THEORETICAL AND COMPUTER MODELS OF 3-DIMENSIONAL GRAIN BOUNDARY MIGRATION WITH MOBILE PARTICLES

In this work, the migration of a three-dimensional (3D) spherical crystal in the presence of mobile particles using a Monte Carlo algorithm was studied. Different concentrations of particles (<i>f</i>) and different particle mobility (<i>M_p</i>) were used. It was found that the grain size reaches a critical radius (<i>R_c</i>) which depends exclusively on <i>f</i>. This dependence can be written as: <i>R_c</i>∝<i>f</i>^1/3. The dynamic equation of grain size evolution and its analytical solution were also found. The analytical solution proposed fits successfully the simulation results. The particle fraction in the grain boundary was also found analytically and it fits the computational data.

TRAIT2D: a Software for Quantitative Analysis of Single Particle Diffusion Data

Single particle tracking (SPT) is one of the most widely used tools in optical microscopy to evaluate particle mobility in a variety of situations, including cellular and model membrane dynamics. Recent technological developments, such as Interferometric Scattering microscopy, have allowed recording of long, uninterrupted single particle trajectories at kilohertz framerates. The resulting data, where particles are continuously detected and do not displace much between observations, thereby do not require complex linking algorithms. Moreover, while these measurements offer more details into the short-term diffusion behaviour of the tracked particles, they are also subject to the influence of localisation uncertainties, which are often underestimated by conventional analysis pipelines. we thus developed a Python library, under the name of TRAIT2D (Tracking Analysis Toolbox – 2D version), in order to track particle diffusion at high sampling rates, and analyse the resulting trajectories with an innovative approach. The data analysis pipeline introduced is more localisation-uncertainty aware, and also selects the most appropriate diffusion model for the data provided on a statistical basis. A trajectory simulation platform also allows the user to handily generate trajectories and even synthetic time-lapses to test alternative tracking algorithms and data analysis approaches. A high degree of customisation for the analysis pipeline, for example with the introduction of different diffusion modes, is possible from the source code. Finally, the presence of graphical user interfaces lowers the access barrier for users with little to no programming experience.

Confocal Analysis of CNC based Hydrogels and Suspensions

Cellulose nano crystal (CNC) hydrogels, while mechanically weak, have unique properties such as easy synthesis, high water content, and biocompatibility. Further improvement is needed to make CNC hydrogels mechanically stable and self-healable. Herein, using quantitative fluorescence recovery after photobleaching (FRAP) analysis, we assess stability, collapse, and level of self-healing of CNC hydrogels with different CNC and sodium chloride (NaCl) concentrations. We use the mean signal intensity obtained by confocal laser scanning microscopy (CLSM) to measure signal loss of the samples made of CNC hydrogels of different CNC concentrations and as a function of initial gel height and NaCl loading. The CNC dynamics inside the gels based on universality curves is unraveled which links the zeta potentials to the immobile particle percentages and the storage moduli as a function of NaCl/CNC concentration ratio. FRAP recovery analysis shows that for the ratio of NaCl/CNC beyond 0.1, the mobility of the ensemble of CNC particles becomes severely restricted. Hydrogel samples with low CNC concentrations (6 g/L and 10 g/L) experience a more substantial collapse rate under gravity than the rate observed for samples with a high CNC concentration (30 g/L). Increasing the CNC concentration hinders particle mobility and thus impedes the self-healing process. Quantification of the gel collapse behavior of CNC gel and its self-healing property is critical in many applications, including water and air filters, oil spill sponges, and tissue engineering.

A novel modelling and simulation approach for the hindered mobility of charged particles in biological hydrogels

This article presents a novel computational model to study the selective filtering of biological hydrogels due to the surface charge and size of diffusing particles. It is the first model that includes the random three-dimensional fibre orientation and connectivity of the biopolymer network and that accounts for elastic deformations of the fibres by means of beam theory. As a key component of the model, novel formulations are proposed both for the electrostatic and repulsive steric interactions between a spherical particle and a beam. In addition to providing a thorough validation of the model, the presented computational studies yield new insights into the underlying mechanisms of hindered particle mobility, especially regarding the influence of the aforementioned aspects that are unique to this model. It is found that the precise distribution of fibre and thus charge agglomerations in the network have a crucial influence on the mobility of oppositely charged particles and gives rise to distinct motion patterns. Considering the high practical significance for instance with respect to targeted drug release or infection defence, the provided proof of concept motivates further advances of the model towards a truly predictive computational tool that allows a case- and patient-specific assessment for real (biological) systems.

Microscopic understanding of particle-matrix interaction in magnetic hybrid materials by element-specific spectroscopy

Mössbauer spectroscopy is a well-known technique to study complex magnetic structures, due to its sensitivity to electronic and magnetic interactions of the probed nucleus with its electronic surrounding. It has also been applied to the emerging fields of magnetic hybrid materials as well as to ferrofluids, as information on the magnetic alignment and the velocity of the probed nucleus, i.e. of the particle it is embedded in, can be inferred from the spectra in addition to the above-mentioned quantities. Considering the wide range of preparation methods and sample properties, including fluids, particle powders, sintered pellets, polymer matrices and viscoelastic hydrogels, a considerable advantage of Mössbauer spectroscopy is the usage of γ-photons. This allows measurements on opaque samples, for which optical experiments are usually not feasible, also making the technique relatively independent of specific sample geometries or preparation. Using iron oxide nanoparticles in glycerol solution as an exemplary material here, the variety of system parameters simultaneously accessible via Mössbauer spectroscopy can be demonstrated: Spectra recorded for particles of different sizes provided information on the particles’ Brownian dynamics, including the effect of the shell thickness on their hydrodynamic diameter, the presence (or absence) and ballpark frequency of Néel superspin relaxation as well as the particles’ average magnetic orientation in external magnetic fields. For single-core particles, this resulted in the observation of standard Langevin-type alignment behavior. Mössbauer spectra additionally provide information on the absolute degree of spin alignment, also allowing the determination of the degree of surface spin canting, which limits the maximum magnetization of ferrofluid samples. Analyzing the alignment behavior of agglomerated particles for comparison, we found a completely different trend, in which spin alignment was further hindered by the competition of easy magnetic directions. More complex particle dynamics are observed when going from ferrofluids to hybrid materials, where the particle mobility and alignability depends not only on the particles’ shape and material, but also on the matrices’ inner structure and the acting force-transfer mechanism between particles and the surrounding medium. In ferrohydrogels for example, particle mobility in terms of Mössbauer spectroscopy was probed for different crosslinker concentrations, resulting in widely different mesh-sizes of the polymer network and different degrees of freedom. While a decrease in particle dynamics is clearly visible in Mössbauer spectroscopy upon rising crosslinker density, complementary AC-susceptometry experiments indicated no Brownian motion on the expected timescales. This apparent contradiction could, however, be explained by the different timescales of the experiments, probing either the relatively free Brownian motion on ultrashort timescales or the more bound state preventing extensive particle motion by interaction with the trapping mesh walls in the millisecond regime. However, it should also be considered that the effect of the surroundings on particle rotation in AC-susceptometry may also differ from the variation in translational motion, probed by Mössbauer spectroscopy. Being sensitive mainly to translational motion also results in a wide range of particles to be accessible for studies via Mössbauer spectroscopy, including larger agglomerates embedded in polymers, intended for remote-controlled heating. Despite the agglomerates’ wide distribution in effective diameters, information on particle motion was found to be in good agreement with AC-susceptometry experiments at ultralow frequencies in and above the polymer melting region, while additionally giving insight into Néel relaxation of the individual nanoparticles and their magnetic structure.

TRAIT2D: a Software for Quantitative Analysis of Single Particle Diffusion Data

SummarySingle Particle Tracking (SPT) is one of the most widespread techniques to evaluate particle mobility in a variety of situations, such as in cellular and model membrane dynamics. The proposed TRAIT2D Python library is developed to provide object tracking, trajectory analysis and produce simulated datasets with graphical user interface. The tool allows advanced users to customise the analysis to their requirements.Availability and implementation: the software has been coded in Python, and can be accessed from: https://github.com/Eggeling-Lab-Microscope-Software/TRAIT2D, or the pypi and condaforge repositories.A comprehensive user guide is provided at https://eggeling-lab-microscope-software.github.io/TRAIT2D/.

Quantifying microplastic particle transport and retention in an experimental flume environment

&lt;p&gt;Rivers and streams are the dominant transport vectors for microplastic (MP) input into marine environments. During transport, complex physicochemical interactions between particles, water and river sediments influence particle mobility and retention. The specific transport mechanisms of MP in fluvial systems are not yet fully understood, and the main reason lies in the limitation in reliable data derived from experimental analysis.&lt;/p&gt;&lt;p&gt;In our subproject of the &amp;#8216;CRC 1357 Microplastics&amp;#8217;, we investigate the hydrodynamic mechanisms that control the transport and retention behavior of MP in open channel flows and streambed sediments. In an experimental flume environment, we create realistic hydrodynamic and hyporheic flow conditions by using various porous media (e.g. glass beads or sand) and bedform structures (e.g. riffle-pool sequences, ripples and dunes), modelled from real stream systems.&lt;/p&gt;&lt;p&gt;The method developed here can quantitatively analyze the transport of pore-scale particles (1-40 &amp;#181;m) based on fluorometric techniques. Particle velocity distributions and particle transport are measured using Particle-Image-Velocimetry and Laser-Doppler-Velocimetry. With our setup, we can quantitatively investigate time-resolved MP transport and retention through the aqueous and solid phase in a flume scale experiment.&lt;/p&gt;

Feedbacks between sediment input, bed state and threshold for motion in gravel-bed rivers: an experimental study

&lt;p&gt;In gravel-bed rivers the relation between the magnitude and frequency of sediment input, the threshold for motion and channel stability is still not fully understood.&lt;/p&gt;&lt;p&gt;Here we present results from a 280-hour long flume experiment, in which poorly sorted sediment was fed episodically in an 18-m long, 2.2%-steep channel. The experiment included 7 consecutive runs lasting 40 hours each characterized by a constant water discharge but different sediment supply regimes (i.e., with no feed, constant feed and sediment pulses). Several measurements of sediment transport, flow depth and bed structures were taken along the flume, to assess how changes in sediment supply influence particle mobility and channel stability.&lt;/p&gt;&lt;p&gt;Our results show that the surface grain&amp;#8208;size distribution coarsened quickly, developing an armored layer that persisted throughout the entire experiment with only short-lived changes after sediment pulses. Grain clusters and other bed structures developed continuously during the experiments, changing dynamically in response to sediment pulses.&lt;/p&gt;&lt;p&gt;We estimated the thresholds of motion with three different methods, all of which yielded consistent results. Overall, the threshold for motion increased during the experiment, fluctuating in response to changes in sediment input. Our results provide further evidence to the idea that the threshold for motion in gravel-bed rivers is not a constant, but changes as a state parameter. These changes in our experiments are controlled by (a) the sediment supply regime, (b) the degree of bed structuring, and (c) the history of bed evolution. These outcomes suggest that sediment supply regime is a primary control on bed surface evolution and the channel stabilizing function played by surface structures.&lt;/p&gt;