The human innate immune response, particularly the type-I interferon (IFN) response, is highly robust and effective first line of defense against virus invasion. IFN molecules are produced and secreted from infected cells upon virus infection and recognition. They then act as signaling/communication molecules to activate an antiviral response in neighboring cells so that those cells become refractory to infection. Previous experimental studies have identified the detailed molecular mechanisms for the IFN signaling and response. However, the principles underlying how host cells use IFN to communicate with each other to collectively and robustly halt an infection is not understood. Here we take a multiplex network modeling approach to provide a theoretical framework to identify key factors that determine the effectiveness of the IFN response against virus infection of a host. In this approach, we consider the virus spread among host cells and the interferon signaling to protect host cells as a competition process on a two-layer multiplex network. We focused on two types of network topology, i.e., the Erdős-Rényi (ER) network and the Geometric Random (GR) network, which represent the scenarios when infection of cells is mostly well mixed  and when infection is spatially segregated , respectively. We show that in general, the IFN response works effectively to stop viral infection when virus infection spreads spatially . Importantly, we show that the effectiveness of the IFN response is robust against large variations in the distance of IFN diffusion as long as IFNs diffuse faster than viruses and they can effectively induce antiviral responses in susceptible host cells. This suggests that the effectiveness of the IFN response is insensitive to the specific arrangement of host cells in peripheral tissues. Thus, our work provides a quantitative explanation of why the IFN response can serve an effective and robust response in different tissue types to a wide range of viral infections of a host. Virus infections and the resulting diseases are major challenges that our society faces today . One impIFN molecules belong to a group of signaling proteins, known as cytokines, used by the immune system for cell-to-cell communication and induction of protective response. Upon infection, detection of viral RNA/DNA in the host cell triggers a signaling cascade and gene regulation, resulting in the production of IFNs . These Iin vitro experiments and in vivo systematic infection dynamics , infected cells (I), protected cells (P), and recovered/dead cells (R). The two layers share nodes (representing host cells) in the network; however, the two layers may have different edges that represent the infection or the protection of susceptible cells in the infection layer and the protection layer, respectively. The nodes have average degrees of In general, the multiplex network is modeled by a family of graphs  network .G   graph G  in which G , in whicg et al. .The following ordinary differential equations (ODEs) describe the mean field model of the infection and protection processes we consider in the networks:I) die at per capita rate γ to become cells in the R class. We assume that protected cells remain protected for simplicity, although anti-viral response in protected cells can be switched off over time  are infected at rate β or become protected at rate φ. Since we mainly focus on the initial infection dynamics, generation and death of susceptible cells are ignored. Infected cells  are random and there is no spatial structure in the contacts. These assumptions are reasonable for infections where cells move and contact with other cells (through viruses and IFNs) roughly randomly, for example, HIV infection in the blood. In our multiplex network model, the topologies of the graphs in the two layers, i.e., the contact structure between cells, can be explicitly modeled, in contrast to well-mixed models or single-layer network models. This allows us investigate how the IFN signaling through the protection layer competes with virus infection through the infection layer at the level of individual infected cells.R(∞) and P(∞), respectively) changes with the infectivity of the virus . When the two layers are independent of each other, the subset of target cells that an IFN molecule can reach is independent from the subset that a virus (produced from the same cell as the IFN molecule) reaches, and thus there is no direct competition for target cells between viruses and IFNs at the individual infected cell level. We found that the predicted infection threshold value for virus infectivity, βc, i.e., the threshold value that viruses can cause sustained infection in a host, is independent of the parameter that governs the IFN protection of target cells, i.e., φ. On the other hand, when the two layers are identical , IFN molecules will reach to the same subset of target cells as the viruses produced from the same infected cell. In this case, the infection threshold becomes much larger than the threshold in the absence of IFN response, suggesting that IFN can prevent virus infection  and protected cells , IFN protection of susceptible cells works much more effectively in the GR network than in the ER network. As shown in RF; defined in Methods) is sufficiently high, e.g., when RF > RI , and assumed that the individual-cell effectiveness of the IFN signaling, RF, is constant. In this way, when the radius of IFN diffusion increases, the average degree of nodes in the protection layer (kF) increases; however, the protection rate per contact decreases. We explored how the final fraction of infection R(∞) changes with the ratio of the radius of IFN diffusion over the radius of virus diffusion, rF/rI. We found that there exists an optimal ratio, such that the total fraction of infection is minimized  in the GR network. Due to differences in the viscosity of the fluid in the tissue and the layout of target cells, the ratio of the IFN diffusion over the virus diffusion and thus the ratio of the numbers of target cells they reach may differ in different tissue compartments. Below, we evaluate how the effectiveness of the overall IFN response changes with changes in these ratios. In the analysis, we varied the radius of the IFN diffusion in the protection layer , the lower the final fraction of infection R(∞) (k = 0.1 in the gamma distribution in P(∞) remains similar across simulations  . This isulations . TherefoHere, we use a multiplex network approach to show how the collective host cell IFN response can effectively and robustly halt/suppress virus spread especially when viruses spread spatially. For a wide variety of viral infections, including influenza infection , HIV infDuring systematic infection, viral infection process can be spatial or non-spatial. For viruses like HIV, infection in the blood and in the lymph nodes occurs among host cells that move around and contact each other randomly, the infection process may be better modeled using a random (ER) network. We show that in this case, the critical parameter that determines the effectiveness of IFN protection of target cells is the similarity between the infection layer and the protection layer . The higin vivo and in vitro observations. For example, imaging of liver biopsy from patients chronically infected with hepatitis C virus (HCV) showed that HCV infected cells form clusters and that IFN stimulated genes are highly expressed in infected cells as well as the surrounding susceptible cells. This strongly suggests effective IFN response to constrain cell-to-cell spatial spread in the liver  network and the spatial (GR) network. An actual infection ood flow , 32. Simood flow . FurtherGiven that the IFN response is a highly optimized and highly effective general response against viruses , we arguYH and RK derived and analyzed the theoretical models. YH conducted the computer simulations. All authors analyzed the data, wrote and edited the manuscript and contributed to the development of ideas.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Automatic vehicle detection and counting are considered vital in improving traffic control and management. This work presents an effective algorithm for vehicle detection and counting in complex traffic scenes by combining both convolution neural network (CNN) and the optical flow feature tracking-based methods. In this algorithm, both the detection and tracking procedures have been linked together to get robust feature points that are updated regularly every fixed number of frames. The proposed algorithm detects moving vehicles based on a background subtraction method using CNN. Then, the vehicle’s robust features are refined and clustered by motion feature points analysis using a combined technique between KLT tracker and K-means clustering. Finally, an efficient strategy is presented using the detected and tracked points information to assign each vehicle label with its corresponding one in the vehicle’s trajectories and truly counted it. The proposed method is evaluated on videos representing challenging environments, and the experimental results showed an average detection and counting precision of 96.3% and 96.8%, respectively, which outperforms other existing approaches. Detecting and counting vehicles on the road are very important tasks for the traffic information analysis that can be used in traffic control and management to ensure a safe transportation system. Recently , vision-The most recent studies in intelligent transportation systems focus on vehicle detection ,10,11,12Yang and Qu proposed a new detection and counting technique that combined the detection and tracking processes . In 13]13], the In , a backgIn , a fast Most vehicle counting strategies depend mainly on background modeling with good accuracy, yet some false positive and false negative results have occurred when the background has large changes. Recently, the convolution neural networks have been used in object detection with better foreground and background discrimination because of their powerful capabilities in extracting low, mid, and high-level image features. Better foreground detection by CNN can improve tracking and counting accuracy. CNN can demonstrate excellent performance, but the performance is not perfect. The tracking and counting accuracy can be enhanced by reducing false-positive and false-negative results using a refining process to improve the foreground detection by CNN. Thus, a new framework for vehicle counting is suggested in this paper based on the collaboration process between collected detection information using convolutional neural networks and tracking information using optical flow. The work depends on the feature point’s information analysis between the fixed number of frames, and using the temporal information of the detection and tracking feature points between the framesets to achieve better counting decision. In this work, a new detection and counting method is proposed by developing a three-step approach in each frameset, as explained in The proposed framework contains three functional steps in each frameset, as shown in Recently ,4,16,17,The network has 20,243 trainable weights that learned by back-propagation with a cross entropy error function:N frames, as shown in In general, the training process of CNN in the vehicle detection task requires a large annotated training set under different environments; it is highly difficult to obtain such large manually annotated dataset. In this work, we combine the segmentation result with connected component labeling and modify the training step to tackle this problem. As we focused on vehicle detection, primitive training is implemented using the foreground and background of the baseline scene in ChangeDetection.net dataset (CDnet 2014). For a different video, we need only to efficiently train the background that can be extracted automatically using the temporary median of each pixel for 150 frames of the video without changing the primitive foreground patches. The point here is to train the background efficiently, so the network can easily extract the foreground vehicles on different scenes. The detection process has been calculated in the first frame every fixed number of frames, Selecting good features of the bounding boxes that resulted from the detection step is necessary for robustly tracking feature points across frames. While the Harris detector is the most famous corner detector, Shi and Tomasi’s detector perform better than the Harris corner detector . Here, aN frames. The new extracted feature points from the detection step and the old tracked features in the latter frame of the former N frames are combined. Hence, the system is updated every N frames by integrating both the tracking and detection feature points.The periodically updated vehicle feature points are very important to guarantee tracking them for a long time because these features may disappear as a result of illumination change and out-of-plane rotation. So, the detection process is repeated regularly every fixed number of frames, t to frame The extracted rectangular boxes from the obtained detection result are refined and clustered to achieve better counting results by discarding the noisy part and clustering the detected vehicles. First, the feature points are extracted within the detected bounding boxes, and the optical flow-based feature point tracking is implemented for tracking the vehicle features point. The detected corner point is tracked from frame approach .C e.g C matches a feature point t to frame The optical flow results in the first frame pairs are a set of vectors The noisy detections tend to result in short-lived trackers . In thisF relates to the foreground. K value is the number of detected regions. Each feature point is a vertex X and Y coordinates in the current frame, Nth frame.After refining the detection result, the number of detected regions will only contain the foreground vehicle features as shown in K-means clustering is an unsupervised learning, computationally efficient algorithm for large datasets. Initially, k samples, serving as the initial centroids, are chosen randomly to approximate the centroids of the initial clusters (K is a positive integer number). Simply, K-means clustering is an algorithm to group the objects based on features into a K number of groups. The grouping process is done by minimizing the sum of squares of distances (Euclidean squared distance) between data and the corresponding cluster centroid. The K-means algorithm will be carried out by executing the following three steps below until convergence (Iterate until stable) is obtained.Determine the centroid coordinates.Determine the distance between the centroids and each data feature.Group the data based on the minimum distance to find the closest centroid.In this work, K-means clustering is used after vehicle detection based on a convolutional neural network and vehicle refinement by the optical flow information in the first frame of each frameset. K-means clustering helps to achieve better counting results by grouping the vehicle features points based on their displacement, angle, and coordinates. Good detection results using CNN and vehicle detection refinement using the optical flow information give us the correct value of K that is used in the K-means algorithm. The selection process of the K value is an important issue to improve the vehicle clustering process for achieving perfect counting performance. This clustering information in the current frameset will be used with the last frame information in the previous frameset to check the connecting trajectory and make the counting decision. K value is evaluated in the first frame of each frameset and considered the value of the detected vehicles after refining the detection result (the number of refined bounding boxes).S and the bounding boxes for the last frame in frameset The detection vehicle regions with their most robust feature points inside each cluster will be accompanied by a bounding box created according to feature points’ coordinates in each cluster. Each one of the bounding boxes with their feature points will take a unique ID that tracked within the frameset as shown in This step to judge the possibility of assigning the same ID for the new detected vehicle or it is a new vehicle with a different ID. For this purpose, the following two cases are considered.1. Maintaining the same vehicle clusterIn this case, the intersection area is greater than a predetermined 2. Creating a new vehicle clusterThis case established when there is no intersection area or the estimated intersection area is less than or equal to To solve the problem of fixed bounding box size, we propose an adaptive bounding box using the output points from the optical flow, every frame after getting the strongest points as shown in N = 10 for achieving better tracking and counting result using KLT [The proposed method for detection and counting is evaluated and compared with four state of the art algorithms ,13,14,15sing KLT . We examThe proposed method offers a robust multi-vehicles detection and counting system.Quantitative evaluation of the detection and counting will be discussed and compared with recent approaches ,13,14,15The detection accuracy is evaluated using quantitative performance metrics that have been used as a standard evaluation , known aRecall refers to the ratio of accurately detected vehicle pixels to the number of actual vehicle pixels that include the number of false negative pixels (FN).The counting precision can be defined asIn the sake of comparison with the recent counting techniques, we assume focusing on data sets containing vehicle objects only because the counting strategies based on background subtraction technique cannot discriminate between the vehicles and personal objects. The first experiment focused on two sequences from GRAM dataset , M-30 anIn the second experiment, four video sequences from CDnet2014 dataset  are usedThe counting precision results for approaches focused on the detection and counting are reported in Although the results of  showed gEstimating the number of vehicles is the main target for traffic information analysis. The proposed approach was tested on different scenes, including night time, day time, intermittent vehicle motion, and crowded. Although the suggested strategy achieved a sufficient counting accuracy, it depends on the training process in the detection part, which tends to cause time delay in real applications. Another concern in our work is the vehicles occlusion that occurs in overlapped vehicles regions cases.In this work, we focused on achieving accurate vehicle detection and counting. Besides, the strategy relies on multi-vehicle tracking based on optical flow to track the moving vehicles and counting them based on the detection and tracking results. The proposed algorithm may cause vehicle mis-counting in some scenes with heavy occlusions, such as tram station scene when two vehicles occluded for a long time. The congestion and intensive traffic situations in urban traffic situations often make more obstacles for the proposed approach in achieving accurate vehicle detection, tracking, and counting.In this work, a new and robust vehicle detection and counting approach was proposed by developing a three-step approach. The proposed method first detects the vehicles using a CNN-based classifier with connected component labeling, then vehicle feature motion is analyzed to remove the noise and cluster the vehicles. Finally, a way to assign the detected vehicles with its corresponding cluster is introduced, to ensure a non-repeated counting process, by considering the intersection area between the detected and tracked point information. Experimental results on different datasets showed that the proposed strategy outperforms other existing methods.


Electrochemically gas-evolving systems are utilized in alkaline water electrolysis, hydrogen production, and many other applications. To design and optimize these systems, high-fidelity models must account for electron-transfer, chemical reactions, thermodynamics, electrode porosity, and hydrodynamics as well as the interconnectedness of these phenomena. Further complicating these models is the production and presence of bubbles. Bubble nucleation naturally occurs due to the chemical reactions and impacts the reaction rate. Modeling bubble growth requires an accurate accounting of interfacial mass transfer. When the bubble becomes large, detachment occurs and the system is modeled as a two-phase flow where the bubbles can then impact material transport in the bulk. In this paper, we review the governing mathematical models of the physicochemical life cycle of a bubble in an electrolytic medium from a multiscale, multiphysics viewpoint. For each phase of the bubble life cycle, the prevailing mathematical formulations are reviewed and compared with particular attention paid to physicochemical processes and the impact the bubble. Through the review of a broad range of models, we provide a compilation of the current state of bubble modeling in electrochemically gas-evolving systems. A particularly challenging feature of electrochemical systems is the generation of bubbles at the surface of the electrodes. These bubbles, a by-product of the chemical reactions taking place, can both hinder and aid the efficiency of the cell. While the rising of the produced gas bubbles promotes flow circulation in the cell, gas production has unwanted effects such as changing the conductivity of the bulk liquid and reducing the active area of the electrode surface.5 Despite the importance of electrochemical gas-evolving systems and their frequent applications, further development and incorporation of several underlying physicochemical phenomena, such as bubble nucleation, electrical field and Marangoni stress effects on the bubble, and liquid-bubble momentum and mass exchanges, are required to accurately model the bubbles in the electrolyte.9 While all of these effects have been studied in isolation, their interactions and importance to other scales demands a multiscale, multi-physics framework to enable a comprehensive description of electrochemically gas-evolving system.Initially, gases are in a dissolved state inside the electrolyte’s liquid phase. However, due to the continuous production of these gases by the chemical reactions, the electrolytic medium becomes supersaturated and gas bubbles start forming at the electrode surface.From its birth to eventual bursting, each phase of the life cycle of a bubble involves multiple phenomena at different scales. Understanding nucleation, the generation of the bubble, at the electrode surface requires the consideration of the electrolytic medium states and processes prior to the nucleation stage. Specifically, evaluation of the amount of the dissolved gases and the supersaturation condition are needed to determine nucleation rates. The next stage in the bubble’s evolution cycle is bubble growth at the electrode surface, which ends at the stage of bubble detachment. Accurately modeling the growth of a bubble is impacted by hydrodynamics and transport of species through the interface. After the bubble detaches from the electrode, the rising stage takes place where the bubble starts ascending through the electrolyte phase. Once the bubble has reached the electrolyte free surface, the bubble undergoes bursting, the final stage. During these five main stages, multiple, interconnected physics such as chemical reactions by the electrode current kinetics, generation of gases, species transport, and two-phase fluid interactions take place.13 These techniques, however, are limited to systems with either a single or small number of bubbles, or are restricted to investigating a single stage of the bubble life cycle, for example bubble growth dynamics, or bubble flow in two-phase system.16 Alternatively, numerically modeling the behavior of bubbles from a multiscale, multi-physics viewpoint provides an opportunity to investigate the contributions of multiple physics phenomena simultaneously throughout the life cycle of a large number of bubbles. Deriving models to predict the gas bubble evolution in electrochemical systems is challenging since the associated physics occur over vastly different time and spatial scales, ranging from nanometers, such as electronic transfer process that occurs at femtoseconds, to meters where hydrodynamics and species interaction effects take place on the order of seconds to hours. The nonlinearity in gas bubble behavior and the intrinsic physicochemical hydrodynamics further complicates models of the gas-evolving systems. For instance, understanding the bubble detachment from the electrode surface requires a careful consideration of the interfacial forces, sliding motion, coalescence, and jump off behavior of the bubble at the electrode surface.17 Predicting and simulating the electrochemically gas-evolving systems entails a successfully bridging technique that couples the multiscale physics within the system including bubble formation, growth, detachment, interfacial characteristics, ion transport, transport of species, electrolyte properties, and two phase dynamics. Although previous investigations have modeled the multiple scales of electrochemical systems, bubbles are not fully integrated into the models.22Extensive experimental work has been done to study the electrochemically produced gas-bubbles by means of employing Particle Image Velocimetry and Laser Doppler Anemometry techniques.24 These consequences make incorporating gas bubble behavior into electrochemical systems crucial to modeling from a multiscale and multiphysics viewpoint. The multiphysics modeling using multiscale approach is a rapidly developing research area due to its ability in advancing the deep understanding of fundamental sciences. However to date, there has not been a comprehensive review of the existing isolated phenomena models and how they interact within the context of bubbles in electrochemically gas-evolving systems. In this paper, we present a review of the physicochemical aspects of the different phases of the bubble’s life cycle from a multiscale and multiphysics viewpoint.Accurately accounting for bubbles in the design of electrochemical systems helps reduce the cell voltage drop and energy loss, increases the mass transfer rates, and protects the electrode surface.We will provide a mathematical reference of multiscale formulations, to help researchers in modeling bubbles in electrochemically gas-evolving systems simulations. This starts with a discussion of multiscale coupling principles and introduction of the multiphysics models in the electrochemically gas-evolving systems. The next section compares nucleation theories at different scales and presents the associated mathematical framework. After bubble nucleation, we discuss the physical insight and the governing mathematical formulations of bubble growth. Bubble growth is due in part to the increased ion concentrations that are produced by the electrochemical kinetics near the electrodes. We present the three prevailing theories of electrochemical kinetics. Ultimately, bubble growth leads to detachment from the electrode surface, so we present detachment models where interfacial forces play a key role. Next, we present the dynamics of the bubble and transport of species through the interface as it rises in the two-phase flow section. Bubble bursting upon reaching the surface is not presented because it generally occurs outside the active domain of the electrochemical system. Finally, we provide an overview about the recent progress in the area of the gas-evolving systems and potential directions that could be pursued by implementing multiscale multi-physics modeling.−9, 10−4 and 10−3 meters.13Multiscale modeling aims to analytically or numerically link different physical models that span different degrees of simplifying assumptions and resolution to investigate a system. This type of modeling provides a comprehensive description of a multi-physics system because the impact of smaller scale effects can be accurately measured and communicated to the phenomena at larger scales, which reduces the computational cost of solving the physics entirely at small scales. Several models are needed to accommodate for the enormous breadth of spatial and time scales in electrochemical systems. For instance, the thickness of the electrical double layer, concentration diffusion layer, and viscous boundary layer are respectively 10−9 to 100 m and time scales from 10−15 to 100 seconds. Selecting a physically consistent and computationally efficient approach to model each individual group and coupling the approaches of these groups together is a non-trivial process and is the focus of many investigations and precisely where many significant scientific advances can be made.The electrochemically gas-evolving system can be divided into five computational groups for simulation purposes based on the characteristic lengths and temporal scales. As shown in 25 Combining DFT with statistical mechanics is considered the most successful approach to modeling the electrochemically active materials.20 The combined approach reduces the computational cost and helps to overcome the computational limitations of the structure cell size.27 DFT formulation will be further presented in the context of bubble nucleation in which DFT is used to calculate the nucleation rates.The finest relevant scale in electrochemical systems is the quantum model where the material properties and dependencies on atomistic structure are modeled. Quantum mechanics modeling describes the interactions between the electrode material and electron transfer. An increasingly powerful, accurate, and convenient computational tool for studying the electronic structure (i.e. electron transfer), the density functional theory (DFT) is one approach for modeling the quantum scale physics.18 One of the most common applications of molecular dynamic simulations of electro-chemical systems is quantifying the effective local molecular diffusivity and kinetics.21 At a slightly larger scale, mesoscale dynamics computational methods, such as discrete-time dynamics, dissipative particle dynamics, and population balance models, are used to efficiently bridge the gap between molecular dynamics and continuum scales.33 For instance, the mesoscopic scale can be used to model a microscopic quantum lattice-gas system implementing the lattice Boltzmann equations,34 or it can be used to describe a discretized continuum systems such as fluid particles.31Electron transfer at the electrode initiates a number of reactive and non-reactive physicochemical processes in the electrochemical system that impact the molecular and mesoscale dynamics. The molecular dynamics approach can be used to simulate the solvation of ions in the electrolyte, their adsorption by the electrode surface, and stability of the double layer that forms above the electrode.The fluid dynamics of the electrochemically gas-evolving system should be incorporated in both the mesoscopic and continuum models. The mesoscopic model is required to properly understand the interfacial bubble-surface or bubble-liquid interactions, and the continuum model represents the electrolyte flow using the Navier-Stokes equations. Continuum approximations are also used to model interfacial mass transfer. Finally, the macroscopic scale, which is the largest and based on the characteristics length of the electrochemical cell, is implemented to determine the mechanical and thermodynamic characteristics of the cell.3536 The concurrent approach links the different partitions via a set of boundary conditions. Defining the interfaces between these partitions and the corresponding boundary conditions with physically consistent models is a challenging task. Compounding this challenge is the fact that treating interfaces as discontinuities is complicated by the boundaries moving with time. Finally, the time marching of the concurrent model is set based on the smallest time step of the finest model since all partitions are solved together at the same time. This can substantially increase computational costs for systems that need to run for long time. Several techniques have been developed to simulate the multiscale systems concurrently, such as multiscale field theory, handshaking domain, heterogeneous multiscale, and quasi-continuum methods.37Bridging the various models at different scales is the main goal of multiscale modeling. The two main strategies to couple the different scale physics together into a single coherent system are the concurrent and sequential coupling methods.38 Serial stitching is thus known as a parameter passing approach in which the variables are parametrized based on results at the micro scale computations and then passed to the next larger models. Passing the unknown parameters between the single physical models requires a reliable strategy to fit and transfer these parameters.38 Parameter passing requires the implementation of phenomenological theories to derive the key parameters of the large-scale models from the smaller models. These derivations are based on simplifications and local approximations that limits this technique to only a few key parameters.36 In fact, the computational cost of fitting the full constitutive relations (parameters) is too expensive to gather highly accurate information from the smaller scales to the larger scale models, so power law relationships are often used where coefficients are determined from the smaller scale models.An alternative to the concurrent technique is the sequential or serial coupling technique that is based on simulating one scale at a time and transferring information from the small-scale models to the larger scale models.38 The concurrent coupling approach is a more intricate and computationally demanding method due to the limitation of the time step and complexity of domain partitioning of the system. On the other hand, the concurrent technique has more general applicability compared to the sequential approach since it does not require any phenomenological models.38 To enhance the coupling effectiveness of multiscale simulations a hybrid coupling scheme could be implemented, using both concurrent and sequential strategies. The purpose of the hybrid approach is to benefit from combining elements from both the parallel and serial models by reducing the associated restrictions in partition of domains and lower the number of the needed phenomenological models.38 While the hybrid approach is very promising, it requires further development before being applicable to electrochemical systems with bubbles. The bubble models we present are applicable to both the sequential and concurrent approaches.Both the sequential and concurrent techniques have their own strengths and weaknesses. Of the two methods, the sequential technique is generally considered to be more efficient computationally and the more well-established method despite the challenge of parameter passing.While the life cycle of bubbles begins at the nucleation stage, the precursor to this is the generation of dissolved gas by electrochemical reactions. The nucleation process is defined as the physicochemical transformation of particles initially in the metastable phase of high concentrations of dissolved gas in the electrolyte to the more stable phase of a gas bubble. Nucleation occurs when the supersaturation condition is satisfied at which point the concentration of the dissolved gases in the liquid is high enough to initiate the nucleation. Bubble nucleation begins by a molecular formation of particles, known as embryos, from the combination of the solute molecules. These embryos are a dense collection of the dissolved gas particles in a metastable state meaning they can spontaneously dissolve or can become bubbles. Consequently, a close examination of the transformation of these groups of molecules is crucial to understand the nucleation phenomena. The unstable intermediate formation of embryos requires a sufficient amount of energy to overcome the activation barrier of the solute phase transformation. Embryos may grow and assemble to form a cluster, on the order of nanometers, or decay and dissolve again in the liquid phase. Similarly, the cluster stage is reversible and it can decay. Once the cluster gains the required activation energy an irreversible phase change occurs with a critical size, which is known as the nuclei, as shown in 39 For example, bubbles can nucleate spontaneously inside a bulk liquid during boiling. Heterogeneous nucleation occurs at the interfaces with the presence of substrates, which can be any foreign body such as the electrode or a preexisting bubble. Normally, substrates lower the required interfacial free energy required for nucleation.40 The substrate can impact the nucleation rate in two ways. First, because the surface is not perfectly smooth and not perfectly wetted by the electrolyte, nucleation sites will naturally form where small pockets of gas are trapped to the surface. Second, by providing a surface on which to attach there is a lower surface area exposed to the electrolyte. This reduces the free energy necessary to form the surface of the bubble. Properties of the substrate can also impact the nucleation rate. For instance, coating of the nucleation surface changes the substrate topography and chemistry resulting in either higher or lower wetting contact angle between the fluid and the substrate, which will impact the nucleation rate. Also, nucleation rates can change when a surface is heated because the surface tension decreases as the temperature increases.41Bubble nucleation can be classified either as homogeneous or heterogeneous nucleations. Homogeneous nucleation happens in a medium of solute and solvent, without the presence of any surfaces or impurities.G (J). The free energy of homogeneous nucleation, ΔGHom, is described using CNT as the sum of two components: a surface free energy term, ΔGsurf, which makes a positive contribution and a bulk free energy term, ΔGbulk that makes a negative contribution. Accordingly, ΔGHom can be given as a function of the bubble’s radius, R (m), as follows:−1) represents the interfacial surface tension at the gas/electrolyte interface and Δgv (J m−3) denotes the energy density difference between the dissolved and gaseous states of the molecules in the bubble.42 The first term in the right-hand side of Gsurf and the second term is ΔGbulk. CNT can account for the free energy of heterogeneous nucleation, ΔGHet, by introducing a geometric function, f (θ), to consider the energy reduction due to the presence of the substrate. The behavior of f (θ) depends on the wetting angel, θ, at the substrate interface. The heterogeneous nucleation free energy is given as Classical nucleation theory (CNT) is the standard and most widely used theory to model bubble nucleation. CNT predicts the nucleation rate by describing the activation barrier to producing a nuclei in terms of a combined thermodynamic and kinetic activation potential, which is the Gibbs free energy, ΔG. As shown in Rc, at which point the provided free energy reaches its maximum, Rc, the bubble is defined as sub-nuclei and its natural tendency is to shrink. In electrolytic mediums, bubble nucleation is driven by thermodynamic supersaturation, ζ, which is the ratio of the dissolved gases concentration adjacent to the electrode surface, ce (mol m−3), and the equilibrium, far field dissolved gases concentration in the bulk liquid phase, c∞ In general, nucleation requires ζ to exceeds 100.43 In cases where the bulk concentration is significantly depleted due to a large number of bubbles generated, a higher activation energy is required for nucleation. However, the threshold of ζ required for nucleation shows only small variations.43Bubble shrinkage or growth depends on the value of Δ44 Electrode adsorption of molecules can impact the nucleation rate at the electrode surface. Cations and anions change ΔG and influence the discharge kinetics of ions, which in turn impacts the chemical reaction process. This can impact the production of the dissolved gas. Additionally, these processes can influence the surface tension, which also modifies the nucleation rate. Taking into account all these factors, the rate at which the clusters turn into nuclei stage in units of nuclei per a unit volume per a unit time, Jo(nuclei m−3 s−1), can be given in terms of the maximum energy of heterogeneous nucleation, 44D (mol m−3 s−1) denotes the flow of molecules that are added to the forming nuclei, C (nuclei mol−1) is the concentration of molecules (nuclei consisting number of molecules), kB = 1.38 × 10−32 (J K−1) is Boltzmann’s constant, and T (K) is the electrolytes medium temperature. In this case, both Rc can be expressed in terms of the electrode overpotential, η (V), as:V (m3 mol−1) is the specific volume of the nucleating bubble, z is the transfered ion charge, and F = 96485 (C mol−1) represents Faraday’s constant. For the spherical cap model of bubble nucleation, which is the most common nucleation model, the geometric factor is 39 The overpotential is defined as the difference between the electrode potential and the equilibrium potential of the chemical reaction. In 45There are many contributing factors that impact the modeling of nucleation at the electrode surface requires.G, the computed nucleation rates by CNT underestimate the experimentally measured nucleation rate.46 CNT makes several assumptions to compute the nucleation rates that may contribute to the error in predicting the nucleation. These assumptions include:Gsurf.47 Electrodes may have cylindrical, pin or mesh shape. In these cases, modeling of bubble nucleation at non-planar interfaces requires adding curvature corrections to calculate the nonuniform surface tension.The nucleation rates in CNT are determined from a macroscopic viewpoint of the surface tension and it neglects the curvature corrections to Δ39 For instance, consumption of molecular concentration changes the free energy of nucleation and may effect the supersaturation limits that drives the bubble nucleation.CNT computes the nucleation rates based only on the nucleus size and ignoring the effect of local fluctuation and depletion of the solution concentration.48The sub-nuclei states are assumed to take place under thermodynamic equilibrium and bubble formation is assumed to take place at steady state. However, the supersaturation changes during nucleation result in unsteady nucleation rates that lead to varying the nucleation activation energy limits.6In electrochemical systems, the ionic strength of the electrolytic medium plays a significant role in the bubble nucleation. The ionic species adsorption at the bulk solution/bubble interface changes the interfacial surface tension values, which impacts the nucleation rate.Despite the fact that CNT serves as a benchmark for determining Δ51 In the DFT approach, the nucleating medium is assumed to have inhomogeneous molecular structure where the metastable phase is described by a spatially varying density function. In this molecular-based approach, the potential of short-range interactions, ϑ(r) is−3) is a perturbation attraction potential, and r (m) is a position in the fluid. The repulsive component represents the structure of the fluid and the attractive potential models the local density of the fluid. The intrinsic free energy of the system, ψ[ϕ(r)] (J), can be expressed as a function of the probability density profile, ϕ(r) (m−3) in the form:−3) is the density of Helmholtz free energy of a reference system, and 53 DFT represents a promising technique to determine the nucleation rate, but this technique has yet to be applied to bubble nucleation in electrochemical systems.In contrast to the macroscopic view of CNT, the density functional theory (DFT), a computational quantum scale model, uses a molecular scale approach to calculate the nucleation rates.C in z are needed as well. Determination of these quantities will be discussed as part of electrochemical kinetics impact on bubble growth.The nucleation stage is associated with other coincident physics. Evaluation and investigation of bubble nucleation requires accurate computing of species concentration to determine the concentration ratio condition, ζ, and to calculate the nucleation rate in terms of 54 Interfacial mass transfer flux, co is the concentration at the bubble interface, km (m s−1) is the interfacial mass transfer constant, kH (mol m−3 Pa−1) is Henry’s constant and po (Pa) is the pressure inside the bubble. kH. The value of kH, which is determined experimentally, depends on the solubility of the gases which depends on the system temperature; when the solubility decreases as the temperature increases, kH decreases.Bubble growth in an electrolytic medium involves material transfer from the liquid phase to the gas bubble phase through the interface. The rate of bubble growth at the electrode surface depends on the concentration ratio, which drives the interfacial mass transfer via diffusion through the bubble’s interface per Fick’s law at the macroscopic scale.55 These processes determine the amount of molecular adsorption by the bubble interface which controls the bubble growth rate. The total amount of the generated gases at the electrode surface that will be either dissolved or eventually form the bubble, j (mol s−1), is given by Faraday’s law:I (A) is the applied current at the active electrode. The gas produced by the electrode can then either be absorbed into the bubbles resulting in growth or remain in the electrolyte and increase the local supersaturation ratio. While there is some variation in the dissolved gas concentration, it is frequently assumed that the dissolved gas is uniformly distributed inside the electrolytic liquid. This removes the need to consider spatial derivatives when deriving the time rate of change of the concentration of dissolved gas in the electrolyte, d(cs)/dt (mol m−3 s−1), which is modeled as:55Vℓ(m3) is the volume of the liquid solution and bA (m2) is the bubble’s surface area. If the interfacial mass transfer is limited because the diffusivity of the gas through the interface is small resulting in reduced bubble growth and a build up of dissolved gas in the electrolytic bulk. If mk is relatively large, then the amount of transferred gases through the bubble surface is significant and the growth rate will be fast.55Several mass transfer processes take place at the surface of the gas-evolving electrodes including mass transfer from the liquid solution to the electrode surface, adsorption of the ionic gas reactant at the active sites on the electrode surface, diffusion of the gaseous products toward the liquid bulk, and mass transfer of the dissolved molecular gas product into the surface of the growing bubble.8 Therefore, the fraction of the surface covered by bubbles, Θ, will play a large roll in the amount of dissolved gas produced, which in turn impacts nucleation and bubble growth rates. In important quantity needed for estimating bubble growth is the fraction of the total gas produced that is eventually transfered into the attached bubbles, fg.56 Based on a theoretical approximation, fg can be expressed as:8fg = 0. In contrast, as the electrode becomes completely covered, fg → 1.The presence of the bubbles attached to the electrode surface causes portions of the surface to be inactive, which prevents the electrochemical reaction from proceeding at these inactive spots.R(t ) = cRst, where the value of the fitting coefficient cR and s are determined by the physicochemical conditions that control the bubble growth.58 This simplified model of the radius growth is not dependent on the surface shape. If the bubble grows rapidly then the growth kinetics are driven by the inertial effects and the growth is known as inertia-dominant growth, in which s equals 1.58 The inertia-dominant growth exists when a vapor bubble grows in a superheated liquid domain under low pressures.59 During slow bubble growth, the growth behavior is known as diffusion-controlled because gas diffusion across the bubble interface drives the bubble growth and in this case s will be on the order of 1/3 or 1/2.58 The criteria for selecting the value of s is related to the value of the applied current at the electrode. Generally, the rate of bubble growth, M W (kg mol−1) is the molecular weight of the gas, ℓD (m2 s−1) is the mass diffusivity coefficient of gas in the electrolyte.57 Given this scaling, relationship, it is possible to show that R = Rtc0.5. 60 The bubble growth behavior of R = Rtc0.5 in electrochemical systems has been observed experimentally.61Bubble growth evolves in several phases. Starting from an initial radius during the nucleation stage and ending at the necking stage prior to the detachment from the electrode surface as shown in R = Rtc0.5, at high current densities the bubble growth is more accurately modeled by R ∝ t1/3.62 At these high current densities, the chemical reactions at the electrode surface are fast enough to produce large amounts of gas that diffuses rapidly in the liquid solution. In this case, the chemical reaction rate producing the gas becomes the rate-limiting factor that controls the bubble growth.58 It should be noted that the reported bubble growth kinetics rates are restricted for a moderate current (<100 A cm−2) density and absence of bubble coalescence.5 Bubble growth behavior is attributed by the current density namely the electrode kinetics.While the bubble growth in electrochemical systems is modeled well by the diffusion-controlled formula of 63 The kinetics of the chemical reactions are realized in the electron transfer process within the electric double layer between the reduced and oxidized species.64 A charge-electron transfer reaction involves either gaining (losing) electrons by the electrode from (to) the active reacting species. Understanding the behavior of the electron transfer relies on solid-state physics to study the electronic and geometric effects on the electrocatalysis, material science to investigate the electrode composite and characteristics, and thermodynamics to study the reaction activation process.65As given in 64 The Butler-Volmer (BV) kinetics model is the standard model of electron transport across the electrode-electrolyte interface is described from the macro-scale perspective.68 The BV approach relates the net current density i (A/m−2), and the electrode overpotential η (V), which is the difference between the electrode potential and the equilibrium potential of the reactions.69 The net current density isi0 (A m−2) is the exchange current density, n is the number of transfered electrons per reaction, −1 mol−1) is the ideal gas constant, and βa and βc are respectively the oxidation and reduction electron transfer coefficients, which indicate the favored reaction direction (forward or backward) based on the applied electric potential. The formulation of the net current density represents the total sum of the cathodic and anodic current, which act in opposite directions. To accurately account for the bubbles covering the electrode, it is impotant to multiply i by 70Several modeling theories have been developed to model the electrode kinetics ranging from the quantum to macroscopic mechanical scales.n = 1), βa+βc = 1.71 In this case, the electron transfer coefficients can be written as βa = 1 − β and βc = β, where β is known as the symmetry factor. Typically the electron transfer at both the anode and cathode are symmetric, so β = 0.5.72 In this case, the BV model can be simplified for a single step reaction asi0. Consequently, having fast kinetics requires imposing i0 high as possible.The most common type of chemical reaction induced by the electrode is the single step reaction. For a single step reaction (i0 depends on the nature and temperature of the electrode surface and the composition of the electrode-electrolyte interface solution.73 The exchange current density can be expressed as follows:ka and kc (m s−1) are the rate constants of the anodic and cathodic reactions, respectively, and ca and cc (mol m−3) are the concentration of the anodic and cathodic reactants. 74 The exchange current density for concentrated solutions is modeled based on the activities of reacting species and a transition activity coefficient, γ‡, by Bazant (2013) as follows:74e (C) is the elementary charge, ko is the reaction rate constant, aa and ac are respectively the activity coefficients of the anodic and cathodic reactants, and ae is the activity of the electrons. The order of the reaction determines the scale and the units of 75The exchange current is defined as the current that flows at equilibrium. At equilibrium the net current is zero; however, the faradaic activity does not stop. At equilibrium, both the anodic and cathodic have the same magnitude, equal to the exchange current, but the opposite signs. A high current exchange density corresponds to fast reaction kinetics. The value of 76 Even though the BV model can be easily implemented, it does not yield substantial insight into the influence of the molecular nature of the electrode, the medium , or the specific electroactive species in the electron transfer kinetics, which all depend on the charge transfer process.64 For large overpotentials, the resulting current should be saturated, but the BV model does not capture this. To more accurately model large overpotential systems, the molecular nature of the reaction needs to be accounted for which models electron transfer in electrode kinetics computing. Furthermore, the macroscale BV model fails to account for reaction dependency on the electrode material because of the double layer effects, structure of Helmholtz layer, energy distribution of the electronic states in the material which occurs. Also, the BV model does not consider interactions between the reactants and products that involve changes in the free energy of the electrolyte, such as the ion bonding and electrostatic interactions. Thus, a detailed model at microscopic level is required to understand the electrochemical kinetics accurately and overcome the limitation of BV model.The BV model and its limiting cases provide a good model for the electrochemical kinetics under normal conditions from a macroscopic viewpoint.78 This model provides more physical insight by describing the electronic transitions in terms of the electron reactions between the molecules by relating the activation barrier of the electron transfer to the free energy, λ (eV), required to reorganize the local atomic configuration without charge transfer.64 Originally, Marcus theory was developed for electron transfer kinetics between the same phase, and later the theory was modified to include the multi-phase transfer.80 For a high concentration solute, the Marcus model gives:7474BTk, β = 1/2, which matches the BV model.To investigate the electron transfer from a microscopic viewpoint, Marcus theory has been applied to study the electrode kinetics of electron transfer reactions at the molecular scales.us model  shows th82 The oxidization (ox) and redox (red) chemical kinetic rates in MHC can be written as:83A is a pre-exponential factor that accounts for the electrode electronic density and strength, the plus corresponds to oxidation, the minus corresponds to redox, and ξ is the electron energy. The first integrated term of 83 The molecular nature of the MHC provides more physical insight about the kinetics than the BV model; however, the MHC reaction rate model is given by a complex improper integral and is more computationally demanding. To reduce these complexities, the Faradic and exchange currents density for MHC can be fit to8383Further developments of the Marcus theory have led to the more accurate Marcus-Hush-Chidesy (MHC) kinetics model.69 For small overpotentials, each of the models give approximately the same result. In contrast to the BV model where the current increases exponentially as a function of the overpotential, Marcus theory has a non-monotonic behavior as η increases in magnitude, which is not accurate. The MHC approach accurately captures both the saturation of the current and the monotonic dependence on the overpotential. This limiting behavior at high overpotentials exists due to the electronic density states of the electrode nature.84 Additionally, at these high overpotentials the BV model provides unrealistically large reaction kinetics.83 Finally, as λ increases, the current profiles of the BV model remain accurate for larger overpotential ranges.To highlight the distinctive features between the BV, Marcus, and MHC models, the current density dependence on overpotential is presented in 85 The detachment process can be treated as a quasi-static equilibrium process under the influence of several dynamic forces.86 Typically, these dynamic forces range from orders of 10−8 to 10−6 (N).88 Predicting bubble size at detachment can be done by balancing the applied dynamic forces at the bubble-liquid and bubble-electrode interfaces. The forces that act on a bubble at a general vertical solid surface during the detachment are summarized in x-direction is normal to the solid surface and the y-direction is parallel as shown in 90 Bubble detachment at a heat transfer surface has been extensively studied in which the bubble departure relative to a general wall is investigated.92 For gas-evolving electrochemical systems, bubbles at an electrode surface are further influenced by two additional forces: the electrostatic force, Fe, and the Marangoni force FM.86Bubbles continue to grow as long as there is a adsorption of dissolved gas, but as the bubble continues to get larger, the probability that it will detach from the electrode surface increases. This detachment process begins with the necking stage. At this stage, the bulk of the bubble begins to move away from the surface producing a region called the neck that continues to shrink until reaching zero thickness as shown in Fe may oppose the direction of bubble growth retaining the bubble at the electrode surface. The direction of Fe depends on the surface chemistry of the electrode, bubble, bulk liquid interfaces, and the presence of adsorbing ions.86 The general expression, at the continuum scale, for a electrical force acting on a fluid is given as:94Vℓ (m−3) is the fluid volume, ρF (C m−3) is the free electric charge density, E (V m−1) is the electric field intensity, εo (8.85 × 10−12 C V−1 m−1) is the vacuum dielectric permittivity, E, which induces pressure in the fluid.94 The contribution of Fe can thus be repulsive or attractive based on which of these effects dominate the electrostatic interactions between the electrode and the bubble.The electrostatic interactions between the electrode surface charge and the bubble surface can cause a premature departure of the bubble from the electrode surface, or 86 It is possible for the chemical reactions to produce surfactants, which may have concentrations that vary in space. It can be assumed that the concentration varies linearly within the Nernst diffusion layer, where the highest concentration occurs adjacent to the electrode. The thickness of this boundary layer decreases as the current density increases  is the surfactant concentration and x is the direction normal to the electrode surface.17 The surfactant concentration gradient can be determined from on the diffusion boundary layer profile. Surface tension changes can be expressed as 95 The maximum packing concentration (surface excess of the solute), Γ∞, obeys Gibbs elasticity formula and can be modeled asa is the solute activity, which accounts for the thermodynamic deviations from the ideal state of the chemical composition. 96 Changes in surface tension due to surfactant concentration have been experimentally measured.99Marangoni stresses appear in the electrochemical systems due to the concentration gradients of the generated gases near the electrode surface.6 the contact angle at the interfaces, and the geometry of the electrode surface.86 Bubbles may slide on the electrode surface if the sum of the acting parallel forces to the electrode surface is not zero. During this slipping movement, bubbles can coalesce to form a larger bubble, which can result in sudden detachment and return motion at the electrode.9 Bubble coalescence is currently an area of active research, so accurate models of the dynamics are developing. The sliding and coalescence at the electrode surface depend on the local velocity boundary condition, which are further complicated by the fact that the no-slip boundary condition is always valid.100 In fact, the hydrodynamic boundary conditions at the electrode surface depend on several operating conditions and physical parameters. There are three different slip types that are defined based on the physical interaction scales; slip at the scale of individual molecules, continuum slip at the liquid-solid boundary, and apparent slip due to the motion over heterogeneous boundaries such as bubbles. The strength of the slip condition at the electrode interface depends on the type and the quantity of the dissolved gases.100 In addition to the effects of pressure and temperature on the interfacial tension, the electrical properties of the electrolytic environment influence the surface tension behavior as well.100 The most important electrical property is the ionic strength of the aqueous solutions. Each ion has a unique surface tension sensitivity related to the ion activity.9999Modeling bubble detachment in gas-evolving electrochemical systems is complicated by the sensitive bubble dynamics on the electrode,a and θr as shown in 86 The uncertainty of the contact angle complicates the modeling of the detachment forces. Geometric discontinuities of the electrode, such as porosity, are another source of bubble detachment. These surface features could prevent lateral growth, which leads to higher release frequency and variation in the bubble departure diameter.86 Once the bubble gets released from the electrode surface the rising stage of bubble’s life cycle begins.The contact angle as well as the advancing and receding angles also influence the along electrode bubble dynamics. Measuring the advancing and receding angles, θ101 Similarly, electrowetting techniques are used to enhance bubble detachment by modifying the wetting properties when an electric field is applied. This technique has physical effects on the interfacial contact angles.102Finally, the roughness of the electrode surface is one of the physical parameters that affects the surface tension values at the electrode surface. Both the roughness and the geometry of the electrode surface influence the dynamics of the nearby fluid, which can lead to changes in the adhesion forces that impacts the fluid-solid interactions at the electrode surface. As the roughness increases, the friction at the electrode surface increases lowering the opportunity for bubble slip to occur. However, the probability of slip can increase when there is a large accumulation of gas bubbles at the electrode solid boundary, especially at high roughness, which causes dewetting condition at the electrode surface. The wettability of the electrode surface is a function of the physicochemical properties of both solid and liquid states. To reduce bubble accumulation, surfactants are used to reduce the surface tension by using coating, sprays, or other chemical materials.103 The main struggle in modeling dispersed phase flow is accurately capturing the exchanges between the two phases. The nonlinear and multiscale interphase interactions between the dissimilar phase particles result in a rich variety of flow regimes. Coupling and describing the interphase fluid physics can be done using different approaches based on the bubble-liquid interactions.Electrochemical gas-evolving systems are classified as two-phase flow system in which the bulk electrolyte is the liquid phase and the generated gas bubbles are the gas phase. These systems can be characterized as dispersed phase flow meaning that the gas bubbles are present throughout the continuous liquid phase and that the transfer of energy, momentum, and mass between the two phases has to be considered for individual bubbles.105 One-way coupling is used in cases where the number of bubbles within the liquid is low (i.e. volume fraction is less than 10−6), where the liquid transfers momentum to the bubbles; however, the effect of the momentum from the bubbles to the liquid bulk is negligible. At higher volume fraction of the discrete phase (i.e. bubbles), the presence of bubbles can alter the liquid flow structure, so two-way coupling is needed when the value of the discrete phase volume fraction is between 10−6 to 10−3. Both one-way and two-way coupling can be implemented for dilute dispersed phase flow.104 In this particular flow, the dynamics of the bubbles are controlled by the fluid forces (drag and lift) and the flow can be estimated as collision free flow.103 The four-way coupling describes the dense dispersed phase flow where the collisions and continuous contact between the bubbles dominate.103 The dense flow takes place when the volume fraction of the discrete phase is higher than 10−3. Electrolytically generated bubble flow can be treated as dilute flow (i.e. volume fraction is less than 10−3) since the bubbles obey the fluid forces.108 The frequency of bubbles released from the electrode surface, the size of the generated bubbles, and the void fraction values play important role in reducing the collisions between the bubbles in the electrochemical cells. Thus, one and two-way coupling are the most frequently used models for electrochemical systems.Generally, coupling the associated physics of the fluid flows can be done using either one-way, two-way or four-way coupling.Modeling the flow interactions between the bubble and liquid depends on the resolution, where the fluid dynamics can be modeled using different equations depending on the time and length scales. Selecting the proper modeling scales depends on the fluid characteristics of the systems. At macro-scale modeling, the governing equations can be presented using the Euler, Navier-Stokes, or Burnett equations. At the smaller mesoscale, the molecular models of Boltzmann equations are used to model the flow fields using statistical and distribution functions. Micro-scale systems are modeled by tracking each individual fluid particle using Newton’s law, but this is generally only possible for rarefied gas systems. Given the need to model the electrolyte bulk at the scale of the electrochemical scale, the focus here is on the larger scale models.109 and the particles or the bubbles are described in a Lagrangian frame as a stochastic point process.110 This process models the bubble with probability density functions to describe the bubbles as non-contiguous spheres of finite radius in the space. Flow fluctuations in the liquid phase are a consequence of the movement and momentum exchanged between the bubble and electrolyte. Even when the system is a laminar multiphase flow, it can experience significant fluctuations of pseudo-turbulent velocity with a range of spatial and temporal scales due to the bubbles.110 To model the influence of the bubble particles on the liquid and vice a versa, two-way coupling needs to be applied.Eulerian-Lagrangian (E-L) methodology precisely treats the dilute dispersed flow by expressing the liquid phase using the continuum model, and the gas bubbles as Lagrangian elements. In the E-L formulation, the bubbles are represented as dispersed particles and the electrolyte liquid as a carrier phase. The carrier or continuous phase is modeled in an Eulerian frame incorporating a statistical random field approach due to the averaging of the liquid fluctuations,111 The E-L for-mulation is able to capture the nonlinear and multiscale interactions between the continuous and dispersed phases with high accuracy.110 By determining the instantaneous position, shape, and velocity of each individual bubble, the E-L approach is able to explicitly compute bubble-liquid and bubble-bubble collisions. Despite the advantages of the E-L approach, proper implementation of the interphase coupling is required to avoid numerical divergence.110 The E-L method only handles particles and continuum but a model of the interphase coupling with the property transfer at the surface is required to avoid numerical divergence. The following subsections present further details on the Eulerian and Lagrangian models as well as the dominant interphase and Multiphysics coupling models used for simulating two-phase flow in electrochemical systems.The dynamics of each individual bubble are modeled using Newton’s second law and the liquid phase model uses the Navier-Stokes equations. Interphase information such as mass transfer, momentum exchange, and energy transfer are continuously transfered between both the Lagrangian and the Eulerian phase.ℓ(kg m−3) is the electrolyte density, uℓ(m s−1) is the electrolyte velocity, −3 s−1) is the interphase mass transfer rate of dissolved gas in the liquid into and out of the gas bubbles, P is the pressure field, τℓ (N m−2) is the shear stress tensor of the liquid phase, g (m s−2) is the gravitational constant, fbℓ →  (N m−3) is the volumetric forcing term due to liquid-bubble interactions that acts only at the interface, and fe (N m−3) is the acting electrical force on the fluid that comes from the ion species movement within the electrochemical system. The stress tensor for Newtonian and laminar flow, τℓ, in τℓ = μℓ[(∇uℓ) (∇uℓ)T], where μℓ (Pa s) is the molecular viscosity of the electrolyte. The momentum of the electrolyte and the bubble are linked in fbℓ → . This term represents the sum of all instantaneous interfacial forces, over all bubbles, which are described in The liquid phase at the continuum scale models the mass and momentum transfers,assuming no energy transfer,using the incompressible continuity and Navier-Stokes equations as following:Vℓ (m3) is the liquid volume, n denotes the number of bubbles, and −1) is the mass transfer rate from the liquid to a bubble. The total amount of gas transfer through a particular bubble surface can be expressed as a summation over the chemical species as follows:J is the number of species, Ab (m2) is the bubble surface area, MWj (kg mol−1) is the molecular weight of the involved species, and −2 s−1) is the interfacial molar mass flux.In ℓ changes during the electrochemical reactions. The change in αℓ values can also be associated with the transport of species since species concentration impacts the bubble nucleation, growth, and detachment at the electrode surface. The species transport near the electrode surface of a dilute solution can be modeled using the Nernst-Planck law:73Nj is the species molar flux perpendicular to the electrode, cj (mol m−3) is the concentration, Dj (m2 s−1) is the ionic diffusion coefficient of species j, T (K) is the species temperature, and U (V) denotes to the electric potential. Due to the continuous motion and growth of gas bubbles at the electrode, the spatially and temporally varying value of α13 The value of the electric current density, i (A m−2), determines the production of the oxidized and reduced chemical species within the system,which can be given using Faraday’s lawi = iox + ired where iox is the oxidation current and ired is the reduction current. When i = 0, the chemical reactions are in equilibrium, which means the concentrations of the oxidation and reduction species remain constant and balanced. In this case, the equilibrium electrical potential, Ueq is determined as following:cox is the concentration of the oxidization species at equilibrium, and cred is the concentration of the reduction species at equilibrium. For non-equilibrium states, the concentration changes are described by the overpotential value which is given as:i = 0 as shown in the electrochemical kinetics section of this paper. To apply an electrical potential to generate current density, the conservation of the current isjcjz = 0 should be applied to ensure that there are no charged regions in the electrolyte at the macroscopic level.13The accumulated effect of the migration of charged species within the electrochemical system results in an electric current based.K (S m−1) and the diffusion properties, jD. The effective electrolyte conductivity for the system is given as:b = 1−αℓ, decreases the value of K.9 Different equations have been reported to predict accurately K for heterogeneous media based on the homogeneous electrolyte conductance without gas bubbles as follows:115K0 is the electrolyte conductivity in the absence of gas bubbles. K/K0 as function of αb based on the proposed relations in K for αb ≤0.3, is almost the same using any of the given relations in b ≥0.3, calculating K shows a variation up to 7% between the given models. For all cases, these functional dependencies are based on fits to experimental results. Other than the linear case, all of these models are similar; however the models by Bruggman, 1953112 and Maxwell, 1998113 are the ones predominantly used. The reduction of the effective conductivity due to bubble volume fraction, reflects the increase in overpotential since bubbles act as discontinuities that increases the electrical resistance. Similarly, the effective iD can be calculated using the relations in Gas bubbles inside the electrolyte are voids that affect the effective electrolyte conductivity 116Bubbles can be treated as rising particles that are best observed in the Lagrangian frame. The Lagrangian model describes the bubbles motion using Newton’s second law of motion, which connects the bubble kinematics to the acting force. In addition to the bubble dynamics, the interfacial mass transfer should be coupled to the model so that changes in the bubble volume can be accounted for. During the bubble motion, the acting forces on the bubble change the shape potentially resulting in changes to the mass transfer rates across the bubble. The Lagrangian model provides the ability to capture these effects with a higher accuracy than an exclusively Eulerian approach.117 Instead, the volume of the bubble alters due to the gas diffusion across the bubble surface due to the electrochemical reactions. The density within the bubble can be assumed uniform and constant. The governing equations of an individual bubble’s trajectory and mass conservation is:xb (m) is the position, ub is the velocity (m s−1), and bV is the volume (m3) of the bubble. The momentum conservation of each bubble can be expressed using Newton’s second law as follows:FB, FL, FP, FV.M, FW, FD are respectively the buoyancy, lift, pressure, virtual mass, wall lubrication and drag forces (N).In this microscopic frame, bubble compressibility can be neglected since the bubble volume does not change as a result of pressure or surface tension variations. There are extreme conditions where the bubbles can be compressible, but for standard electrochemical applications, these are not common.bRe and oB which are the Reynold and Bond numbers, respectively. The numbers are defined as bRe = ρℓ|ub − uℓ|bd/μℓ and bd (m) is the bubble diameter. The value bRe measures the ratio of the inertial force to the viscous force, and Bo measures the ratio of the gravitational force to the surface tension force. The characteristics of bubbles rising in the electrolyte can be determined by the values of Reb and Bo.123 When surface tension forces dominate relative to gravitational, inertial, or viscous forces, the bubble resists surface deformations and keeps its spherical shape.13 When the forces are more balanced, the bubble can take on a variety of shapes.The expression of each forcing term is presented in 123Ab, has a significant influence on the overall mass transfer across the bubble surface. A bubble with a high interfacial area such as the skirted bubble will have a higher mass transfer rate across the bubble compared to a bubble with lower surface area such as spherical bubble. The mass transfer across the bubble interface changes the bubble volume which impacts the forces balance on the bubble and its rising velocity.125Generally, bubble shapes can be categorized into three main types: spherical/ellipsoidal, cap/skirted, and irregular/wobbling shapes.127 Determination of the mass transfer rates in electrochemical system requires evaluation of Nernst-Planck concentration fluxes in Mass transfer rates between the bubble and liquid phases are controlled by the electrochemical reactions in the system which comes from either the difference in the electrical or chemical potentials between the bubbles and bulk solution.Proper characterization of interfacial mass transfer requires a detailed consideration of the multiscale interactions around the bubble surface. While there are many factors that impact the rate of material transfer through the interface, there are three critical effects that must be accounted for in addition to accurately determining the bubble shape. First, the thermodynamic equilibrium state between the liquid-bubble phases impacts the phase change at the interface and is modeled using Henry’s law. Next, local velocity fluctuations due to turbulence can rapidly change the nearby concentration fields and enhance the mass transfer rates. Finally, the presence of contaminates inside the electrolyte changes the chemical mixture of the electrolyte, which varies the properties of the electrolyte such as the diffusion coefficient. These changes play a role in controlling the interfacial mass transfer.129 The bubble’s rising velocity influences the transport of species since the velocity determines the mean residence time of the bubble inside the liquid fluid. Also, the rising velocity of the ascending bubble determines the two-phase flow structure, which is characterized by bubble swarms, wakes and rising paths.Bubble geometry not only plays a role in calculating the interfacial area, but it also impacts the bubbles rising velocity. The rising velocity of the bubble depends on the nature of the electrochemical, physico-chemical system. The bulk liquid hydrodynamics and contamination physical properties, such as density and viscosity, affect the bubble motion by defining the viscous, surface tension, and buoyancy interactions between the two phases as discussed in the Lagrangian phase section. Several empirical and theoretical formulations have been developed to evaluate the terminal velocity of rising bubbles due to its importance in the multiphase flows.130 How the bubble rises also influences the structure of the wakes. Bubble wakes can be either steady without circulation, steady with circulation, or unsteady with vertex shedding. The induced wake eddies establish micro-convection that enhances the interfacial mass transfer rate at the bubble surface. These turbulent eddies can be suppressed in highly viscous systems. The shape of the bubble as it rises also plays a major role in the type of wake produced. As an example, the curvature of spherical bubbles is not conducive to generating a recirculating or vertex wakes. Elliptical or spherical-cap bubbles shed vorticity, which produces recirculating or eddy filled wakes that increases the interfacial mass transfer. The different types of wakes have qualitatively different mixing patterns and mass transfer boundary layer characteristics. Understanding the local mixing properties is valuable for chemical reaction boundaries, since the mixing has a considerable influence on the reaction yield and high selectivity.131Qualitatively different bubble swarm and wake structures can be observed based on the value of Reynold number. At low Reynold numbers bubbles are expected to move in a straight path with a single-threaded wake. While at high Reynolds, the bubble experiences oscillatory motions, such as spiral and zigzag trajectories with a double-threaded wake.132 the mass transfer from a bubble is very complex. The exchange of mass occurs within a diffusion layer in which the mass transfer is proportional to the concentration gradient at the bubble interface and is given in terms of Fick’s law.133km, in terms of the dimensionless Sherwood number, Sh = KmL/Di where L is the characteristics length of the mass transfer. The value of km determines the mass transfer rate as shown in Sh result in increases in the mass transfer rate. A high Sh indicates that convection dominates mass transfer compared to diffusive mass transfer, which generally occurs in high flow velocity such as turbulent flows. Another dimensionless number that impacts mass transfer rates is the Peclet number, Pe. Pe = uL/Di.Although it is known that the vortex wakes produce very effective mass transport,141 The great and continuous interest in developing the understating of electrolytic bubbles come from its wide and vital applications including hydrogen production,142 metal reduction cells,143 water electrolysis,144 and chlorine generation.145 In these applications, the presence of the gas bubbles is one of the dominant factors that controls the electrical efficiency of these systems and mass transport. To enhance bubble management in these systems, several techniques have been developed in the recent years to enhance bubble removal from the electrode surface and increase mass transfer mixing by either applying external fields such as super gravity,146 ultrasound,147 and magnetic-hydrodynamics1 fields. Alternatively, bubbles can be removed by means of manipulating the components of the electrochemical cell such as electrode shape,148 distance between the electrodes,149 and the cell/electrode inclination angle in the cell.150 However, these experimental investigations can be enhanced with numerical modeling to further enhance these techniques.Electrolytic gas-evolving systems have been investigated for more than a century.9 particularly, in studying the bubble growth and detachment at the electrode surface.151 Recently, the nature of nanoformed bubbles has received attention due to the difference in its fundamental behavior compared to the micro-bubbles.154 Nano-bubbles are considered more stable to dissolution against violent decompression and survive for longer compared to the micro-bubbles.153 Understanding the behavior of these bubbles is crucial to further enhance bubble management in electrochemical cells and the efficiency of these systems. Detecting these nano-bubbles and controlling them could impact industrial applications including nano-energy harvesting devices in which the bubbles are converted into electrical pulse.155 Developing models to describe properly the stability and life cycles of nano-bubbles is an important field of study15 as bubbles at this scale show deviation from the exiting models for example the contact angles of the nano-bubbles are overestimated by Young’s law.9The majority of the experimental investigations have been limited to studying the nature of the micro-scale generated bubbles as reviewed by Sequeira et al., 2013,85 developed a structure-based optimization to describe the gas evolution at porous electrodes based on the structural properties of the electrode and the evolving bubbles by introducing preferential nucleation sites. This study shows the potential of further developing models to control bubble production for both hydrophobic and hydrophilic electrode structures. Deriving such models that incorpo-rates the characteristics of the electrode material will not only give better physicochemical insight of the bubble behavior, but it also provides opportunities to optimize the bubble-electrode interaction and, ultimately, develop new electrode materials and structures to manage these bubbles.Another area of current investigation is the influence of the electrode structure on bubble development. Understanding bubble nucleation, growth, and removal from an electrode surface needs proper modeling that takes into account the electrode structure and complexity of the multi associated physics. In a recent investigation, Kady et al., 2016156 growth,157 or on simulating two-phase flows.108 However, full understanding of the nature of these bubble demands taking into account all the associated physics for the entire life cycle. This type of modeling helps to reveal several aspects in the area of gas-evolving systems including bubble nucleation at the electrode surface, effects of bubble presence on the electrolyte conductivity in large-scale systems, and voltage distribution on the gas-evolving electrodes.9 Multi-physics modeling also enables the simulation and study of complex new systems and applications where bubble evolution play an important role such as electrochemical therapy of tumors159 and nano-scale electrochemically energy harvesting and microfluidics devices.161 Performing a multiscale, multi-physics modeling to study electrolytic gas-evolving systems is a challenging task that requires a huge computational effort. To overcome the computational cost of multiscale multi-physics systems, optimizing the computational coupling between the multiscale physics is a topic that requires further attention.Significant progress in modeling of electrochemically gas-evolving bubbles has been presented, however, in most of these studies the modeling is focused on one or two stages of the bubble life cycle such as bubble formation,The life cycle of a bubble in electrochemically gas-evolving systems is defined by five stages cycle: nucleation, growth, detachment, rising, and burst of the bubble. Each stage is subject to multiscale and multiple physical phenomena influencing the bubble behavior. Further complicating the model is the electrochemical gas-evolving physics. The essential equations to describe the multiscale physics of gas bubbles in electrochemical applications are presented and reviewed. These phenomena range from quantum up to macroscopic scales. The wide range of physicochemical phenomena, in the electrolytic two-phase system, requires fundamental and detailed understanding of the relation between the different levels of the physical theories. Coupling these physical models can be achieved either through concurrent or sequential techniques. The concurrent approach is limited to the process of the smallest time step, and the sequential stitching model makes approximations to link different scale models into a unified model. A hybrid approach can enhance the coupling by bridging some processes through concurrent strategy and other processes using sequential method, but requires further development before application.Understanding bubble nucleation at the electrode surface depends on the complex electrochemical processes. Nucleation rates in electrolytic medium are driven by the behavior of supersaturated dissolved gas in the electrolyte. There are two type of nucleation; homogeneous and heterogeneous nucleation. Presence of a substrate in heterogeneous nucleation lowers the nucleation activation energy compared to the homogeneous. The two prevailing nucleation theories, the classical nucleation theory and density functional theory, model nucleation from the macroscopic and molecular scale perspectives. The classical nucleation theory makes several assumptions to predict nucleation rates and is generally considered to underestimate the nucleation rate compared to experimental results. Since the density functional theory uses a microscopic scale, it has higher accuracy compared to classical nucleation theory; however, implementation of the density functional theory model to simulate bubble nucleation rates in electrolytic environment requires further investigations.After nucleation, bubble growth takes place. The growth rate depends on the electrical current density magnitude, which drives the reaction rate and the development of regions of super saturated dissolved gas. Electrochemical kinetics play a significant role in evaluating the amount of produced gases and their local diffusion, where the amount of the produced gases controls the bubble nucleation, growth, and detachment of the bubbles at the electrode surface. The Butler-Volmer model evaluates the electrochemical kinetics from a mesoscopic viewpoint while Marcus theory determines the Faradic current density based on microscopic approach. Marcus-Hush-Chidsey model is a developed model of Marcus theory, which solves the electrochemical kinetics at a microscopic scale. Although the Butler-Volmer model gives similar results to Marcus-Hush-Chidsey model at small overpotential values, the BV calculations are overestimated at high overpotential values. Since the presence of gas bubbles in the electrolyte liquid phase increase the overpotential by reducing the effective conductivity of the bulk flow, evaluating the Faradic current density using Marcus-Hush-Chidsey approach is preferable in this case.The bubble growth ends once the bubble detaches from the electrode surface. At the stage of bubble release, there are several hydrodynamic forces influencing the bubble’s quasi-equilibrium detachment, including the electrical field and the concentration fluctuations. Bubble response at the electrode surface is predominately determined by the interfacial conditions. These hydrodynamic conditions can be either slip or no-slip based on several parameters, such as the electrode roughness and geometric features. Electrode wettability is subject to physicochemical variations which is a function of the amount of dissolved gases, ionic strength of the electrolyte solution, electrode potential, and presence of chemical surfactant. These parameters determine the amount of interfacial slip occurrence, and therefore control bubble growth and detachment at the electrode surface.Once the bubble detaches from the electrode surface it is free to move in the electrolyte resulting in a multiphase flow. To model the liquid electrolyte and the bubbles as particles, the Eulerian-Lagrangian approach provides the best balance of accurately and consistently incorporating the different physics. The Eulerian-Lagrangian model is a rigorous approach to describe the dispersed dilute flow of bubble rising and bursting. The two-phase computational framework of the model uses a statistical approach by modeling the bubble as a stochastic point process using a Lagrangian reference, while the liquid phase is described using a random field in an Eulerian frame. The dynamics of the multiphase flow are coupled with a concurrent mass transfer due to the transport of species between the two phases. Species transport result in ion flows within the electrolyte.To accurately capture the interaction between the bubbles and the electrolyte it is necessarily to accurately model the interface location and the flux of properties and material through it. The interfacial mass transfer rates are a function of the liquid-gas interactions. The twophase interactions influence the shape, trajectory and wakes of the bubble. This fine scale modeling of the bubbles and their wakes can then be incorporated into system scale measurements of mass transfer. Modeling of the later quantities requires a careful mathematical approach, that accounts for all the physicochemical aspects of mass transfer at both macro- and micro-levels. Capture the interfacial mass transfer rates of electrolytic processes and at different scales is still an active area of research.Accurately modeling the physicochemical behavior of bubbles in electrochemical cells is necessary to further enhance the overall efficiency of these cells. Modeling of the electrochemically gas-evolving cells requires a multiscale approach that unifies the multiphysics which happen during the proceeding chemical reactions. Separate, bubbles and electrochemical systems pose modeling challenges, but the combination of the two is the focus of a number of investigations. This review provides a summary of the fundamentals of these multiphysics by presenting the needed mathematical formulations to model an electrochemically gas-evolving system with bubbles.While significant progress has been made in modeling bubbles and how they can be incorporated into models of electrochemical systems, there are a number of topics that are the focus of continued development. For example, accurate modeling of bubble nucleation in these systems would benefit from the application of the density functional theory approach. Also, coupling techniques of the multiscale physics in the gas-evolving is another area of possible development that will help to simulate these complex systems from the rest point, prior the chemical reactions take place, up to the bubble bursts in the system. Finally, continued development of the hybrid multiphysics schemes will enable greater resolution and more accurate simulations of these gas-evolving systems.


Tooth inhalation remains a rare incident but it may occur during dental care, especially in children. We report here the case of a four-year-old boy with Down syndrome who came to the hospital after a dental trauma. During the extraction procedure, he aspired his maxillary incisor without presenting any signs of respiratory distress and was discharged by the surgical team, who thought that he had swallowed the tooth. Three weeks later, he was admitted to the emergency service because of a pulmonary infection. Two endoscopy interventions under general anesthesia were necessary to recover the foreign body inside the left lung. Because of the multiple symptoms associated with the trisomy 21 syndrome , practitioners should be very mindful of aspiration risks and complications during dental care. The systematic prescription of lung radiography would prevent the onset of pulmonary infections and enable an earlier intervention. A foreign body may be accidentally aspirated into the airways. This may occur in adults , 2 but iDifferential diagnosis between inhalation and ingestion may be difficult when no cough or suffocation occurs. In this context, we report the case of a tooth aspiration by a four-year-old boy with Down syndrome, which caused a severe chest infection. Practical recommendations to prevent such incidents and their complications are reviewed.A 4-year-old boy with trisomy 21 came to the Dental Hospital-University service with his parents, following a fall at the family home four days before. The child did not show any loss of consciousness after the trauma and was up to date with his vaccines. He had already seen a general practitioner and was referred in emergency to a specialist for oral care. His parents described bleeding and crying when the fall occurred and serious difficulties with eating since it happened. The clinical examination was complicated by the poor cooperation of the child. No lesion of the exobuccal integuments was found, but an enamel fracture of the 51 (upper right incisor) and a complex coronal fracture of the 61 (upper left incisor) with pulp exposure and intrusion were noticed. The swollen aspect of the vestibular gingiva with a necrotic appearance led to the decision to extract the 61 under local anesthesia and nitrous oxide sedation. It had not been possible to obtain a correct X-ray. Despite the use of equimolar oxygen nitrous oxide mix, the patient was very restless. Due to a sudden head movement by the child just after the extraction, the tooth slipped out of the forceps, fell on the tongue, and disappeared into the pharynx. The child performed a swallowing movement without any sign of choking or coughing and thereafter presented normal postoperative behavior while playing in the waiting room. As no obvious signs of respiratory distress were shown by the young boy, it was thought that he had swallowed the tooth. He was discharged with supervisory advice.Three weeks later, the patient was admitted to the Emergency Department because of a regular cough and fever that had lasted for several days despite three different antibiotic therapies. The cardiopulmonary auscultation was normal. A chest radiograph confirmed the presence of a foreign body inside the left lung . It had Reports about inhalation of foreign bodies and focusing attention on Down syndrome children , 16 and The major risk is usually immediate, with asphyxia, suffocation, and choking cardiac arrest, but sometimes, FBA may remain undiagnosed if asymptomatic . DelayeTeeth are radiopaque and should be easily seen on lung X-rays. Nevertheless, it is still helpful for the physician when parents can recall the event and give information about the foreign body. In our case, as the dental practitioner and the parents thought that the tooth had been swallowed, the foreign body was not immediately identified.Therefore, the prescription of a lung X-ray should become systematic when a tooth disappears, in cases of suspicion of swallowing or even in the absence of symptoms . The guiOnce the aspiration is confirmed, the patient should firstly be leant forward to improve breathing. It is essential that the practitioner and his staff remain calm. The patient must be reassured and carefully evaluated. If symptoms of respiratory distress appear, first aid must be initiated  and prompt contact with the emergency services must be establishedSecondly, both clinical and radiological evaluations are required. Urgent management with a flexible or hard fiber optic bronchoscope should be performed to remove the object that can obstruct the airway or cause lung infectionThorough documentation of the accident is required. Further documentation may include notation of all medical care, copies of radiographic reports confirming the diagnosis, and removal reports for the objects. The patient's whole medical and dental history should be reviewedFinally, in cases of tooth extraction, prevention should include appropriate anesthesia and sedation, proper body and head positioning, adequate lighting, and four-handed dentistry with an attentive assistant helping to maintain the head if necessary. Sterile gauze or appliances like Isovac® (Innerlite Inc.) may be used as a protective barrier in the oral cavity, distal to the working areaThe recommendations to be published could be the following:Conscious sedation with nitrous oxide preserves the protective reflex of airways, but the backward inclined position of the patient promotes the fall of objects directly into the posterior part of the oral cavity, triggering a swallowing reflex or inhalation instead of deglutition . The indForeign body aspiration is a common complication that should be anticipated, especially for special care in dentistry. At the slightest doubt after an object has disappeared, a chest X-ray must be prescribed immediately. This case of tooth inhalation confirms the importance of a multidisciplinary organization inside the hospitals, to take care of patients with handicap and manage the care complications as well as possible.