We here explore confinement-induced assembly of whey protein nanofibrils (PNFs) into microscale fibers using micro-focused synchrotron X-ray scattering. Solvent evaporation aligns the PNFs into anisotropic fibers and the process is followed in situ by scattering experiments in a droplet of PNF dispersion. We find an optimal temperature at which the order of the protein fiber has a maximum, suggesting that the degree of order results from a balance between the time scales of the forced alignment and the rotational diffusion of the fibrils. Moreover, we observe that the assembly process depends on the nano-scale morphology of the PNFs. Stiff PNFs with a persistence length in the micrometer scale are aligned at the air-water interface and the anisotropy gradually decrease towards the center of the droplet. Marangoni flows often increase entanglements toward the center, leading to complex patterns in the droplet. Flexible fibrils with a short persistence length (< 100 nm) tends to align uniformly throughout the droplet, possibly due to stronger local entanglements. Straight PNFs form smaller clusters with shorter inter-cluster distances due to their tight packing and consistent linear structure. In contrast, curved PNFs form intricate networks with larger characteristic distances and more varied structures because of their flexibility and adaptability.

Lubricated textured surfaces immersed in liquid flows offer tremendous potential for reducing fluid drag, enhancing heat and mass transfer, and preventing fouling. According to current design rules, the lubricant must chemically match the surface to remain robustly trapped within the texture. However, achieving such chemical compatibility poses a significant challenge for large-scale flow systems, as it demands advanced surface treatments or severely limits the range of viable lubricants. In addition, chemically tuned surfaces often degrade over time in harsh environments. Here, we demonstrate that a lubricant-infused surface (LIS) can resist drainage in the presence of external shear flow without requiring chemical compatibility. Surfaces featuring longitudinal grooves can retain up to 50% of partially wetting lubricants in fully developed turbulent flows. The retention relies on contact-angle hysteresis, where triple-phase contact lines are pinned to substrate heterogeneities, creating capillary resistance that prevents lubricant depletion. We develop an analytical model to predict the maximum length of pinned lubricant droplets in microgrooves. This model, validated through a combination of experiments and numerical simulations, can be used to design chemistry-free LISs for applications where the external environment is continuously flowing. Our findings open up new possibilities for using functional surfaces to control transport processes in large systems.

The combination of flow elasticity and inertia has emerged as a viable tool for focusing and manipulating particles using microfluidics. Although there is considerable interest in the field of elasto-inertial microfluidics owing to its potential applications, research on particle focusing has been mostly limited to low Reynolds numbers (Re<1), and particle migration toward equilibrium positions has not been extensively examined. In this work, we thoroughly studied particle focusing on the dynamic range of flow rates and particle migration using straight microchannels with a single inlet high aspect ratio. We initially explored several parameters that had an impact on particle focusing, such as the particle size, channel dimensions, concentration of viscoelastic fluid, and flow rate. Our experimental work covered a wide range of dimensionless numbers (0.05 < Reynolds number < 85, 1.5 < Weissenberg number < 3800, 5 < Elasticity number < 470) using 3, 5, 7, and 10 µm particles. Our results showed that the particle size played a dominant role, and by tuning the parameters, particle focusing could be achieved at Reynolds numbers ranging from 0.2 (1 µL/min) to 85 (250 µL/min). Furthermore, we numerically and experimentally studied particle migration and reported differential particle migration for high-resolution separations of 5 µm, 7 µm and 10 µm particles in a sheathless flow at a throughput of 150 µL/min. Our work elucidates the complex particle transport in elasto-inertial flows and has great potential for the development of high-throughput and high-resolution particle separation for biomedical and environmental applications. (Figure presented.)

Motivated by the limitations of conventional coarse-grained molecular dynamics for simulation of large systems of nanoparticles and the challenges in efficiently representing general pair potentials for rigid bodies, we present a method for approximating general rigid body pair potentials based on a specialized type of deep neural network that maintains essential properties, such as conservation of energy and invariance to the chosen origins of the particles. The network uses a specialized geometric abstraction layer to convert the relative coordinates of the rigid bodies to input more suitable to a conventional artificial neural network, which is trained together with the specialized layer. This results in geometric representations of the particles optimized for the specific potential. The network can be trained directly on scalar values to fit a model without explicit gradient and then used to efficiently evaluate the force and torque on the particles resulting from the potential. The concept is demonstrated with an atomistic interaction model for carbon nanotubes and the resulting model is compared with a common type of coarse-grained model optimized for the same potential, with even very small networks comparing favourably and larger networks achieving up to two orders of magnitude lower cost. The sensitivity to noise in the training data is investigated and the model is found to strongly reject noise up to 12.5% given a dataset of 107 samples. The performance of a proof-of-concept implementation is demonstrated on a variety of hardware, showing the models viability for large-scale simulations. Furthermore, generalization to soft bodies and potentials for polydisperse systems are discussed.

Thixo-elasto-viscoplastic (TEVP) fluids are very complex fluids. In addition to elasticity and viscoplasticity, they exhibit thixotropy, i.e., time-dependent rheology due to breakdown and recovery of internal structures at different length- and timescales. General and consistent methods for a priori flow prediction of TEVP fluids based on rheological characteristics are yet to be developed. We report a combined study of the rheology and flow of 18 samples of different TEVP fluids (three yogurts and three concentrations of Laponite and Carbopol, respectively, in water in both the unstirred and a stirred state). The rheology is determined both with standard protocols and with an ex situ protocol aiming at reproducing the shear history of the fluid in the flow. Micrometer resolution flow measurements in a millimeter scale rectangular duct are performed with Doppler Optical Coherence Tomography (D-OCT). As expected, the results show the existence of a plug flow region for samples with sufficiently high yield stress. At low flow rates, the plug extends almost all the way to the wall and the extent of the plug decreases not only with increased flow rate but also with increased thixotropy. The ex situ rheology protocol enables estimation of the shear rate and shear stress close to the wall, making it possible to identify two scaling laws that relates four different non-dimensional groups quantifying the key properties wall-shear stress and slip velocity. The scaling laws are suggested as an ansatz for a priori prediction of the near-wall flow of TEVP fluids based on shear flow-curves obtained with a rheometer.

Suspensions of finite-size solid particles in a turbulent pipe flow are found in many industrial and technical flows. Due to the ample parameter space consisting of particle size, concentration, density and Reynolds number, a complete picture of the particle-fluid interaction is still lacking. Pressure drop predictions are often made using viscosity models only considering the bulk solid volume fraction. For the case of turbulent pipe flow laden with neutrally buoyant spherical particles, we investigate the pressure drop and overall drag (friction factor), fluid velocity and particle distribution in the pipe. We use a combination of experimental (MRV) and numerical (DNS) techniques and a continuum flow model. We find that the particle size and the bulk flow rate influence the mean fluid velocity, velocity fluctuations and the particle distribution in the pipe for low flow rates. However, the effects of the added solid particles diminish as the flow rate increases. We created a master curve for drag change compared to single-phase flow for the particle-laden cases. This curve can be used to achieve more accurate friction factor predictions than the traditional modified viscosity approach that does not account for particle size.

Biopolymer network dynamics play a significant role in both biological and materials science. This study focuses on the dynamics of cellulose nanofibers as a model system given their relevance to biology and nanotechnology applications. Using large-scale coarse-grained simulations with a lattice Boltzmann fluid coupling, we investigated the reptation behavior of individual nanofibers within entangled networks. Our analysis yields essential insights, proposing a scaling law for rotational diffusion, quantifying effective tube diameter, and revealing release mechanisms during reptation, spanning from rigid to semiflexible nanofibers. Additionally, we examine the onset of entanglement in relation to the nanofiber flexibility within the network. Microrheology analysis is conducted to assess macroscopic viscoelastic behavior. Importantly, our results align closely with previous experiments, validating the proposed scaling laws, effective tube diameters, and onset of entanglement. The findings provide an improved fundamental understanding of biopolymer network dynamics and guide the design of processes for advanced biobased materials. 

The ability to control the structure of the wood-pulp fiber cell wall is an attractive means to obtain increased accessibility to the fiber interior, providing routes for functionalization of the fibers that support further processing and novel material concepts, e.g. improved degree of polymerization, nanofiltration as demonstrated in previous studies. It has been proposed that dynamic compression and decompression of the cellulose pulp fibers in the wet state make it possible to modify the cell wall significantly. We hypothesize that hydrostatic pressure exerted on fibers fully submerged in water will increase the accessibility of the fiber wall by penetrating the fiber through weak spots in the cell wall. To pursue this, we have developed an experimental facility that can subject wet cellulose pulp samples to a pressure pulse -10 ms long and with a peak pressure of -300 MPa. The experiment is thus specifically designed to elucidate the effect of a rapid high-pressure pulse passing through the cellulose sample and enables studies of changes in structural properties over different size ranges. Different characterization techniques, including Scanning electron microscopy, X-ray diffraction, and wide- and small-angle X-ray scattering, have been used to evaluate the material exposed to pulsed pressure. The mechanism of pressure build-up is estimated computationally to complement the results. Key findings from the experiments consider a decrease in crystallinity and changes in the surface morphology of the cellulose sample.

Textured hydrophobic surfaces that repel liquid droplets unidirectionally are found in nature such as butterfly wings and ryegrass leaves and are also essential in technological processes such as self-cleaning and anti-icing. In many occasions, surface textures are oriented to direct rebounding droplets. Surface macrostructures (>100 μm) have often been explored to induce directional rebound. However, the influence of impact speed and detailed surface geometry on rebound is vaguely understood, particularly for small microstructures. Here, we study, using a high-speed camera, droplet impact on surfaces with inclined micropillars. We observed directional rebound at high impact speeds on surfaces with dense arrays of pillars. We attribute this asymmetry to the difference in wetting behavior of the structure sidewalls, causing slower retraction of the contact line in the direction against the inclination compared to with the inclination. The experimental observations are complemented with numerical simulations to elucidate the detailed movement of the drops over the pillars. These insights improve our understanding of droplet impact on hydrophobic microstructures and may be useful for designing structured surfaces for controlling droplet mobility.