Particle Diffusion Research Articles

Enhanced diffusion through multivalency.

The diffusion of macromolecules, nanoparticles, viruses, and bacteria is essential for targeting hosts or cellular destinations. While these entities can bind to receptors and ligands on host surfaces, the impact of multiple binding sites-referred to as multivalency-on diffusion along strands or surfaces is poorly understood. Through numerical simulations, we have discovered a significant acceleration in diffusion for particles with increasing valency, while maintaining the same overall affinity to the host surface. This acceleration arises from the redistribution of the binding affinity of the particle across multiple binding ligands. As a result, particles that are immobilized when monovalent can achieve near-unrestricted diffusion upon becoming multivalent. Additionally, we demonstrate that the diffusion of multivalent particles with a rigid ligand distribution can be modulated by patterned host receptors. These findings provide insights into the complex diffusion mechanisms of multivalent particles and biological entities, and offer new strategies for designing advanced nanoparticle systems with tailored diffusion properties, thereby enhancing their effectiveness in applications such as drug delivery and diagnostics.

Role of obstacle softness in the diffusive behavior of active particles.

We numerically investigate the diffusive behavior of active Brownian particles in a two-dimensional confined channel filled with soft obstacles, whose softness is controlled by a parameter K. Here, active particles are subjected to an external bias F. Particle diffusion is influenced by entropic barriers that arise due to variations in the shape of the chosen channel geometry. We observed that the interplay between obstacle softness, entropic barriers, and external bias leads to striking transport characteristics of the active particles. For instance, with increasing F, the non-linear mobility exhibits a non-monotonic behavior, and effective diffusion is greatly enhanced, showing multiple peaks in the presence of soft obstacles. Furthermore, as a function of K and F, particles exhibit various diffusive behaviors, e.g., normal diffusion-where the role of obstacles is insignificant, and subdiffusion or superdiffusion-where the particles are partially trapped by the obstacles, and the particles are ultimately caged by the obstacles. These findings help understand the physical situations wherein active agents diffuse in crowded environments.

Numerical simulation of the dual-loop different velocity air curtain system in the operating room

The problem of cross-infection between patients and surgeons in the operating room had been widely concerned by the international community. The purpose of this study had been to explore the potential of a Dual-loop Different Velocity Air Curtain System (DDVACS) being applied in the operating room to reduce cross-infection. In this study, the performance of the system was studied using the computational fluid dynamics (CFD) numerical simulation method, with the operating conditions of different air supply velocities inside and outside being set. The RNG k-ε model, Discrete Phase Model (DPM), and Species Transport model were used to simulate the air distribution, particle diffusion, and virus diffusion. The main research results showed that, when the DDVACS was operated at an inner air supply velocity of 0.24 m/s and an outer air supply velocity of 0.45 m/s, the virus concentration in the surgical area had decreased by 89%, the particulate matter concentration in the surgical area had decreased by 78.08%, but the deposition of particulate matter had increased by 21.62%. This study concluded that DDVACS had been found to effectively reduce the concentration of cross-infection. Finally, the velocity difference between the inner and outer sides was controlled within 2 times. This setting had been arranged to make the DDVACS work better. This study's contribution was to further verify the application of the DDVACS in operating rooms, providing a new perspective and empirical support for related fields and possessing certain practical application value.

Study on Plugging Process Parameters and Mechanism of Large-Scale Plugging Experimental Device for Blowout Preventer Stack

Summary The blowout preventer (BOP) stack is the last line of defense for well control (pressure control of the wellbore), and the failure of its sealing components will cause serious well control accidents. At present, there is a lack of suitable experimental device to verify the feasibility of the emergency rescue method proposed for the early seal failure of the BOP stack by pumping plugging particles. However, it is costly, risky, and time-consuming to build and implement a large-scale plugging experimental model for BOP stack with ultrahigh pressure (70 MPa) and large displacement. Therefore, based on this device, this paper establishes a large-scale plugging experimental model of BOP stack by using computational fluid dynamics-discrete element method (CFD-DEM). The volume flow rate is used as the evaluation index to analyze the influence of the shape, size, concentration, and pump displacement of the plugging particle on the plugging efficiency, and the plugging parameters of the experimental device are researched and selected. The results show that the plugging particles can be divided into four stages—particle pumping, particle diffusion, particle bridging, and particle accumulation. The 5-mm spherical particle has the best plugging efficiency for the largest dimension of flange connection seal failure. The particle concentration and pump displacement affect the bridging time of particles but have no obvious effect on the plugging efficiency. A pump displacement of 2.4 m3/min and 5-mm spherical particles with 25% concentration were selected for emergency plugging of the experimental device. This study not only reduces the cost and risk of large-scale plugging experimental device for BOP stack but also further promotes the feasibility study of emergency rescue of BOP seal failure.

Numerical study of underwater oil spill diffusion in complex hydrodynamic environments

Waves and currents are key dynamic factors that influence the diffusion of underwater oil spills. To study the behavior of such spills under complex hydrodynamic conditions, an oil spill diffusion numerical model was established and physically verified by model test data. The drift and diffusion of crude oil from seabed to surface under current, wave, and wave–current coupling conditions were analyzed. The results reveal that under the wave–current coupling condition, the oil spill diffusion exhibits the movement characteristics of oil particles influenced by both currents and waves. The oil particles oscillate with water particles while simultaneously diffusing in the direction of the water flow. The rising speed of oil droplets is fastest in still water but slows significantly under the influence of waves and currents. The shape of the oil slick at the leakage point is related to the hydrodynamic conditions. The oil slick diffuses vertically upwards in still water. While in current and wave conditions, it takes on “C” and “Z” shapes, respectively. Under the influence of the wave–current coupling, the slick spreads in an “S” shape. Moreover, the faster the oil spill, the more significant the entrainment effect, leading to an intensified lateral and vertical diffusion of the oil particles. These research findings offer valuable insights for tracking underwater oil spill trajectories during accidents.

Dust ring and gap formation by gas flow induced by low-mass planets embedded in protoplanetary disks

The observed dust rings and gaps in protoplanetary disks could be imprints of forming planets. Even low-mass planets in the 1-10 Earth-mass regime, which have not yet carved deep gas gaps, can generate such dust rings and gaps by driving a radially-outward gas flow, as shown in previous work. However, understanding the creation and evolution of these dust structures is challenging due to dust drift and diffusion, requiring an approach beyond previous steady state models. Here we investigate the time evolution of the dust surface density influenced by the planet-induced gas flow, based on post-processing three-dimensional hydrodynamical simulations. We find that planets larger than a dimensionless thermal mass of m = 0.05, corresponding to 0.3 Earth mass at 1 au or 1.7 Earth masses at 10 au, generate dust rings and gaps, provided that solids have small Stokes numbers (St ≲ 10−2) and that the disk midplane is weakly turbulent (αdiff ≲10−4). As dust particles pile up outside the orbit of the planet, the interior gap expands with time when the advective flux dominates over diffusion. Dust gap depths range from a factor of a few to several orders of magnitude, depending on planet mass and the level of midplane particle diffusion. We constructed a semi-analytic model describing the width of the dust ring and gap, and then compared it with the observational data. We find that up to 65% of the observed wide-orbit gaps could be explained as resulting from the presence of a low-mass planet, assuming αdiff = 10−5 and St = 10−3. However, it is more challenging to explain the observed wide rings, which in our model would require the presence of a population of small particles (St = 10−4). Further work is needed to explore the role of pebble fragmentation, planet migration, and the effect of multiple planets.

Autoregressive HMM resolves biomolecular transitions from passive optical tweezer force measurements

Optical tweezer (OT) single molecule force spectroscopy is a powerful method to map out the energy landscape of biological complexes and has found increasing applications in academic and pharmaceutical research. The dominant method to extract molecular conformation transitions from the thermal diffusion-broadened trajectories of the microscopic OT probes attached to the single molecule of interest is through hidden Markov models (HMM). In standard applications, the HMMs assume a white noise spectrum of the probes superimposed onto the molecular signal.Here, we demonstrate, through theoretical derivation, computer modeling and experimental measurements that this standard white noise HMM (wnHMM) misses key features of real OT data. The deviation is most pronounced at higher frequencies because the white noise model does not account for the over-damped nature of particle diffusion in an OT harmonic potential in aqueous environments. To address this, we derive how to incorporate autoregression (ar) between consecutive data points into an HMM, and demonstrate through modeling and experiment that such an arHMM captures real OT data behavior across all frequency ranges. Through analysis of real OT data we recorded on a single DNA hairpin undergoing folding and unfolding transitions, we show that the wnHMM extracts lifetimes that are at least a factor of 2 faster and less consistent than the arHMM results which match expectations and prior measurements. Overall, our work suggests that arHMM should be the default model choice for analysis OT single molecule transitions and that its use will improve the fidelity and accuracy of single molecule force spectroscopy measurements.

Simulation of stochastic discrete dislocation dynamics in ductile Vs brittle materials

Defects are inevitable during the manufacturing processes of materials. Presence of these defects and their dynamics significantly influence the responses of materials. A thorough understanding of dislocation dynamics of different types of materials under various conditions is essential for analysing the performance of the materials. Ductility of a material is directly related with the movement and rearrangement of dislocations under applied load. In this work, we look into the dynamics of dislocations in ductile and brittle materials using simplified two dimensional discrete dislocation dynamics (2D-DDD) simulation. We consider Aluminium (Al) and Tungsten (W) as representative examples of ductile and brittle materials respectively. We study the velocity distribution, strain field, dislocation count, and junction formation during interactions of the dislocations within the domain. Furthermore, we study the probability densities of dislocation motion for both materials. In mesoscale, moving dislocations can be considered as particle diffusion, which are often stochastic and super-diffusive. Classical diffusion models fail to account for these phenomena and the long-range interactions of dislocations. Therefore, we propose the nonlocal transport model for the probability density and obtained the parameters of nonlocal operators using a machine learning framework.

Signal profiles and spatial regulation of β-arrestin recruitment through Gβ5 and GRK3 at the μ-opioid receptor

The μ-opioid receptor (MOR) is a G-protein-coupled receptor (GPCR) that mediates both analgesic effects and adverse effects of opioid drugs. Despite extensive efforts to develop a signal-biased drug, drugs with sufficiently reduced side effects have not been established, in part owing to lack of comprehensive signal transducer profiles of MOR. In this study, by profiling the activity of signal transducers including G proteins and GPCR kinases (GRKs), we revealed an unprecedented mechanism of selective GRK3 activation by Gβ5, leading to β-arrestin recruitment. By utilizing multiple genome-edited cell lines and functional assays, we found that oliceridine, an FDA-approved G-protein-biased agonist, selectively activates Gαz- and GRK3-mediated signaling. Notably, among the five Gβ subtypes, only Gβ5 distinguishes GRK3 from GRK2. Using single-molecule imaging, we found that GRK3 is recruited to the plasma membrane upon MOR agonist stimulation by Gβ1 and Gβ5, yet their interaction dynamics with GRK3 and mechanisms of action are different. Furthermore, particle diffusion analysis suggests that Gβ5 is enriched in confined membrane domains, through which GRK3 is recruited to the plasma membrane in a freely diffusible state, thereby allowing GRK3 to efficiently interact with MOR. These findings provide a mechanism by which MOR agonists rely on a specific Gα–Gβ–GRK axis to induce β-arrestin recruitment.

A concise approach for interpreting the removal kinetic of 2,4-D by carbonized peanut shell

The present study displays the adsorption characteristics of dichloro phenoxy acetic acid (2.4-D) on biomass-based peanut shell (BPC) carbon. Experiments were conducted by varying some parameters like concentration, pH, and temperature. 2,4-D adsorption uptake was increased from 0.504 to 3.826 mg/g with rising concentration from 1 µg/mL to 5 µg/mL and giving the highest adsorption at acidic pH. 1.605 mg/g uptake was obtained at pH 2.7. The data were in good harmony with Langmuir, Freundlic, Dubinin-Radushkevich (DR), and Generalized isotherms. The monolayer adsorption capacity was found as 42.62 ± 0.3257 mg/g. The kinetic was governed by the first-order kinetic. Various kinetic models were employed and particle diffusion was predominated for 2,4-D adsorption. Thermodynamics were explained in detail. The mean adsorption energy (E) was shown by the physical sorption, and Gibbs energy was the indicator of the spontaneous nature of the adsorption. Standard enthalpy, entropy, and Gibbs values were recorded as 5.064 ± 139.84, 18.849 ± 0.148, and 0,7414 ± 0.0943 J/mol. The results showed that BPC was an eco-friendly and cost-effective material.

Numerical Study of Multi-Term Time-Fractional Sub-Diffusion Equation Using Hybrid L1 Scheme with Quintic Hermite Splines

Anomalous diffusion of particles has been described by the time-fractional reaction–diffusion equation. A hybrid formulation of numerical technique is proposed to solve the time-fractional-order reaction–diffusion (FRD) equation numerically. The technique comprises the semi-discretization of the time variable using an L1 finite-difference scheme and space discretization using the quintic Hermite spline collocation method. The hybrid technique reduces the problem to an iterative scheme of an algebraic system of equations. The stability analysis of the proposed numerical scheme and the optimal error bounds for the approximate solution are also studied. A comparative study of the obtained results and an error analysis of approximation show the efficiency, accuracy, and effectiveness of the technique.

Effect of channel roughness on the particle diffusion and permeability of carbon nanotubes in reverse electrodialysis process applying molecular dynamics simulation

Innovative technology and methods are crucial for making pure and refreshing water. Two main methods are present to delete soluble salts from water: membrane processes and thermal processes. A beneficial membrane technique is reverse electrodialysis. This research used molecular dynamics (MD) simulation to investigate how channel roughness affected particle diffusion and permeability in carbon nanotubes (CNTs) via the reverse electrodialysis process. The results indicate that adding roughness in the CNT duct increased the force between the primary fluid and the duct. Using an armchair-edged CNT structure maximized the electric current in the sample. Furthermore, the roughness increased the intensity of force in the channel, which was due to gravity, leading to a decrease in the mobility of fluid particles. Additionally, several broken hydrogen bonds inside the simulation box increased from 116 to 128 in the duct sample with roughness.

Anomalous diffusion of active Brownian particles in responsive elastic gels: Nonergodicity, non-Gaussianity, and distributions of trapping times.

Understanding actual transport mechanisms of self-propelled particles (SPPs) in complex elastic gels-such as in the cell cytoplasm, in in vitro networks of chromatin or of F-actin fibers, or in mucus gels-has far-reaching consequences. Implications beyond biology/biophysics are in engineering and medicine, with a particular focus on microrheology and on targeted drug delivery. Here, we examine via extensive computer simulations the dynamics of SPPs in deformable gellike structures responsive to thermal fluctuations. We treat tracer particles comparable to and larger than the mesh size of the gel. We observe distinct trapping events of active tracers at relatively short times, leading to subdiffusion; it is followed by an escape from meshwork-induced traps due to the flexibility of the network, resulting in superdiffusion. We thus find crossovers between different transport regimes. We also find pronounced nonergodicity in the dynamics of SPPs and non-Gaussianity at intermediate times. The distributions of trapping times of the tracers escaping from "cages" in our quasiperiodic gel often reveal the existence of two distinct timescales in the dynamics. At high activity of the tracers these timescales become comparable. Furthermore, we find that the mean waiting time exhibits a power-law dependence on the activity of SPPs (in terms of their Péclet number). Our results additionally showcase both exponential and nonexponential trapping events at high activities. Extensions of this setup are possible, with the factors such as anisotropy of the particles, different topologies of the gel network, and various interactions between the particles (also of a nonlocal nature) to be considered.

Resetting by rescaling: Exact results for a diffusing particle in one dimension.

In this paper, we study a simple model of a diffusive particle on a line, undergoing a stochastic resetting with rate r, via rescaling its current position by a factor a, which can be either positive or negative. For |a|<1, the position distribution becomes stationary at long times and we compute this limiting distribution exactly for all |a|<1. This symmetric distribution has a Gaussian shape near its peak at x=0, but decays exponentially for large |x|. We also studied the mean first-passage time (MFPT) T(0) to a target located at a distance L from the initial position (the origin) of the particle. As a function of the initial position x, the MFPT T(x) satisfies a nonlocal second order differential equationand we have solved it explicitly for 0≤a<1. For -1<a≤0, we also solved it analytically but up to a constant factor κ whose value can be determined independently from numerical simulations. Our results show that, for all -1<a<1, the MFPT T(0) (starting from the origin) shows a minimum at r=r^{*}(a). However, the optimized MFPT T_{opt}(a) turns out to be a monotonically increasing function of a for -1<a<1. This demonstrates that, compared to the standard resetting to the origin (a=0), while the positive rescaling is not beneficial for the search of a target, the negative rescaling is. Thus resetting via rescaling followed by a reflection around the origin expedites the search of a target in one dimension.

Motility-Induced Pinning in Flocking System with Discrete Symmetry.

We report a motility-induced pinning transition in the active Ising model for a self-propelled particle system with discrete symmetry. This model was known to exhibit a liquid-gas type flocking phase transition, but a recent study reveals that the polar order is metastable due to droplet excitation. Using extensive MonteCarlo simulations, we demonstrate that, for an intermediate alignment interaction strength, the steady state is characterized by traveling local domains, which renders the polar order short-ranged in both space and time. We further demonstrate that interfaces between colliding domains become pinned as the alignment interaction strength increases. A resonating back-and-forth motion of individual self-propelled particles across interfaces is identified as a mechanism for the pinning. We present a numerical phase diagram for the motility-induced pinning transition, and an approximate analytic theory for the growth and shrink dynamics of pinned interfaces. Our results show that pinned interfaces grow to a macroscopic size preventing the polar order in the regime where the particle diffusion rate is sufficiently smaller than the self-propulsion rate. The growth behavior in the opposite regime and its implications on the polar order remain unresolved and require further investigation.

Dispersion and particle pressure in sedimenting suspensions with hydrodynamic interactions and particle inertia

Particle inertia is a feature of suspension dynamics that is often neglected. However, its neglect changes the order of the governing equations of particle motion. In this work, we examine the impact of the inertia of particles over their velocity fluctuations and the resulting stresses. In order to observe effects of particle inertia, we consider dusty-gas suspensions of spherical weakly Brownian microparticles sedimenting in a Newtonian fluid at low Reynolds numbers. We first investigate the motion of a single spherical particle. The simulations for this simple case allow us to define the appropriate physical parameters of the flow and to validate the numerical calculation of velocity statistics over a range of Péclet and Stokes numbers. Next, we perform Langevin dynamics simulations with periodic boundary conditions to integrate the equations governing the translational and rotational motions of N hydrodynamically interacting particles suspended in the viscous fluid. The first aim of this paper is to examine the behavior of the velocity variance of inertial particles, with small fluctuations produced by Brownian motion at the moderate Péclet numbers. The interplay between mild Brownian forces and hydrodynamic interactions decreases the anisotropy of velocity fluctuations observed in non-Brownian suspensions (Pe≫1) of sedimenting particles. The simulation results suggest that particle inertia attenuates the magnitude of velocity fluctuations produced by both Brownian motion and viscous hydrodynamic interactions. Additionally, we study the long-time behavior of the velocity fluctuations by calculating their autocorrelation functions and the particle diffusivities. The numerical simulations show clear evidence of particle pressure arising from the flow disturbance produced by hydrodynamic interactions in a suspension. From the particle velocity variance obtained in the present numerical simulations, we propose a simple model for particle-phase pressure as a linear function of the particle volume fraction ϕ⩽5%, which is usually a closure quantity required in models of more complex particulate systems such as fluidized beds.

Impact of spatially varying transport coefficients in EMC3-Eirene simulations of W7-X and assessment of drifts

Abstract Modelling the scrape-off layer of a stellarator is challenging due to the complex magnetic 3D geometry. The here presented study analyses simulations of the scrape-off layer (SOL) of the stellarator Wendelstein 7-X (W7-X) using spatially varying diffusion coefficients for the magnetic standard configuration, extending our previous study (Bold et al 2022 Nucl. Fusion 62 106011). Comparing the EMC3-Eirene simulations with experimental observations, an inconsistency between the strike-line width (SLW) and the upstream parameters was observed. While to match the experimental SLW a particle diffusion coefficient D ≈ 0.2 m2 s−1 is needed, D ≈ 1 m2 s−1 is needed to get experimental separatrix temperatures of 50 eV at the given experimental heating power. We asses the impact of physically motivated spatially varying transport coeffients. Agreement with experimental data can be improved, but various differences remain. We show that drifts are expected to help overcome the discrepancies and, thus, the development of SOL transport models including drifts is a necessary next step to study the SOL transport of the W7-X stellarator.

Research on visual features and identification of coal and gangue in simulated underground mine environments

ABSTRACT In underground mine environments, factors such as low light intensity, diffusion of dust particles, and coal slurry coverage interfere with coal gangue identification. To further realize coal-gangue sorting underground, we analyzed the coal-gangue visual characteristics coupled with the above factors. The analysis reveals that both smoke environments and coal slurry coverage tend to make the coal-gangue visual features more consistent. There are still differences in coal-gangue visual features under smoke environments, while coal slurry coverage leads to gangue exhibiting coal-related feature information. To improve the detection capability of coal gangue in underground environments, this study adopts a coal gangue recognition method based on the DG module and YOLOX-PSB network: Utilize the DG module to achieve dehazing and then use the YOLOX-PSB model for object detection. The proposed method was validated through ablation experiments, and the results showed that the detection accuracy reached 99.6%, with an FPS of 98. The proposed method enables real-time detection and exhibits the best robustness in complex environments, realizing reliable and efficient performance.

Mitochondrial Network Branching Enables Rapid Protein Spread with Slower Mitochondrial Dynamics

Mitochondrial network structure is controlled by the dynamical processes of fusion and fission, which merge and split mitochondrial tubes into structures including branches and loops. To investigate the impact of mitochondrial network dynamics and structure on the spread of proteins and other molecules through mitochondrial networks, we used stochastic simulations of two distinct quantitative models that each included mitochondrial fusion and fission, and particle diffusion via the network. Better-connected mitochondrial networks and networks with faster dynamics exhibit more rapid particle spread on the network, with little further improvement once a network has become well connected. As fragmented networks gradually become better connected, particle spread either steadily improves until the networks become well connected for slow-diffusing particles or plateaus for fast-diffusing particles. We compared model mitochondrial networks with both end-to-end and end-to-side fusion, which form branches, to nonbranching model networks that lack end-to-side fusion. To achieve the optimum (most rapid) spread that occurs on well-connected branching networks, nonbranching networks require much faster fusion and fission dynamics. Thus, the process of end-to-side fusion, which creates branches in mitochondrial networks, enables rapid spread of particles on the network with relatively slow fusion and fission dynamics. This modeling of protein spread on mitochondrial networks builds toward mechanistic understanding of how mitochondrial structure and dynamics regulate mitochondrial function. Published by the American Physical Society 2024

Pollutant Dispersion of Aircraft Exhaust Gas during the Landing and Takeoff Cycle with Improved Gaussian Diffusion Model

Evaluating aviation emissions and examining the dispersion properties of contaminants are crucial for understanding atmospheric pollution. To assess the pollutant emissions and dispersion of aircraft during the landing and takeoff (LTO) cycle, and address air pollution surrounding the airport resulting from flight operations, this study evaluated emissions throughout the LTO phase based on Quick Access Recorder (QAR) data in conjunction with the first-order approximation method. An improved Gaussian diffusion model for mobile point sources was employed to examine the diffusion characteristics of contaminants. Additionally, CFD calculation outcomes for various exhaust velocities and wind speeds were utilized to validate the trustworthiness of the improved Gaussian model. The discussion also encompasses the influence of diffusion time, wind direction, wind speed, temperature gradient, and particle deposition on the concentration distribution of contaminants. The findings indicated that the Gaussian diffusion model aligned with the results of the CFD calculations. The diffusion distribution of contaminants around airports varies over time and is significantly influenced by atmospheric environmental factors, including wind direction, wind speed, and atmospheric stability. Specifically, a change in wind direction from 0° to 45° caused a shift of approximately 1000 m in the contaminant’s center. An increase in wind speed from 3 m/s to 5 m/s led to a decrease in concentration by about 15%. Furthermore, a transition in atmospheric stability from category ‘a’ (very unstable) to ‘f’ (very stable) resulted in a two-order-of-magnitude increase in contaminant concentrations.