Spontaneous motion of a passive fluid droplet in an active microchannel.

We numerically study the dynamics of a passive fluid droplet confined within a microchannel whose walls are covered with a thin layer of active gel. The latter represents a fluid of extensile material modelling, for example, a suspension of cytoskeletal filaments and molecular motors. Our results show that the layer is capable of producing a spontaneous flow triggering a rectilinear motion of the passive droplet. For a hybrid design (a single wall covered by the active layer), at the steady state the droplet attains an elliptical shape, resulting from an asymmetric saw-toothed structure of the velocity field. In contrast, if the active gel covers both walls, the velocity field exhibits a fully symmetric pattern considerably mitigating morphological deformations. We further show that the structure of the spontaneous flow in the microchannel can be controlled by the anchoring conditions of the active gel at the wall. These findings are also confirmed by selected 3D simulations. Our results may stimulate further research addressed to design novel microfludic devices whose functioning relies on the collective properties of active gels.

Benchmarking YOLOv5 and YOLOv7 models with DeepSORT for droplet tracking applications.

Tracking droplets in microfluidics is a challenging task. The difficulty arises in choosing a tool to analyze general microfluidic videos to infer physical quantities. The state-of-the-art object detector algorithm You Only Look Once (YOLO) and the object tracking algorithm Simple Online and Realtime Tracking with a Deep Association Metric (DeepSORT) are customizable for droplet identification and tracking. The customization includes training YOLO and DeepSORT networks to identify and track the objects of interest. We trained several YOLOv5 and YOLOv7 models and the DeepSORT network for droplet identification and tracking from microfluidic experimental videos. We compare the performance of the droplet tracking applications with YOLOv5 and YOLOv7 in terms of training time and time to analyze a given video across various hardware configurations. Despite the latest YOLOv7 being 10% faster, the real-time tracking is only achieved by lighter YOLO models on RTX 3070 Ti GPU machine due to additional significant droplet tracking costs arising from the DeepSORT algorithm. This work is a benchmark study for the YOLOv5 and YOLOv7 networks with DeepSORT in terms of the training time and inference time for a custom dataset of microfluidic droplets.

Effect of coarse graining in water models for the study of kinetics and mechanisms of clathrate hydrates nucleation and growth.

Clathrate hydrates are crystalline inclusion compounds wherein a water framework encages small guest atoms/molecules within its cavities. Among the others, methane clathrates are the largest fossil fuel resource still available. They can also be used to safely transport gases and can also form spontaneously under suitable conditions plugging pipelines. Understanding the crystallization mechanism is very important, and given the impossibility of experimentally identifying the atomistic path, simulations played an important role in this field. Given the large computational cost of these simulations, in addition to all-atom force fields, scientists considered coarse-grained water models. Here, we have investigated the effect of coarse-graining, as implemented in the water model mW, on the crystallization characteristics of methane clathrate in comparison with the all-atom TIP4P force field. Our analyses revealed that although the characteristics directly depending on the energetics of the water models are well reproduced, dynamical properties are off by the orders of magnitude. Being crystallization a non-equilibrium process, the altered kinetics of the process results in different characteristics of crystalline nuclei. Both TIP4P and mW water models produce methane clathrate nuclei with some amount of the less stable (in the given thermodynamic conditions) structure II phase and an excess of pentagonal dodecahedral cages over the tetrakaidecahedral ones regarding the ideal ratio in structure I. However, the dependence of this excess on the methane concentration in solution is higher with the former water model, whereas with the latter, the methane concentration in solution dependence is reduced and within the statistical error.

Lightweight lattice Boltzmann.

A regularized version of the lattice Boltzmann method for efficient simulation of soft materials is introduced. Unlike standard approaches, this method reconstructs the distribution functions from available hydrodynamic variables (density, momentum, and pressure tensor) without storing the full set of discrete populations. This scheme shows significantly lower memory requirements and data access costs. A series of benchmark tests of relevance to soft matter, such as collisions of fluid droplets, is discussed to validate the method. The results can be of particular interest for high-performance simulations of soft matter systems on future exascale computers.

Simulating Polymerization by Boltzmann Inversion Force Field Approach and Dynamical Nonequilibrium Reactive Molecular Dynamics.

The radical polymerization process of acrylate compounds is, nowadays, numerically investigated using classical force fields and reactive molecular dynamics, with the aim to probe the gel-point transition as a function of the initial radical concentration. In the present paper, the gel-point transition of the 1,6-hexanediol dimethacrylate (HDDMA) is investigated by a coarser force field which grants a reduction in the computational costs, thereby allowing the simulation of larger system sizes and smaller radical concentrations. Hence, the polymerization is investigated using reactive classical molecular dynamics combined with a dynamical approach of the nonequilibrium molecular dynamics (D-NEMD). The network structures in the polymerization process are probed by cluster analysis tools, and the results are critically compared with the similar all-atom system, showing a good agreement.

Double Life of Methanol: Experimental Studies and Nonequilibrium Molecular-Dynamics Simulation of Methanol Effects on Methane-Hydrate Nucleation.

We have investigated systematically and statistically methanol-concentration effects on methane-hydrate nucleation using both experiment and restrained molecular-dynamics simulation, employing simple observables to achieve an initially homogeneous methane-supersaturated solution particularly favorable for nucleation realization in reasonable simulation times. We observe the pronounced "bifurcated" character of the nucleation rate upon methanol concentration in both experiments and simulation, with promotion at low concentrations and switching to industrially familiar inhibition at higher concentrations. Higher methanol concentrations suppress hydrate growth by in-lattice methanol incorporation, resulting in the formation of "defects", increasing the energy of the nucleus. At low concentrations, on the contrary, the detrimental effect of defects is more than compensated for by the beneficial contribution of CH3 in easing methane incorporation in the cages or replacing it altogether.

Stochastic Jetting and Dripping in Confined Soft Granular Flows.

We report new dynamical modes in confined soft granular flows, such as stochastic jetting and dripping, with no counterpart in continuum viscous fluids. The new modes emerge as a result of the propagation of the chaotic behavior of individual grains-here, monodisperse emulsion droplets-to the level of the entire system as the emulsion is focused into a narrow orifice by an external viscous flow. We observe avalanching dynamics and the formation of remarkably stable jets-single-file granular chains-which occasionally break, resulting in a non-Gaussian distribution of cluster sizes. We find that the sequences of droplet rearrangements that lead to the formation of such chains resemble unfolding of cancer cell clusters in narrow capillaries, overall demonstrating that microfluidic emulsion systems could serve to model various aspects of soft granular flows, including also tissue dynamics at the mesoscale.

Capturing Free-Radical Polymerization by Synergetic Ab Initio Calculations and Topological Reactive Molecular Dynamics.

Photocurable polymers are used ubiquitously in 3D printing, coatings, adhesives, and composite fillers. In the present work, the free radical polymerization of photocurable compounds is studied using reactive classical molecular dynamics combined with a dynamical approach of the nonequilibrium molecular dynamics (D-NEMD). Different concentrations of radicals and reaction velocities are considered. The mechanical properties of the polymer resulting from 1,6-hexanediol dimethacrylate systems are characterized in terms of viscosity, diffusion constant, and activation energy, whereas the topological ones through the number of cycles (polymer loops) and cyclomatic complexity. Effects like volume shrinkage and delaying of the gel point for increasing monomer concentration are also predicted, as well as the stress-strain curve and Young's modulus. Combining ab initio, reactive molecular dynamics, and the D-NEMD method might lead to a novel and powerful tool to describe photopolymerization processes and to original routes to optimize additive manufacturing methods relying on photosensitive macromolecular systems.

Electric field induced macroscopic cellular phase of nanoparticles.

A suspension of nanoparticles with very low volume fraction is found to assemble into a macroscopic cellular phase that is composed of particle-rich walls and particle-free voids under the collective influence of AC and DC voltages. Systematic study of this phase transition shows that it was the result of electrophoretic assembly into a two-dimensional configuration followed by spinodal decomposition into particle-rich walls and particle-poor cells mediated principally by electrohydrodynamic flow. This mechanistic understanding reveals two characteristics needed for a cellular phase to form, namely (1) a system that is considered two dimensional and (2) short-range attractive, long-range repulsive interparticle interactions. In addition to determining the mechanism underpinning the formation of the cellular phase, this work presents a method to reversibly assemble microscale continuous structures out of nanoscale particles in a manner that may enable the creation of materials that impact diverse fields including energy storage and filtration.

A fast and efficient deep learning procedure for tracking droplet motion in dense microfluidic emulsions.

We present a deep learning-based object detection and object tracking algorithm to study droplet motion in dense microfluidic emulsions. The deep learning procedure is shown to correctly predict the droplets' shape and track their motion at competitive rates as compared to standard clustering algorithms, even in the presence of significant deformations. The deep learning technique and tool developed in this work could be used for the general study of the dynamics of biological agents in fluid systems, such as moving cells and self-propelled microorganisms in complex biological flows. This article is part of the theme issue 'Progress in mesoscale methods for fluid dynamics simulation'.

Tracking droplets in soft granular flows with deep learning techniques.

The state-of-the-art deep learning-based object recognition YOLO algorithm and object tracking DeepSORT algorithm are combined to analyze digital images from fluid dynamic simulations of multi-core emulsions and soft flowing crystals and to track moving droplets within these complex flows. The YOLO network was trained to recognize the droplets with synthetically prepared data, thereby bypassing the labor-intensive data acquisition process. In both applications, the trained YOLO + DeepSORT procedure performs with high accuracy on the real data from the fluid simulations, with low error levels in the inferred trajectories of the droplets and independently computed ground truth. Moreover, using commonly used desktop GPUs, the developed application is capable of analyzing data at speeds that exceed the typical image acquisition rates of digital cameras (30 fps), opening the interesting prospect of realizing a low-cost and practical tool to study systems with many moving objects, mostly but not exclusively, biological ones. Besides its practical applications, the procedure presented here marks the first step towards the automatic extraction of effective equations of motion of many-body soft flowing systems.

Translocation Dynamics of High-Internal Phase Double Emulsions in Narrow Channels.

We numerically study the translocation dynamics of double emulsion drops with multiple close-packed inner droplets within constrictions. Such liquid architectures, which we refer to as HIPdEs (high-internal phase double emulsions), consist of a ternary fluid, in which monodisperse droplets are encapsulated within a larger drop in turn immersed in a bulk fluid. Extensive two-dimensional lattice Boltzmann simulations show that if the area fraction of the internal drops is close to the packing fraction limit of hard spheres and the height of the channel is much smaller than the typical size of the emulsion, the crossing yields permanent shape deformations persistent over long periods of time. Morphological changes and rheological response are quantitatively assessed in terms of the structure of the velocity field, circularity of the emulsion, and rate of energy dissipated by viscous forces. Our results may be used to improve the design of soft mesoscale porous materials, which employ HIPdEs as templates for tissue engineering applications.

Mesoscale modelling of droplets' self-assembly in microfluidic channels.

A recently proposed mesoscale approach for the simulation of multicomponent flows with near-contact interactions is employed to investigate the early stage formation and clustering statistics of soft flowing crystals in microfluidic channels. Specifically, we first demonstrate the ability of the aforementioned mesoscale model to accurately reproduce main mechanisms leading to the formation of two basic droplet patterns (triangular and hexagonal), in close agreement with experimental evidence. Next, we quantitatively evaluate the device-scale clustering efficiency of the crystal formation process by introducing a new orientational order parameter, based on the Delaunay triangulation and Voronoi diagrams analysis of the droplet patterns. The mesoscale computational approach employed in this work proves to be an efficient tool to shed new light on the complex dynamics of dense emulsions, from short-scale thin-film hydrodynamics, all the way up to global structure formation and statistics of the resulting droplets ensembles.

Multiparticle collision dynamics for fluid interfaces with near-contact interactions.

We present an extension of the multiparticle collision dynamics method for flows with complex interfaces, including supramolecular near-contact interactions mimicking the effect of surfactants. The new method is demonstrated for the case of (i) short range repulsion of droplets in close contact, (ii) arrested phase separation, and (iii) different pattern formation during spinodal decomposition of binary mixtures.

Ab initio accelerated molecular dynamics study of the hydride ligands in the ruthenium complex: Ru(H2)2H2(P(C5H9)3)2.

The dihydrogen complex Ru(H2)2H2(P(C5H9)3)2 has been investigated, via ab initio accelerated molecular dynamics, to elucidate the H ligands dynamics and possible reaction paths for H2/H exchange. We have characterized the free energy landscape associated with the H atoms positional exchange around the Ru centre. From the free energy landscape, we have been able to estimate a barrier of 6 kcal mol-1 for the H2/H exchange process. We have also observed a trihydrogen intermediate as a passing state along some of the possible reaction pathways.

Acoustic-propagation properties of methane clathrate hydrates from non-equilibrium molecular dynamics.

Given methane hydrates' importance in marine sediments, as well as the widespread use of seabed acoustic-signaling methods in oil and gas exploration, the elastic characterization of these materials is particularly relevant. A greater understanding of the properties governing phonon, sound, and acoustic propagation would help to better classify methane-hydrate deposits, aiding in their discovery. Recently, we have published a new nonequilibrium molecular-dynamics (NEMD) methodology to recreate longitudinal and transverse perturbations, observing their propagation through a crystalline lattice by various metrics, to study the underlying S- and P-wave velocities (achieving excellent agreement with experiment) [Melgar et al., J. Phys. Chem. 122(5), 3006-3013 (2018); ibid.150, 084101 (2019)]. Here, we apply these NEMD methods to methane-clathrate systems to study acoustic-propagation characteristics, as well as the lattice elastic behavior. In so doing, we determine S- and P-wave velocities in excellent accord with experiment; we also ascertain the allowable magnitude range of acoustic perturbation and establish a threshold for lattice breakup and hydrate decomposition. Interestingly, upon dissociation, we observe the formation of methane nanobubbles, which agrees with previous studies on the microscopic fundamentals of hydrate dissociation by various means.

Amplitude effects on seismic velocities: How low can we go?

α-quartz is one of the most important SiO2 polymorphs because it is the basis of very common minerals, especially for seabed materials with geoscientific importance. The elastic characterization of these materials is particularly relevant when the properties governing phonon and sound propagation are involved. These studies are especially interesting for oil exploration purposes. Recently, we published a new method that constitutes to the best of our knowledge the first attempt to recreate longitudinal and transversal perturbations in a simulation box to observe their propagation through the crystal by means of a set of descriptors [D. Melgar et al., J. Phys. Chem. C 122, 3006-3013 (2018)]. The agreement with the experimental S- and P-wave velocities was rather excellent. Thus, an effort has been undertaken to deepen the particularities of this new methodology. Here, bearing in mind this encouraging initial methodology-development progress, we deepen our knowledge of the particularities of this new methodology in presenting a systematic investigation of the implementation of the perturbation source. This includes new ways of creating the perturbation, as well as analyzing the possible effects the perturbation amplitude could have on the resultant velocities. In addition, different force fields were tested to describe the interatomic interactions. The lack of dependence of the seismic velocities on the way the perturbation is created and the perturbation amplitude, and the good agreement with the experimental results are the main reasons that allow the definition of this new methodology as robust and reliable. These qualities are consolidated by the physical behavior of the calculated velocities in the presence of vacancies and under stress. The development of this method opens up a new line of research of calculating seismic velocities for geophysically relevant materials in a systematic way, with full control not only on the sample features (composition, porosity, vacancies, stress, etc.) but also on the particularities of perturbation itself, as well as determining optimal system-response metrics.

Entropic lattice Boltzmann model for charged leaky dielectric multiphase fluids in electrified jets.

We present a lattice Boltzmann model for charged leaky dielectric multiphase fluids in the context of electrified jet simulations, which are of interest for a number of production technologies including electrospinning. The role of nonlinear rheology on the dynamics of electrified jets is considered by exploiting the Carreau model for pseudoplastic fluids. We report exploratory simulations of charged droplets at rest and under a constant electric field, and we provide results for charged jet formation under electrospinning conditions.

Mechanistic modelling of drug release from multi-layer capsules.

We propose a novel in silico model for computing drug release from multi-layer capsules. The diffusion problem in such heterogeneous layer-by-layer composite medium is described by a system of coupled partial differential equations, which we solve analytically using separation of variables. In addition to the conventional partitioning and mass transfer interlayer conditions, we consider a surface finite mass transfer resistance, which corresponds to the case of a coated capsule. The drug concentration in the core and through all the layers, as well as in the external release medium, is given in terms of a Fourier series that we compute numerically to describe and characterize the drug release mechanism.

Iontophoretic transdermal drug delivery: a multi-layered approach.

We present a multi-layer mathematical model to describe the transdermal drug release from an iontophoretic system. The Nernst-Planck equation describes the basic convection-diffusion process, with the electric potential obtained by solving the Laplace's equation. These equations are complemented with suitable interface and boundary conditions in a multi-domain. The stability of the mathematical problem is discussed in different scenarios and a finite-difference method is used to solve the coupled system. Numerical experiments are included to illustrate the drug dynamics under different conditions.