Droplet microfluidics has emerged as highly relevant technology in diverse fields such as nanomaterials synthesis, photonics, drug delivery, regenerative medicine, food science, cosmetics, and agriculture. While significant progress has been made in understanding the fundamental mechanisms underlying droplet generation in microchannels and in fabricating devices to produce droplets with varied functionality and high throughput, challenges persist along two important directions. On one side, the generalization of numerical results obtained by computational fluid dynamics would be important to deepen the comprehension of complex physical phenomena in droplet microfluidics, as well as the capability of predicting the device behavior. Conversely, truly three-dimensional architectures would enhance microfluidic platforms in terms of tailoring and enhancing droplet and flow properties. Recent advancements in artificial intelligence (AI) and additive manufacturing (AM) promise unequaled opportunities for simulating fluid behavior, precisely tracking individual droplets, and exploring innovative device designs. This review provides a comprehensive overview of recent progress in applying AI and AM to droplet microfluidics. The basic physical properties of multiphase flows and mechanisms for droplet production are discussed, and the current fabrication methods of related devices are introduced, together with their applications. Delving into the use of AI and AM technologies in droplet microfluidics, topics covered include AI-assisted simulations of droplet behavior, real-time tracking of droplets within microfluidic systems, and AM-fabrication of three-dimensional systems. The synergistic combination of AI and AM is expected to deepen the understanding of complex fluid dynamics and active matter behavior, expediting the transition toward fully digital microfluidic systems.

In microfluidic systems, droplets undergo intricate deformations as they traverse flow-focusing junctions, posing a challenging task for accurate measurement, especially during short transit times. This study investigates the physical behavior of droplets within dense emulsions in diverse microchannel geometries, specifically focusing on the impact of varying opening angles within the primary channel and injection rates of fluid components. Employing a sophisticated droplet tracking tool based on deep-learning techniques, we analyze multiple frames from flow-focusing experiments to quantitatively characterize droplet deformation in terms of ratio between maximum width and height and propensity to form liquid with hexagonal spatial arrangement. Our findings reveal the existence of an optimal opening angle where shape deformations are minimal and hexagonal arrangement is maximal. Variations of fluid injection rates are also found to affect size and packing fraction of the emulsion in the exit channel. This paper offers insight into deformations, size, and structure of fluid emulsions relative to microchannel geometry and other flow-related parameters captured through machine learning, with potential implications for the design of microchips utilized in cellular transport and tissue engineering applications.

We introduce a two-step, fully reversible process designed to project the outer shape of a generic droplet onto a lower-dimensional space. The initial step involves representing the droplet's shape as a Fourier series. Subsequently, the Fourier coefficients are reduced to lower-dimensional vectors by using autoencoder models. The exploitation of the domain knowledge of the droplet shapes allows us to map generic droplet shapes to just two-dimensional (2D) space in contrast to previous direct methods involving autoencoders that could map it on minimum eight-dimensional (8D) space. This six-dimensional (6D) reduction in the dimensionality of the droplet's description opens new possibilities for applications, such as automated droplet generation via reinforcement learning, the analysis of droplet shape evolution dynamics, and the prediction of droplet breakup. Our findings underscore the benefits of incorporating domain knowledge into autoencoder models, highlighting the potential for increased accuracy in various other scientific disciplines.

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.

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.

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.

Fluid flows hosting electrical phenomena are the subject of a fascinating and highly interdisciplinary scientific field. In recent years, the extraordinary success of electrospinning and solution-blowing technologies for the generation of polymer nanofibers has motivated vibrant research aiming at rationalizing the behavior of viscoelastic jets under applied electric fields or other stretching fields including gas streams. Theoretical models unveiled many original aspects in the underpinning physics of polymer solutions in jets and provided useful information to improve experimental platforms. This review examines advances in the theoretical description and numerical simulation of polymer solution jets in electrospinning and solution blowing. Instability phenomena of electrical and hydrodynamic origin, which play a crucial role in the relevant flow physics, are highlighted. Specifications leading to accurate and computationally viable models are formulated. Electrohydrodynamic modeling, theories on jet bending instability, recent advances in Lagrangian approaches to describe the jet flow, including strategies for dynamic refinement of simulations, and effects of strong elongational flow on polymer networks are reviewed. Finally, the current challenges and future perspectives in the field are outlined and discussed, including the task of correlating the physics of the jet flows with the properties of relevant materials, as well as the development of multiscale techniques for modeling viscoelastic 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.

Electrospinning is a nanotechnology process whereby an external electric field is used to accelerate and stretch a charged polymer jet, so as to produce fibers with nanoscale diameters. In quest of a further reduction in the cross section of electrified jets hence of a better control on the morphology of the resulting electrospun fibers, we explore the effects of an external rotating electric field orthogonal to the jet direction. Through intensive particle simulations, it is shown that by a proper tuning of the electric field amplitude and frequency, a reduction of up to a 30% in the aforementioned radius can be obtained, thereby opening new perspectives in the design of future ultra-thin electrospun fibers. Applications can be envisaged in the fields of nanophotonic components as well as for designing new and improved filtration materials.

We investigate the effects of nanoparticles on the onset of varicose and whipping instabilities in the dynamics of electrified jets. In particular, we show that the non-linear interplay between the mass of the nanoparticles and electrostatic instabilities, gives rise to qualitative changes of the dynamic morphology of the jet, which in turn, drastically affect the final deposition pattern in electrospinning experiments. It is also shown that even a tiny amount of excess mass, of the order of a few percent, may more than double the radius of the electrospun fiber, with substantial implications for the design of experiments involving electrified jets as well as spun organic fibers. Copyright (C) EPLA, 2017

We study the effects of a controlled gas flow on the dynamics of electrified jets in the electrospinning process. The main idea is to model the air drag effects of the gas flow by using a nonlinear Langevin-like approach. The model is employed to investigate the dynamics of electrified polymer jets at different conditions of air drag force, showing that a controlled gas counterflow can lead to a decrease of the average diameter of electrospun fibers, and potentially to an improvement of the quality of electrospun products. We probe the influence of air drag effects on the bending instabilities of the jet and on its angular fluctuations during the process. The insights provided by this study might prove useful for the design of future electrospinning experiments and polymer nanofiber materials.

Nowadays, several models of unidimensional fluid jets exploit discrete element methods. In some cases, as for models aiming at describing the electrospinning nanofabrication process of polymer fibers, discrete element methods suffer a non-constant resolution of the jet representation. We develop a dynamic mesh- refinement method for the numerical study of the electro-hydrodynamic behavior of charged jets using discrete element methods. To this purpose, we import ideas and techniques from the string method originally developed in the framework of free-energy landscape simulations. The mesh-refined discrete element method is demonstrated for the case of electrospinning applications.

Program summary We present the open-source computer program JETSPIN, specifically designed to simulate the electro-spinning process of nanofibers. Its capabilities are shown with proper reference to the underlying model, as well as a description of the relevant input variables and associated test-case simulations. The various interactions included in the electrospinning model implemented in JETSPIN are discussed in detail. The code is designed to exploit different computational architectures, from single to parallel processor workstations. This paper provides an overview of JETSPIN, focusing primarily on its structure, parallel implementations, functionality, performance, and availability.

We investigate the effects of dissipative air drag on the dynamics of electrified jets in the initial stage of the electrospinning process. The main idea is to use a Brownian noise to model air drag effects on the uniaxial elongation of the jets. The developed numerical model is used to probe the dynamics of electrified polymer jets at different conditions of air drag force, showing that the dynamics of the charged jet is strongly biased by the presence of air drag forces. This study provides prospective beneficial implications for improving forthcoming electrospinning experiments. (C) 2015 Elsevier Ltd. All rights reserved.

We present a nonlinear Langevin model to investigate the early-stage dynamics of electrified polymer jets in electrospinning experiments. In particular, we study the effects of air drag force on the uniaxial elongation of the charged jet, right after ejection from the nozzle. Numerical simulations show that the elongation of the jet filament close to the injection point is significantly affected by the nonlinear drag exerted by the surrounding air. These results provide useful insights for the optimal design of current and future electrospinning experiments.

Electrospun polymer jets are imaged for the first time at an ultra-high rate of 10 000 frames per second, investigating the process dynamics, and the instability propagation velocity and displacement in space. The polymer concentration, applied voltage bias and needle-collector distance are systematically varied, and their influence on the instability propagation velocity and on the jet angular fluctuations is analyzed. This allows us to unveil the instability formation and cycling behavior, and its exponential growth at the onset, exhibiting radial growth rates of the order of 10(3) s(-1). Allowing the conformation and evolution of polymeric solutions to be studied in depth, high-speed imaging at the sub-ms scale shows significant potential for improving the fundamental knowledge of electrified jets, leading to finely controllable bending and solution stretching in electrospinning, and consequently better designed nanofiber morphologies and structures.