Droplet Tracking Research Articles

Engineered DR/NIR dual-emission carbonized polymer dots for simultaneous tracking of lipid droplets and lysosomes

Engineered DR/NIR dual-emission carbonized polymer dots for simultaneous tracking of lipid droplets and lysosomes

Efficient Real-Time Droplet Tracking in Crop-Spraying Systems

Spray systems in agriculture serve essential roles in the precision application of pesticides, fertilizers, and water, contributing to effective pest control, nutrient management, and irrigation. These systems enhance efficiency, reduce labor, and promote environmentally friendly practices by minimizing chemical waste and runoff. The efficacy of a spray is largely determined by the characteristics of its droplets, including their size and velocity. These parameters are not only pivotal in assessing spray retention, i.e., how much of the spray adheres to crops versus becoming environmental runoff, but also in understanding spray drift dynamics. This study introduces a real-time deep learning-based approach for droplet detection and tracking which significantly improves the accuracy and efficiency of measuring these droplet properties. Our methodology leverages advanced AI techniques to overcome the limitations of previous tracking frameworks, employing three novel deep learning-based tracking methods. These methods are adept at handling challenges such as droplet occlusion and varying velocities, ensuring precise tracking in real-time potentially on mobile platforms. The use of a high-speed camera operating at 2000 frames per second coupled with innovative automatic annotation tools enables the creation of a large and accurately labeled droplet dataset for training and evaluation. The core of our framework lies in the ability to track droplets across frames, associating them temporally despite changes in appearance or occlusions. We utilize metrics including Multiple Object Tracking Accuracy (MOTA) and Multiple Object Tracking Precision (MOTP) to quantify the tracking algorithm’s performance. Our approach is set to pave the way for innovations in agricultural spraying systems, offering a more efficient, accurate, and environmentally responsible method of applying sprays and representing a significant step toward sustainable agricultural practices.

Study on the microscopic characteristics of pentanol/highly active fuel spray based on high-speed droplet tracking velocimetry technology

Pentanol blending with highly reactive fuels for internal combustion engines holds considerable promise. The atomization of sprays closely influences the efficiency and combustion characteristics of the system. Based on Particle Tracking Velocimetry and high-speed shadow imaging technology, we propose a method termed High-speed Droplet Tracking Velocimetry for analyzing spray particle size. Prior to conducting our study, we validated the reliability of High-speed Droplet Tracking Velocimetry through comparisons with the Malvern and Phase Doppler methods. This study utilized long working distance micro high-speed shadow imaging technology in a visualized constant volume combustion chamber to investigate the spray micro-characteristics of pentanol blended with highly reactive fuels, including Fatty Acid Methyl Ester, Hydrogenated Catalytic Biodiesel, and Diesel. The results indicate that under all operating conditions, the droplets of P20H80 (the volume fraction of 20 % n-pentanol mixed with 80 % Hydrogenated Catalytic Biodiesel) are smaller and more uniformly distributed, demonstrating the minimum Sauter Mean Diameter, characteristic diameter, and width of droplet size distribution. The droplet velocities of P20H80 are the lowest, followed by P20Di80 (the volume fraction of 20 % n-pentanol mixed with 80 % Diesel), and P20B80 (the volume fraction of 20 % n-pentanol mixed with 80 % Fatty Acid Methyl Ester) exhibits the highest velocities. These velocity differences among the three fuels generally remain within 10 %-20 % across various injection pressures. P20Di80 shows the highest Reynolds and Weber numbers, followed by P20H80, with P20B80 having the smallest values due to its intermediate physical properties. The gaps between each fuel type range from 20 % to 30 %.

An acridone based fluorescent dye for lipid droplet tracking and cancer diagnosis

Lipid droplets (LDs) are spherical organelles that localize in the cytosol of eukaryotic cells. Different proteins are embedded on the surface of LDs, so LDs play a vital role in the physiological activities of cells. The dysregulation of LDs is associated with various human diseases, such as diabetes and obesity. Therefore, it is essential to develop a fluorescent dye that labels LDs to detect and monitor illnesses. In this study, we developed the compound BDAA12C for staining LDs in cells. BDAA12C exhibits excellent LD specificity and low toxicity, enabling us to successfully stain and observe the fusion of LDs in A549 cancer cells. Furthermore, we also successfully distinguished A549 cancer cells and MRC-5 normal cells in a co-culture experiment and in normal and tumour tissues. Interestingly, we found different localizations of BDAA12C in well-fed and starved A549 cancer cells and consequently illustrated the transfer of fatty acids (FAs) from LDs to mitochondria to supply energy for β-oxidation upon starvation. Therefore, BDAA12C is a promising LD-targeted probe for cancer diagnosis and tracking lipid trafficking within cells.

Automated droplet manipulation enabled by a machine-vision-assisted acoustic tweezer

Automated droplet manipulation holds great significance for various biochemical applications, including but limited to heavy metal ions detection. Despite notable progress, contactless-acoustic-tweezer-based automated droplet manipulation on superhydrophobic surfaces is rarely reported. In this work, we introduce a machine-vision-assisted acoustic tweezer (MVAAT) for automated and contactless manipulation of droplets on a superhydrophobic surface. The MVAAT generates ultrasound standing waves between an ultrasonic transducer (UST) and a superhydrophobic surface, inducing acoustic radiation force to facilitate contactless droplet manipulation on the superhydrophobic surface. An industrial-camera-based machine vision system is employed for real-time detection and tracking of droplets, providing precise droplet positions for automated droplet transportation and merging via a UST equipped on a Cartesian robot. Experimental results demonstrate that the proposed MVAAT can transport droplets of various volumes on a superhydrophobic surface along planned paths at considerably high velocities exceeding one centimeter per second. Furthermore, with the vision feedback, the MVAAT can reliably and precisely transport a droplet to a target position, even if the droplet fails to follow the UST during its movement. Moreover, automated merging and patterning of multiple droplets are achieved, showcasing the versatility of the MVAAT. Last, the MVAAT is applied for fluorescence-based Cu2+ detection to showcase its potential for practical biochemical analyses. The proposed MVAAT significantly expands the capability of ultrasound for droplet manipulation on superhydrophobic surfaces, promising numerous practical applications in biology and chemistry.

Combinatorial sample preparation platform for droplet-based applications in microbiology

Droplet microfluidics has demonstrated immense potential in microbiological studies due to its unique features, such as miniaturization, compartmentalization, and parallelization. Multiplexing droplet content allows the investigation of various experimental conditions in a highly parallelized manner. Yet, droplet library generation and tracking remain challenging in high-throughput screening. The introduction of distinct reagents into droplets necessitates precise control over droplet flow in a microfluidic chip, limiting the throughput to a few reagents. Additionally, tracking individual droplets is complex due to their fast dynamics. To address these challenges, we have developed a multiplexing platform for automated sample preparation, enabling on-demand merging and mixing of reagents for fine-tuning the sample compositions for droplet generation. A coding space with 169 optical barcodes can be realized by the pairwise combination of four fluorescence dyes at six concentration levels to encode droplet populations as required by the experimental design. A machine-learning algorithm has been employed to identify distinct droplet populations. As proof of concept, we conducted an antibiotic susceptibility assay on an E. coli strain to screen for susceptibility of four antibiotics and determine minimum inhibitory concentrations in one experiment. Utilizing the on-demand sample preparation, optical barcodes, and machine-learning analysis, our setup provides a rapid, straightforward, and reliable multiplexing capability for numerous microbial and biochemical applications.

Conversion of a regular patient room to negative pressure at Ancona university hospital (Italy). A combined experimental and numerical investigation

Negative pressure isolation rooms are employed to prevent the spread of infectious diseases to surrounding spaces. This study reports a combined experimental and numerical analysis aimed at reliably assessing the performance of hospital ventilation systems and effectively reducing the risk of airborne infections. The study appraises the enhancement of the safety conditions by retrofitting an existing, positive pressure room ventilation system with a negative pressure and recirculation one. The assessment consisted in experimental measurements of airborne particle concentration and Computational Fluid Dynamics (CFD) simulations with Lagrangian particle tracking of respiratory droplets. The ventilation performance improvement was quantified in terms of recovery rate, contaminant removal effectiveness, particle transfer toward adjacent spaces and probability of infection.Two experimental campaigns were carried out: an ISO standard recovery assessment and a test in real operational conditions. The negative pressure configuration significantly improved the safety inside the patient ward: the recovery rate for droplets with diameter larger than 5 μm increased by a factor three, and the contaminant removal effectiveness after 15 min increased from 45% to 81%. The numerical model for Computational Fluid Dynamics simulation was successfully validated, confirming the ability of the approach to reproduce the undergoing Physics. The negative pressure design eliminated the particle transmission to surrounding spaces, while 2.5% of the emitted respiratory droplets escaped the room in the original existing scenario. The probability of infection after 15 min exposure was reduced by a factor three and peak values reaching 15% were limited to the immediate vicinity of the patients.

Measuring arrangement and size distributions of flowing droplets in microchannels through deep learning using DropTrack

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.

Combustion and micro-explosion characteristics of biodiesel–ethanol–aluminum powder particles droplet under simulated air nitrogen–oxygen

Compared with aviation fuel, biodiesel has a smaller energy density, so the flight range of aircraft using biodiesel as aviation fuel is relatively short. Adding aluminum powder particles can significantly improve the energy density of fuel and then improve the flight range of aircraft, but the combustion characteristics of fuel will change after adding aluminum powder particles to fuel. In this experiment, a tubular furnace was used to simulate the ratio of nitrogen to oxygen in air, and the micro-explosion and combustion characteristics of BD60E40 droplet and BD60E40 droplet 10%AP (8 μm) under 700 and 750 °C were compared and studied by using moving droplet tracking technology. It is found that the greater the steam pressure inside the droplet, the greater the intensity of micro-explosion, and the greater the initial velocity of the secondary droplet after micro-explosion. In addition, it is found that the proportion of aluminum powder in droplets increases gradually during the combustion cycle, and the frequency and intensity of micro-explosion of droplets increase gradually. The instantaneous combustion micro-explosion intensity model and the average combustion intensity model were established. It was found that the average combustion micro-explosion intensity of droplets increased gradually with the increase in temperature through calculation.

Engineering of a NIR fluorescent probe for high-fidelity tracking of lipid droplets in living cells and nonalcoholic fatty liver tissues

LDs (Lipid droplets) are key organelles for lipid metabolism and storage, which are closely related to ferroptosis and fatty liver. Due to its small size and highly dynamic nature, developing high-fidelity fluorescent probes for imaging of LDs is crucial for observing the dynamic physiological processes of LDs and investigating LDs-associated diseases. Herein, we synthesized three dicyanoisophorone-based fluorescent probes (DCIMe, DCIJ, and DCIQ) with different electron-donating groups and studied their imaging performance for LDs. The results show that DCIQ is highly polarity sensitive and can perform high-fidelity imaging for LDs, with significantly better performance than DCIMe, DCIJ, and commercial LD probe BODIPY 493/503. Based on this, DCIQ was successfully applied to real-time observe the interplays between LDs and other organelles (mitochondria, lysosomes, and endoplasmic reticulum), and to image the dynamics of LDs with fast scanning mode (0.44 s/frame) and the generation of oleic acid-induced LDs with high-fidelity. Finally, DCIQ was used to study the changes of LDs in the ferroptosis process and nonalcoholic fatty liver disease tissues. Overall, this study provided a powerful tool for high-fidelity imaging of LDs in cells and tissues.

Extension of droplet/aerosol heat transfer model coupling Lagrangian approach in containment atmosphere under severe accident

Spray droplet dynamics are expected to significantly affect steam condensation, droplet evaporation, and global gas mixing in the containment atmosphere. During severe accidents, droplets may evaporate under high gas temperatures (>200 °C) such as hydrogen fire radiation, and water-soluble aerosols such as CsI and CsHO may dissolve in droplets. An extension of the droplet heat transfer model was conducted to account for these conditions and coupled with a Lagrangian approach for droplet tracking. The approach was comprehensively validated through single droplet experiments, demonstrating the feasibility of this new approach. Analyses of the effects of high atmosphere temperature, droplet curvature, and aerosol solute indicate that, 1) High gas temperature significantly reduces heat transfer, with the effect increasing at higher gas temperatures; 2) Droplet curvature enhances droplet evaporation, but this effect is limited to tiny droplets (<100 nm) compared to other effects; 3) The solute effect significantly impacts small droplet evaporation and dry aerosol growth, dominating the final equilibrium droplet diameter. The extended droplet/aerosol heat transfer model fills a gap in the knowledge of containment spray under severe accidents. A feasible way to approximate the minimum spray droplet size in the heat transfer model is to truncate or initialize the small droplet by the equilibrium diameter.

Real-time 4D tracking of airborne virus-laden droplets and aerosols

There is currently no real-time airborne virus tracking method, hindering the understanding of rapid virus changes and associated health impacts. Nano-digital in-line holographic microscopy (Nano-DIHM) is a lensless technology that can directly obtain the interference patterns of objects by recording the scattered light information originating from the objects. Here, we provide evidence for real-time physicochemical tracking of virus-laden droplets and aerosols in the air using desktop label-free Nano-DIHM. The virus interference patterns, as single and ensemble particles, were imaged by the Nano-DIHM with 32.5 ms resolution. The next-generation Stingray and Octopus software was used to automate object detection, characterization and classification from the recorded holograms. The detection system was demonstrated to detect active MS2 bacteriophages, inactivated SARS-CoV-2 and RNA fragments, and an MS2 mixture with metallic and organic compounds. This work demonstrates the feasibility of using Nano-DIHM to provide rapid virus detection to improve transmission management in real time.

Super-resolution dynamic tracking of cellular lipid droplets employing with a photostable deep red fluorogenic probe.

Lipid droplets (LDs) are critical organelles involved in many physiological processes in eukaryotic cells. To visualize and study LDs, particular the small/nascent LDs, the emerging super-resolution fluorescence imaging techniques with nanoscale resolution would be much more powerful in comparison to the conventional confocal/wide-field imaging techniques. However, directly limited by the availability of advanced LDs probes, super-resolution fluorescence imaging of LDs is a practically challenging task. In this context, a superior LDs fluorescent probe named Lipi-Deep Red is newly developed for structured illumination microscopy (SIM) super-resolution imaging. This fluorescent probe features with the advantages of strong deep red/NIR emission, fluorogenic character, high LDs specificity, and outstanding photostability. These advantages enable the fluorescent probe to be finely applied in SIM super-resolution imaging, e.g. time-lapse imaging (up to 1000 frames) to monitor the LDs dynamics at nanoscale (159nm), two-color time-lapse imaging to discover the nearby contact/interaction between LDs and mitochondria. Consequently, the fusion processes of LDs are impressively visualized at a high spatial and temporal resolution. Two kinds of contact models between LDs and mitochondria (dynamic contact and stable contact) newly proposed in the recent literatures are successfully revealed.

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.

Experimental analysis of droplet coalescence and transport mechanisms on a single vertical fiber

In mist filtration, fiber-based coalescers are an established form of filtering droplets contained in mist. The filtration process can be divided into different process steps, describing the impact of the droplets on fibers, the formation of fluid structures and the liquid transport. In order to investigate mechanisms inside depth filters on a microscopic level, investigations are often reduced to single fibers. In this work, the coalescence and transport mechanisms of axially distributed water droplets on a vertical fiber, subjected to gravity, are reported. This is done with the latest high-speed camera technology commercially available. Automated tracking of droplets is used for a frame-by-frame investigation of droplet position, size, and oscillation. Coalescence mechanisms describe the process of fluid formation. The first observed coalescence mechanism is identified by the coalescence of droplets contained in mist with an adhering droplet at the fiber. The second coalescence mechanism describes the coalescence of two closely spaced sessile droplets on a fiber. As a result, the newly formed droplet oscillates and can begin to drain. Furthermore, the coalescence process of a draining and sessile droplet is reported. Both the draining droplet and the second droplet involved in the coalescence event can be subject to oscillation. The given temporal high-resolution information about the droplet position and deformation improves the understanding of droplet coalescence on fibers and by that also its influence on transport processes and therefore droplet drainage. Observed transport mechanisms which take part in the drainage process are gravitational draining, droplet bouncing, and droplet sweeping.

Low-Gradient Magnetophoresis of Nanospheres and Nanorods through a Single Layer of Paper.

The possible magnetophoretic migration of iron oxide nanoparticles through the cellulosic matrix within a single layer of paper is challenging with its underlying mechanism remained unclear. Even with the recent advancements of theoretical understanding on magnetophoresis, mainly driven by cooperative and hydrodynamics phenomena, the contributions of these two mechanisms on possible penetration of magnetic nanoparticles through cellulosic matrix of paper have yet been proven. Here, by using iron oxide nanoparticles (IONPs), both nanospheres and nanorods, we have investigated the migration kinetics of these nanoparticles through grade 4 Whatman filter paper with a particle retention of 20-25 μm. By performing droplet tracking experiments, the real-time stained area growth of the particle droplet on the filter paper, under the influences of a grade N40 NdFeB magnet, were recorded. Our results show that the spatial and temporal expansion of the IONP stain is biased toward the magnet and such an effect is dependent on (i) particle concentration and (ii) particle shape. The kinetics data were first analyzed by treating it as a radial wicking fluid, and later the IONP distribution within the cellulosic matrix was investigated by optical microscopy. The macroscopic flow front velocities of the stained area ranged from 259 μm/s to 16 040 μm/s. Moreover, the microscopic magnetophoretic velocity of nanorod cluster was also successfully measured as ∼214 μm/s. Findings in this work have indirectly revealed the strong influence of cooperative magnetophoresis and the engineering feasibility of paper-based magnetophoretic technology by taking advantage of magnetoshape anisotropy effect of the particles.

Modelling of Droplet Capture in an Open-Cell Metal Foam at the Pore and Macroscopic Scales

Open-cell metal foams are often used in applications where particulate and/or droplet capture is important. Here a Computational Fluid Dynamics (CFD) modelling approach is described which models the metal foam at both the pore-scale and the macroscopic scale. At the pore-scale, the detailed internal geometry of the foam is included and the flow field and droplet tracking and capture is modelled explicitly. At this scale, a coefficient is found for each metal foam that relates the distance a droplet can freely travel through the foam to both the droplet diameter and the Darcian velocity in the porous medium. Then, at the macroscopic scale, the coefficient from the pore-scale droplet capture simulations is used in a novel stochastic particle extinction model. Here, the droplets travel through a porous zone and are removed from the model, the probability of which is determined by the coefficient from the pore-scale modelling. A test case is described in which the macroscopic model is verified against the pore-scale model with acceptable levels of accuracy.

Improving droplet microfluidic systems for studying single bacteria growth.

Antimicrobial resistance remains a global threat with ~ 5 million deaths in 2019 alone and 10 million deaths projected every year by 2050. Current tools employed in the analysis of bacteria can be time inefficient, leading to delayed diagnosis and treatment. In this work, we develop a microfluidic setup capable of bacteria incubation and detection of growth in ~ 2h. We fabricated polydimethylsiloxane (PDMS) microchips via soft lithography, enclosed microchannels by plasma bonding to glass, and utilized PDMS blocks for simplified connection of devices to a flow system. We generated uniform droplets enclosing zero, one or two bacteria within our devices, and incubated droplet-encapsulated bacteria with 100 × lower concentrations of a fluorescence probe of bacterial growth compared to prior work. We assessed bacterial growth via laser induced fluorescence after room temperature incubation for 2h and obtained a range of signals corresponding to droplets with or without bacteria. Our devices allow for online droplet incubation, monitoring, detection, and tracking. Developing microfluidic chips for single bacteria studies will improve the analysis and treatment of antimicrobial resistance.

Planar Interferometric Tracking of droplets in evaporating conditions

An effective Lagrangian Planar Interferometric Tracking (PIT) processor is proposed to track the size and path of multiple droplets, with spray droplet diameters (20–150 µm) and volumetric concentrations (approx300 drops/cm^3) consistent with industrial applications, produced by an ultrasonic atomiser in evaporating conditions. A test facility was developed where liquid droplets are exposed to a temperature gradient in a co-axial air flow, where the outer stream is preheated to the desired temperature (288–550 K). The PIT method builds on a TSI Global Sizing Velocimtery measurement technique and allows to detect, size and follow the path of droplets which were otherwise discarded or mis-analysed by the commercial software. The methodology was first tested under non-evaporating conditions, and multiple sources of errors, some common to most planar interferometric techniques, were identified and their order of magnitude and impact on final droplet measurement assessed. The main source of error is related to the out-of-plane motion of the droplets and the time they spend in the measurement volume. For non-evaporating conditions, measured data can be processed to filter out this source of error. In evaporating conditions, a novel method for assessing the impact of measurement error with respect to droplet evaporation and measurement timescales is defined. The PIT method allowed tracking of individual methanol droplets entrained within an airflow heated to 495 K and determining their size reduction under evaporating conditions. Measured droplet evaporation rates were then compared against those predicted by an iterative evaporation model, and a very good agreement was found between the modelled and measured estimates.Graphical abstract

Development of a 3D Eulerian/Lagrangian Aircraft Icing Simulation Solver Based on OpenFOAM

A 3D icing simulation code is developed in the open-source CFD toolbox OpenFOAM. A hybrid Cartesian/body-fitted meshing method is used to generate high-quality meshes around complex ice shapes. Steady-state 3D Reynolds-averaged Navier-Stokes (RANS) equations are solved to provide the ensemble-averaged flow around the airfoil. Considering the multi-scale nature of droplet size distribution, and more importantly, to represent the less uniform nature of the Super-cooled Large Droplets (SLD), two droplet tracking methods are realized: the Eulerian method is used to track the small-size droplets (below 50 m) for the sake of efficiency; the Lagrangian method with random sampling is used to track the large droplets (above 50 m); the heat transfer of the surface overflow is solved on a virtual surface mesh; the ice accumulation is estimated via the Myers model; finally, the final ice shape is predicted by time marching. Limited by the availability of experimental data, validations are performed on 3D simulations of 2D geometries using the Eulerian and Lagrangian methods, respectively. The code proves to be feasible and accurate enough in predicting ice shapes. Finally, an icing simulation result of the M6 wing is presented to illustrate the full 3D capability.