska KS, Mollenhauer D, Przybyla CP, Patterson EA. Quantitative Comparisons of Volumetric Datasets from Experiments and Computational Models. IEEE Access. 11: 123401-123417, 2023.

Predicting release rates of hydrogen from stainless steel

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Decorative photograph showing electrolysis cellThe influence of hydrogen on the structural integrity of nuclear power plant, where water molecules in the coolant circuit can be split by electrolysis or radiolysis to produce hydrogen, has been a concern to engineers for decades.  However, plans for a hydrogen economy and commercial fusion reactors, in which plasma-facing structural components will likely be exposed to hydrogen, has accelerated interest in understanding the complex interactions of hydrogen with metals, especially in the presence of irradiation.  A key step in advancing our understanding of these interactions is the measurement and prediction of the uptake and release of hydrogen by key structural materials.  We have recently published a study in Scientific Reports in which we developed a method for predicting the amount hydrogen in a steel under test conditions.  We used a sample of stainless steel as an electrode (cathode) in an electrolysis cell that split water molecules producing hydrogen atoms that were attracted to the steel. After loading the steel with hydrogen in the cell, we measured the rate of release of the hydrogen from the steel over two minutes by monitoring the drop in current in the cell, using a technique called potentiostatic discharge.  We used our measurements to calibrate a model of hydrogen release rate, based on Fick’s second law of diffusion, which relates the rate of hydrogen motion (diffusion) to the surface area perpendicular to the motion and the concentration gradient in the direction of motion.  Finally, we used our calibrated model to predict the release rate of hydrogen over 24 hours and checked our predictions using a second measurement based on the hydrogen released when the steel was melted.  So, now we have a method of predicting the amount of hydrogen in a steel remaining in a sample many hours after exposure during electrolysis without destroying the test sample.  This will allow us to perform better defined tests on the influence of hydrogen on the performance of stainless steel in the extreme environments of fission and fusion reactors.

Source:

Weihrauch M, Patel M, Patterson EA. Measurements and predictions of diffusible hydrogen escape and absorption in cathodically charged 316LN austenitic stainless steel. Scientific Reports. 13(1):10545, 2023.

Image:

Figure 2a from Weihrauch et al , 2023 showing electrolysis cell setup for potentiostatic discharge experiments.

Reliable predictions of non-Newtonian flows of sludge

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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