Particle Dynamics Research Articles

Dynamics of the surface growth resulted from sedimentation of spheres in a Hele–Shaw cell containing a low-viscosity fluid

In this paper, we investigate the dynamics of surface growth resulting from sedimentation of spherical granular particles in a fluid environment, using experiments and simulations. In the experimental part, spherical polystyrene particles are poured down from the top of a vertical Hele–Shaw cell and form a 1 + 1-dimensional growing surface. The surface roughness is obtained from the images, and the growth and roughness exponents are measured. In the numerical simulation part, the surface growth process is simulated using the discrete element method, considering the interactions between the grains, and the exponents are calculated. In this method, unlike conventional simulation models, instead of a discrete deposition law, the dynamics of the individual particles throughout the process are obtained, considering different forces acting on the particles. Finally, the simulation results are compared with the experiment, and we see a very good agreement between them. We find different values for the exponents using different methods, indicating that the system is multi-affine and does not obey the scaling laws of affine models.

A study of the equilibrium dynamics of the test particle in the collinear circular restricted four-body problem with non-spherical central primary

We consider the collinear restricted four-body problem (CR4BP), where the test particle of infinitesimal mass is moving under the gravitational influence of the three primary bodies. It is further assumed that the central primary is a non-spherical body, particularly either an oblate or prolate spheroid, whereas the peripheral primaries are shperical in shape. A numerical analysis is presented to unveil the effect of the oblateness and prolateness parameters on the position of equilibrium points (EPs) and their linear stability in the CR4BP. Moreover, the permissible regions of possible motion as determined by the zero-velocity surface and associated equipotential curves and the basins of convergence linked with the EPs on the orbital plane are presented. The existence and number of collinear EPs and non-collinear EPs in the problem depend on the combination of the mass parameter of the primaries and the oblateness/prolateness parameter. Additionally, the application of the problem in the Saturn-Moon(1)-Moon(2)-System has been presented.

Investigation of morphology and structure of drug-loaded PLA-b-PEO-b-PLA polymeric micelle: A dissipative particle dynamics simulations study.

The dissipative particle dynamics (DPD) simulation was used to study the morphologies and structures of the paclitaxel-loaded PLA-b-PEO-b-PLA polymeric micelle. We focused on the influences of PLA block length, PLA-b-PEO-b-PLA copolymer concentration, paclitaxel drug content on morphologies and structures of the micelle. Our simulations show that: (i) with the PLA block length increase, the self-assemble structure of PLA-b-PEO-b-PLA copolymers with paclitaxel vary between onion-like structure (core-middle layer-shell) to spherical core-shell structure. The PEO shell thins and the size of the PLA core increases. The onionlike structures are comprised of the PEO hydrophilic core, the PLA hydrophobic middle layer, and the PEO hydrophilic shell, the distribution of the paclitaxel drug predominantly occurs within the hydrophobic intermediate layer; (ii) The system forms a spherical core-shell structure when a small amount of the drug is added, and within a certain range, the size of the spherical structure increases as the drug amount increases. When the drug contents (volume fraction) cdrug = 10%, it can be observed that the PLA4-b-PEO19-b-PLA4 spherical structures connect to form rod-shaped structures. With the length of PLA block NPLA = 8, as the paclitaxel drug concentrations cdrug = 4%, PEO has been insufficient to completely encapsulate the PLA and paclitaxel drug beads. To enhance drug loading capacity while maintaining stability of the system in aqueous solution, the optimal composition for loading paclitaxel is PLA4-b-PEO19-b-PLA4; the drug content is not higher than 4%; (iii) The paclitaxel-loaded PLA4-b-PEO19-b-PLA4 micelle undergo the transition from onionlike (core-middle layer-shell) to spherical (core-shell) to rod-shaped and lamellar structure as the PLA4-b-PEO19-b-PLA4 copolymer concentration increases from ccp = 10% to 40%.

Understanding self-assembly mechanisms in supramolecular fiber materials through multiscale simulation

Supramolecular materials, assembled through non-covalent interactions, have emerged as a versatile and tunable platform for the development of advanced functional materials with tailored properties and applications. However, comprehending the interactions between self-assembled monomers at the molecular level, particularly when organized into fibrous structures, remains a considerable challenge. In this study, we discovered that solely relying on all-atomistic molecular dynamics simulations is insufficient to reproduce the self-assembly process and the formation of fibrous supramolecular structures. Thus, we combined dissipative particle dynamics (DPD) and all-atomistic molecular dynamic simulations to accurately simulate the fibrous supramolecular structures previously observed in experiments. These structures consist of benzene-1,3,5-tricarboxamide (BTA-EG4), 2-ureido-4 [1H]-pyrimidinone-ethylene glycol molecules (UPy-EG), and glycyrrhizic acid (GA) molecules, each exhibiting varying degrees of topology and planarity. Our analyses focused on elucidating the stability mechanism of these fibrous structures. Notably, we found that hydrogen bonds, π-π interactions and nonbonded interactions, especially Coulomb interaction between monomers and water molecules, play pivotal roles in stabilizing these fibrous structures. This study significantly contributes to our understanding of self-assembly mechanism in supramolecular fiber materials and opens avenues for extending this knowledge to other fibrous supramolecular architectures.

Multi-wavelength pulse profiles from the force-free neutron star magnetosphere

The last two decades have witnessed dramatic progress in our understanding of neutron star magnetospheres thanks to force-free and particle-in-cell simulations. However, the associated particle dynamics and its emission mechanisms and locations have not been fully constrained, notably in X-rays. In this paper, we compute a full atlas of radio, X-ray, and gamma -ray pulse profiles, relying on the force-free magnetosphere model. Our goal is to use such a data bank of multi-wavelength profiles to fit a substantial number of radio-loud gamma -ray pulsars that have also been detected in non-thermal X-rays to decipher the X-ray radiation mechanism and sites. Using results from the third gamma -ray pulsar catalogue (3PC), we investigate the statistical properties of this population. We assume that radio emission emanates from field lines rooted to the polar caps, at varying height above the surface, close to the surface, at an altitude about 5-10<!PCT!> of the light cylinder radius, rlight . The X-ray photons are produced in the separatrix region within the magnetosphere; that is, the current sheet formed by the jump from closed to open magnetic field lines. We allow for substantial variations in emission height. The gamma -rays are produced within the current sheet of the striped wind, outside the light cylinder. A comprehensive set of radio, X-ray, and gamma -ray light curves was computed. Based on only geometric considerations about magnetic obliquity, line-of-sight inclination, and the radio beam cone opening angle, pulsars can be classified as radio-loud or quiet and as gamma -ray-loud or quiet. We found that the 3PC sample is compatible with an isotropic distribution of obliquity and line of sight. The atlases constructed in this work are the fundamental tools with which to explore individual pulsars and fit their multi-wavelength pulse profiles in order to constrain their magnetic topology, the emission sites, and the observer's line of sight.

Cholesteric liquid crystal phase variations within nanotubes: an in-depth analysis of the influences of twist radius and molecular pitch

ABSTRACT This study investigates the self-assembly and phase transitions of cholesteric liquid crystals (CLCs) when confined in nanotubes, specifically focusing on the variations in their molecular structures. A dissipative particle dynamics method was used to investigate how the changes in helix radius ( r LC ) and pitch ( P ) affect the behaviour of the LCs. For pitches of 24, 12, and 6, the orientation order parameter ( P 2 ) showed distinct trends during cooling. Smaller helix radii resulted in isotropic to nematic to smectic phase transitions, whereas larger radii resulted in a novel smectic phase with spiral structures (Sm s ). Circumferential local-order parameters ( P 2 r and P 2 z ) were introduced to better characterise the phases, and a disordered spiral-structure phase (Sm s 2 ) with local perturbations was observed. The transitions between isotropic, nematic, and smectic phases, highlighting the influence of the pitch and helix radius, were observed from the phase diagrams. Smaller pitches and radii prevented the formation of stable structures; however, specific combinations induced characteristic spiral structures in the smectic phase. These results provide new insights into the self-assembly of confined CLCs, which have potential applications in the design of photonic devices and LC-based sensors. This study advances our understanding of phase control in quasi-one-dimensional systems and highlights the importance of molecular variations in liquid crystal behaviour.

A neural network-based algorithm for the reconstruction and filtering of single particle trajectory in magnetic particle tracking.

Magnetic particle tracking (MPT) is a recently developed non-invasive measurement technique that has gained popularity for studying dense particulate or granular flows. This method involves tracking the trajectory of a magnetically labeled particle, the field of which is modeled as a dipole. The nature of this method allows it to be used in opaque environments, which can be highly beneficial for the measurement of dense particle dynamics. However, since the magnetic field of the particle used is weak, the signal-to-noise ratio is usually low. The noise from the measuring devices contaminates the reconstruction of the magnetic tracer's trajectory. A filter is then needed to reduce the noise in the final trajectory results. In this work, we present a neural network-based framework for MPT trajectory reconstruction and filtering, which yields accurate results and operates at very high speed. The reconstruction derived from this framework is compared to the state-of-the-art extended Kalman filter-based reconstruction.

Dynamics of charged particles in a solenoid and the accurate determination of the magnetic axis of a solenoid using measured field data.

A fundamental understanding of the theory and measurement procedures for characterizing a solenoid is crucial for making a project cost-effective and achieving the desired performance for its use in a LINAC. This paper attempts to comprehend the beam dynamics of charged particles by considering the conservation of canonical angular momentum, which can take on positive, negative, and zero values. The analytical expression to generate a parallel beam using a solenoid is derived. The effect of the final beam size on the values of a solenoid's spherical aberration coefficient and the position of the minimum beam size with respect to the focal length is studied using electron trajectories. A practical procedure to tune the above two parameters of a solenoid is demonstrated by simply modifying the demountable disks connected at each end. A mathematical model is proposed to accurately determine the axis of a solenoid and the magnetic centre using the measured magnetic field data, which is validated experimentally. Several solenoids, used in the 9.5MeV 10kW electron LINAC suitable for sterilizing medical products and food irradiation, have been characterised using this procedure.

Mass-Based Separation of Active Brownian Particles in an Asymmetric Channel

Inertial effects should be considered for micro and nanoswimmers moving in a low-density medium confined by irregular structures that create entropic barriers, where viscous effects are no longer paramount. Here, we present a separation mechanism of self-propelled particles in a two-dimensional asymmetric channel, which leads to the drift of particles of different masses in opposite directions. In particular, this mechanism is based on the combined action of the spatial asymmetry of the channel structure, the temporal asymmetry inherent in particles dynamics and an external static force. This work is relevant for potential applications that can be found in the development of lab-on-a-chip devices and artificial channels for separating particles of different masses.

Unveiling Energetic Particle Dynamics in the Near‐Earth Environment From CubeSat Missions

AbstractThe discovery of the Van Allen radiation belts marked a prominent milestone in space physics. Recent advances, through the measurements of two CubeSat missions, have shed new light on the dynamics of energetic particles in the near‐Earth environment. Measurements from CSSWE, a student‐led mission, revealed that the decay of low‐energy neutrons, associated with cosmic rays impacting the atmosphere, is the primary source of relativistic electrons at the inner edge of the inner belt (Li et al., Nature, 2017, https://doi.org/10.1038/nature2464). Recently CIRBE captured striking details of energetic electron dynamics (Li et al., GRL, 2024, https://doi.org/10.1029/2023gl107521), further demonstrating high‐quality science achievable with CubeSat missions.

Numerical analysis of residence time for particle impregnation in a rotating drum

The impregnation is a key step for handling particles in various chemical engineering processes, which requires uniform distribution of active ingredients over particles. This paper describes the effects of particle residence time in the impregnation area on the inter-particle impregnation variability in a rotating drum. The discrete element method is used to simulate the particle dynamics and the image analysis method is used to model the spray impregnation process. It is observed that the coefficient of inter-particle impregnation variability decreases over time and converges to an asymptotic value, which depends on the particle size and the width of the impregnation area. The dead zone of impregnation near the inclined side walls of the drum is identified, which can be reduced by widening the impregnation area. In addition, the relative standard deviation of residence time for particles in the impregnation area also decrease with the increase of particle size and impregnation area width. With various particle sizes and impregnation areas, it is found that the inter-particle impregnation variability is linearly related to the relative standard deviation of the residence time of the particles in the impregnation area, since the exposure of particles in the spray area controls the impregnation over particles. This study provides fundamental insights for optimizing the particle impregnation process in a rotating drum.

Collapse and expansion kinetics of a single polyelectrolyte chain with hydrodynamic interactions.

We investigate the collapse and expansion dynamics of a linear polyelectrolyte (PE) with hydrodynamic interactions. Using dissipative particle dynamics with a bead-spring PE model, long-range electrostatics, and explicit ions, we examine how the timescales of collapse tcol and expansion texp depend on the chain length N and obtain scaling relationships tcol ∼ Nα and texp ∼ Nβ. For neutral polymers, we derive values of α = 0.94 ± 0.01 and β = 1.97 ± 0.10. Interestingly, the introduction of electrostatic interaction markedly shifts α to α ≈ 1.4 ± 0.1 for salt concentrations within c = 10-4 to 10-2M. A reduction in the ion-to-monomer size ratio noticeably reduces α. On the other hand, the expansion scaling remains approximately constant, β ≈ 2, regardless of the salt concentration or ion size considered. We find β > α for all conditions considered, implying that expansion is always slower than collapse in the limit of long polymers. This asymmetry is explained by distinct kinetic pathways of collapse and expansion processes.

Coarse-grained simulation of thermal conductivity of boron nitride/epoxy composites based on DPD and SPH method

In this study, thermal conductivity (TC) of BN/polymer composites is investigated utilizing dissipative particle dynamics (DPD) and smoothed-particle hydrodynamics (SPH) methods by establishing the coarse-grained (CG) models of boron nitride nanotube (BNNT) and boron nitride nanosheet (BNNS). The Iterative Boltzmann inversion (IBI) approach is employed to calculate the parameters of the potential function of the hexagonal boron nitride (h-BN) coarse grain model for DPD method. Mesoscopic simulations on the TC of BNNT/EP, BNNS/EP, and BNNT/BNNS/EP composites are performed using SPH and DPD simulations to investigate the effects of nanofiller ratio, aspect ratio, size, and orientation on TC. The CG models established for the BNNT and BNNS structures are proven to be efficient in DPD and SPH simulations for predicting the TC of h-BN/polymer composites. Furthermore, it is demonstrated that the TC of BNNT/BNNS/EP composites is superior to that of BNNT/EP and BNNS/EP composites at the same filler ratio due to the synergistic effect of BNNT and BNNS.

Simulating Brownian motion in thermally fluctuating viscoelastic fluids by using the smoothed profile method

To investigate the Brownian motion of individual particles suspended in viscoelastic fluids, the stochastic smoothed profile method (SPm) for direct numerical simulation is developed by extending deterministic SPm for suspensions in viscoelastic media. To simulate viscoelastic flow driven by thermal fluctuations in the suspending medium, the random stress in the fluid momentum equation as well as the random driving force for the conformation tensor in the Oldroyd-B model are incorporated according to the fluctuating hydrodynamics and fluctuating viscoelasticity formalisms. The thermal equilibrium and dynamical properties calculated by using numerical simulations successfully reproduce the analytic predictions, validating the direct simulation for the coupled fluctuating Navier–Stokes and Oldroyd-B equations and for the coupling between the stochastic viscoelastic medium and individual particles. As an application of the stochastic SPm, we investigate finite system-size effects under periodic boundary conditions (PBCs) on the passive microrheological relationship between the mean-square displacement (MSD) of a Brownian particle and the medium's dynamic modulus. Comparing the modulus that was microrheologically calculated from the MSD with the input modulus reveals that the effect of periodic image cell interaction appears not only in the long-time diffusive regime but also in the short-time region. A frequency-dependent finite system-size correction is implemented by phenomenologically extending the long-time diffusive regime correction, allowing passive microrheology analysis under PBCs. This result can be directly applied to other mesoscale numerical simulations including coarse-grained molecular dynamics and dissipative particle dynamics simulations.

Expanding the Palette of SWIR Emitting Nanoparticles Based on Au Nanoclusters for Single-Particle Tracking Microscopy.

Single-molecule localization microscopy has proved promising to unravel the dynamics and molecular architecture of thin biological samples down to nanoscales. For applications in complex, thick biological tissues shifting single-particle emission wavelengths to the shortwave infrared (SWIR also called NIR II) region between 900 to 2100nm, where biological tissues are more transparent is key. To date, mainly single-walled carbon nanotubes (SWCNTs) enable such applications, but they are inherently 1D objects. Here, 0D ultra-small luminescent gold nanoclusters (AuNCs, <3nm) and ≈25nm AuNC-loaded-polymeric particles that can be detected at the single-particle level in the SWIR are presented. Thanks to high brightness and excellent photostability, it is shown that the dynamics of the spherical polymeric particles can be followed at the single-particle level in solution at video rates for minutes. We compared single particle tracking of AuNC-loaded-polymeric particles with that of SWCNT diffusing in agarose gels demonstrating the specificity and complementarity of diffusion properties of these SWIR-emitting nano-objects when exploring a complex environment. This extends the library of photostable SWIR emitting nanomaterials to 0D nano-objects of variable size for single-molecule localization microscopy in the second biological window, opening unprecedented possibilities for mapping the structure and dynamics of complex biological systems.

Characterization of partial wetting by CMAS droplets using multiphase many-body dissipative particle dynamics and data-driven discovery based on PINNs

The molten sand that is a mixture of calcia, magnesia, alumina and silicate, known as CMAS, is characterized by its high viscosity, density and surface tension. The unique properties of CMAS make it a challenging material to deal with in high-temperature applications, requiring innovative solutions and materials to prevent its buildup and damage to critical equipment. Here, we use multiphase many-body dissipative particle dynamics simulations to study the wetting dynamics of highly viscous molten CMAS droplets. The simulations are performed in three dimensions, with varying initial droplet sizes and equilibrium contact angles. We propose a parametric ordinary differential equation (ODE) that captures the spreading radius behaviour of the CMAS droplets. The ODE parameters are then identified based on the physics-informed neural network (PINN) framework. Subsequently, the closed-form dependency of parameter values found by the PINN on the initial radii and contact angles are given using symbolic regression. Finally, we employ Bayesian PINNs (B-PINNs) to assess and quantify the uncertainty associated with the discovered parameters. In brief, this study provides insight into spreading dynamics of CMAS droplets by fusing simple parametric ODE modelling and state-of-the-art machine-learning techniques.

A tri-variate moment projection method for multi-dimensional particle population balance dynamics

This study develops a tri-variate moment projection method (TVMPM) for solving particle dynamics problems governed by the population balance equations involving any three internal coordinates of particles, such as volume, surface area, component mass, etc. By leveraging the concept of conditional moments, the multi-dimensional moments are expressed as the product of marginal moments and several conditional moments, facilitating the solution of high-dimensional moments. To obtain abscissas and conditional weights in different dimensions for reconstructing moments, a three-dimensional (3-D) adaptive Blumstein-Wheeler algorithm is proposed and implemented, which can also improve the stability in the third dimension, where ill-conditioning issues are more likely to arise. More importantly, by fixing the smallest particle abscissas, TVMPM can directly track the weight of the smallest particles, thereby closing the shrinkage and fragmentation terms. An analysis of the sources of errors arising from this approach is also presented. To mitigate potential interference from external factors, constant kernels for inception, growth, coagulation, shrinkage and fragmentation are employed to validate the effectiveness of TVMPM. The resulting moments from TVMPM are computed for both individual and combined processes, and subsequently compared with the moments derived from the direct simulation algorithm (DSA). The results demonstrate that TVMPM maintains a high level of accuracy across various numbers of quadrature nodes for particle dynamics with 3-D internal coordinates, while significantly reducing computational efforts compared to DSA. This study reveals that the developed algorithms and framework are promising for further extending MPM to higher dimensions. Moreover, owing to its accuracy and efficiency, TVMPM shows great potential for implementation in computational fluid dynamics (CFD) for particle dynamics in realistic systems.

Dwell Times, Wavepacket Dynamics, and Quantum Trajectories for Particles with Spin 1/2.

The theoretical connections between quantum trajectories and quantum dwell times, previously explored in the context of 1D time-independent stationary scattering applications, are here generalized for multidimensional time-dependent wavepacket applications for particles with spin 1/2. In addition to dwell times, trajectory-based dwell time distributions are also developed, and compared with previous distributions based on the dwell time operator and the flux-flux correlation function. Dwell time distributions are of interest, in part because they may be of experimental relevance. In addition to standard unipolar quantum trajectories, bipolar quantum trajectories are also considered, and found to relate more directly to the dwell time (and other quantum time) quantities of greatest relevance for scattering applications. Detailed calculations are performed for a benchmark 3D spin-1/2 particle application, considered previously in the context of computing quantum arrival times.

Characterizing the Microparticles Deposition Structure and its Photonic Nature in Surfactant-Laden Evaporating Colloidal Sessile Droplets.

This work presents a simple method to create photonic microstructures via the natural evaporation of surfactant-laden colloidal sessile droplets on a flat substrate. In the absence of dissolved surfactant, the evaporating colloidal droplet forms a well-known coffee ring deposition. In contrast, the presence of surfactant leads to the formation of multiple ring structures due to the repetitive pinning-depinning behavior of the droplet contact line (CL). It is found that the multiring structure shows vibrant iridescent structural colors while the coffee ring lacks a photonic nature. This difference in the structural color for the presence and absence of the surfactant is found to be dependent on the arrangement of the particles in the deposition structure. The particle arrangement in the multirings is monolayered and well-ordered. The ordering of the particles is strongly influenced by the particle dynamics, contact angle (CA), and CL dynamics of the evaporating colloidal solution droplet. Furthermore, the iridescent nature of the multiring deposition is demonstrated and explained. The dependence of the multiring deposition structure on the concentration of the dissolved surfactant and the suspended particles is also studied. The findings demonstrate that an intermediate surfactant concentration is desirable for the formation of a multiring structure. Further, the pinning-depinning CL dynamics that causes the formation of the multiring deposition structure is discussed. Finally, we demonstrate the applicability of the approach to smaller droplet volumes.

Collective relational inference for learning heterogeneous interactions

Interacting systems are ubiquitous in nature and engineering, ranging from particle dynamics in physics to functionally connected brain regions. Revealing interaction laws is of fundamental importance but also particularly challenging due to underlying configurational complexities. These challenges become exacerbated for heterogeneous systems that are prevalent in reality, where multiple interaction types coexist simultaneously and relational inference is required. Here, we propose a probabilistic method for relational inference, which possesses two distinctive characteristics compared to existing methods. First, it infers the interaction types of different edges collectively by explicitly encoding the correlation among incoming interactions with a joint distribution, and second, it allows handling systems with variable topological structure over time. We evaluate the proposed methodology across several benchmark datasets and demonstrate that it outperforms existing methods in accurately inferring interaction types. The developed methodology constitutes a key element for understanding interacting systems and may find application in graph structure learning.