Brownian Dynamics Simulations Research Articles

Chemical herding as a multiplicative factor for top-down manipulation of colloids.

Colloidal particles can create reconfigurable nanomaterials, with applications such as color-changing, self-repairing, and self-regulating materials and reconfigurable drug delivery systems. However, top-down methods for manipulating colloids are limited in the scale they can control. We consider here a new method for using chemical reactions to multiply the effects of existing top-down colloidal manipulation methods to arrange large numbers of colloids with single-particle precision, which we refer to as chemical herding. Using simulation-based methods, we show that if a set of chemically active colloids (herders) can be steered using external forces (i.e., electrophoretic, dielectrophoretic, magnetic, or optical forces), then a larger set of colloids (followers) that move in response to the chemical gradients produced by the herders can be steered using the control algorithms given in this paper. We also derive bounds that predict the maximum number of particles that can be steered in this way, and we illustrate the effectiveness of this approach using Brownian dynamics simulations. Based on the theoretical results and simulations, we conclude that chemical herding is a viable method for multiplying the effects of existing colloidal manipulation methods to create useful structures and materials.

Robustness of traveling states in generic nonreciprocal mixtures.

Emergent nonreciprocal interactions violating Newton's third law are widespread in out-of-equilibrium systems. Phase separating mixtures with such interactions exhibit traveling states with no equilibrium counterpart. Using extensive Brownian dynamics simulations, we investigate the existence and stability of such traveling states in a generic nonreciprocal particle system. By varying a broad range of parameters including aggregate state of mixture components, diffusivity, degree of nonreciprocity, effective spatial dimension and density, we determine that traveling states do exist below the predator-prey regime, but nonetheless are only found in a narrow region of the parameter space. Our work also sheds light on the physical mechanisms for the disappearance of traveling states when relevant parameters are being varied, and has implications for a range of nonequilibrium systems including nonreciprocal phase separating mixtures, nonequilibrium pattern formation and predator-prey models.

Mutual information as a measure of mixing efficiency in viscous fluids

Because of the kinematic reversibility of the Stokes equation, fluid mixing at the microscale requires an interplay between advection and diffusion. Here, we introduce mutual information between particle positions before and after mixing as a measure of mixing efficiency. We demonstrate its application in a Couette flow in an annulus and show that nonuniform rotation sequences can lead to more efficient mixing. We also determine mutual information from Brownian dynamics simulations using data compression algorithms. Our results show that mutual information provides a universal and assumption-free measure of mixing efficiency in microscale flows. Published by the American Physical Society 2024

Lane formation in gravitationally driven colloid mixtures consisting of up to three different particle sizes.

Brownian dynamics simulations are utilized to study segregation phenomena far from thermodynamic equilibrium. In the present study, we expand upon the analysis of binary colloid mixtures and introduce a third particle species to further our understanding of colloidal systems. Gravitationally driven, spherical colloids immersed in an implicit solvent are confined in two-dimensional linear microchannels. The interaction between the colloids is modeled by the Weeks-Chandler-Andersen potential, and the confinement of the colloids is realized by hard walls based on the solution of the Smoluchowski equationin half space. In binary and ternary colloidal systems, a difference in the driving force is achieved by differing colloid sizes but fixed mass density. We observe for both the binary and ternary systems that a driving force difference induces a nonequilibrium phase transition to lanes. For ternary systems, we study the tendency of lane formation to depend on the diameter of the medium-sized colloids. Here we find a sweet spot for lane formation in ternary systems. Furthermore, we study the interaction of two differently sized colloids at the channel walls. Recently we observed that driven large colloids push smaller colloids to the walls. This results in small particle lanes at the walls at early simulation times. In this work we additionally find that thin lanes are unstable and dissolve over very long time frames. Furthermore, we observe a connection between lane formation and the nonuniform distribution of particles along the channel length. This nonuniform distribution occurs either alongside lane formation or in shared lanes (i.e., lanes consisting of two colloid types).

Nonequilibrium dynamics and entropy production of a trapped colloidal particle in a complex nonreciprocal medium.

We discuss the two-dimensional motion of a Brownian particle that is confined to a harmonic trap and driven by a shear flow. The surrounding medium induces memory effects modeled by a linear, typically nonreciprocal coupling of the particle coordinates to an auxiliary (hidden) variable. The system's behavior resulting from the microscopic Langevin equationsfor the three variables is analyzed by means of exact moment equationsderived from the Fokker-Planck representation, and numerical Brownian dynamics simulations. Increasing the shear rate beyond a critical value we observe, for suitable coupling scenarios with nonreciprocal elements, a transition from a stationary to a nonstationary state, corresponding to an escape from the trap. We analyze this behavior, analytically and numerically, in terms of the associated moments of the probability distribution, and from the perspective of nonequilibrium thermodynamics. Intriguingly, the entropy production rate remains finite when crossing the stability threshold.

Activity-induced diffusion recovery in crowded colloidal suspensions.

We show that the forces generated by active enzyme molecules are strong enough to influence the dynamics of their surroundings under artificial crowded environments. We measured the behavior of polymer microparticles in a quasi-two-dimensional system under aqueous environment, at various area fraction values of particles. In the presence of enzymatic activity, not only was the diffusion of the suspended particles enhanced at shorter time-scales, but the system also showed a transition from subdiffusive to diffusive dynamics at longer time-scale limits. Similar observations were also recorded with enzyme-functionalized microparticles. Brownian dynamics simulations have been performed to support the experimental observations.

Competition between physical search and a weak-to-strong transition rate-limits kinesin binding times.

The self-organization of cells relies on the profound complexity of protein-protein interactions. Challenges in directly observing these events have hindered progress toward understanding their diverse behaviors. One notable example is the interaction between molecular motors and cytoskeletal systems that combine to perform a variety of cellular functions. In this work, we leverage theory and experiments to identify and quantify the rate-limiting mechanism of the initial association between a cargo-bound kinesin motor and a microtubule track. Recent advances in optical tweezers provide binding times for several lengths of kinesin motors trapped at varying distances from a microtubule, empowering the investigation of competing models. We first explore a diffusion-limited model of binding. Through Brownian dynamics simulations and simulation-based inference, we find this simple diffusion model fails to explain the experimental binding times, but an extended model that accounts for the ADP state of the molecular motor agrees closely with the data, even under the scrutiny of penalizing for additional model complexity. We provide quantification of both kinetic rates and biophysical parameters underlying the proposed binding process. Our model suggests that a typical binding event is limited by ADP state rather than physical search. Lastly, we predict how these association rates can be modulated in distinct ways through variation of environmental concentrations and physical properties.

Enzymatic Reaction Enhanced Diffusion Accelerates Cargo Transport through Micro/Nano-Channels.

Energy conversion and transfer of enzyme-catalyzed reactions at molecular level are an interesting and challenging scientific topic that helps understanding biological processes in nature. In this study, it is demonstrated that enzyme-catalyzed reactions can enhance diffusion of surrounding molecules and thus accelerate cargo transport within 1D micro/nanochannels. Specifically, urease is immobilized on the inner walls of silica micro/nano-tubes to construct bio-catalytically active micro/nanochannels. The catalytic reaction inside the channels drives a variety of cargoes, including small dye molecules, polymers, and rigid nanoparticles (e.g., quantum dots, QDs), to pass through these micro/nanochannels much faster than they will by free diffusion. The enhanced diffusion of molecular species inside the channels is validated by direct observation of the Brownian motion of tracer particles, and further confirmed by significantly enhanced Raman intensity of reporter molecules. Finite element and Brownian dynamics simulations provide a theoretical understanding of these experimental observations. Furthermore, the effect of the channels' size on the diffusion enhancement is examined. The acceleration effect of the cargo transport through these enzymatically active micro/nanochannels can be turned on or off via chemical activators or inhibitors. This study provides valuable insights on the design of biomimetic channels capable of controlled and efficient transmembrane transport.

Approaches for fast Brownian dynamics simulation with constraints

Constraints can be used to eliminate quickly fluctuating degrees of freedom in dynamical systems enabling solutions that resolve the behavior of the system with fewer time steps over long time scales. In Brownian dynamics models, these constraints lead to linear equations which represent saddle point problems describing the velocity tangent to the constraint manifold and the forces resulting from imposition of the constraint on the dynamics. Simulations of these constrained systems must solve these saddle point problems, which are often ill-conditioned and require clever preconditioning in order to facilitate large scale simulation using iterative methods like GMRES. In this work, we show that the projected conjugate gradient (PrCG) method, which applies conjugate gradients projected onto the tangent space of the constraint manifold, has certain advantages compared to preconditioned iterative methods like GMRES, without the need to devise and compute a preconditioner. Specifically, for simulations of beads interacting hydrodynamically with commonly encountered holonomic constraints, PrCG exhibits linear scaling with the number of beads similar to GMRES. However, because PrCG does not require a preconditioner it often requires less precomputational effort both in terms of computational complexity and storage, depending on the nature of the constraints. Furthermore, PrCG produces iterates which are always feasible, allowing for independent tuning of the error tolerance on the bead velocities from the error tolerance on the constraint equations. We demonstrate PrCG's efficiency by solving several example low Reynolds number systems involving rigid bodies, freely jointed chains, and immobile particles, as well as a mixed constraint problem with both a rigid body and immobile particles, comparing to GMRES and analytical solutions, when available.

Living Growth Kinetics of Polymeric Micelles on a Substrate.

Living growth of micelles on the substrate is an intriguing phenomenon; however, little is known about its growth kinetics, especially from a theoretical viewpoint. Here, we examine the living growth kinetics of polymeric micelles on a hydrophobic substrate immersed in an aqueous solution. The block copolymers first assemble into short cylinder seeds anchored on the substrate. Then, the small aggregates of block copolymers in the solutions fuse onto the active ends of the anchored seeds, leading to micelle growth on the substrate. A theoretical model is proposed to interpret such living growth kinetics. It is revealed that the growth rate coefficient on the substrate is independent of the copolymer concentration and the multistep feedings; however, it is significantly affected by the surface hydrophobicity. Brownian dynamics simulations further support the proposed growth mechanism and the kinetic model. This work enriches living assembly systems and provides guidance for fabricating bioinspired surface nanostructures.

Observation of liquid glass in molecular dynamics simulations.

Molecular anisotropy plays an important role in the glass transition of a liquid. Recently, a novel bulk glass state has been discovered by optical microscopy experiments on suspensions of ellipsoidal colloids. "Liquid glass" is a disordered analog of a nematic liquid crystal, in which rotation motion is hindered but particles diffuse freely. Global nematic order is suppressed as clusters of aligned particles intertwine. We perform Brownian dynamics simulations to test the structure and dynamics of a dense system of soft ellipsoidal particles. As seen in the experiments and in accordance with predictions from the mode coupling theory, on the time scale of our simulations, rotation motion is frozen but translation motion persists in liquid glass. Analyses of the dynamic structure functions for translation and rotation corroborates the presence of two separate glass transitions for rotation and translation, respectively. Even though the equilibrium state should be nematic, aligned structures remain small and orientational order rapidly decays with increasing size. Long-wavelength fluctuations are remnants of the isotropic-nematic transition.

Exploring Mechanisms of Lipid Nanoparticle-Mucus Interactions in Healthy and Cystic Fibrosis Conditions.

Mucus forms the first defense line of human lungs, and as such hampers the efficient delivery of therapeutics to the underlying epithelium. This holds particularly true for genetic cargo such as CRISPR-based gene editing tools which cannot readily surmount the mucosal barrier. While lipid nanoparticles (LNPs) emerge as versatile non-viral gene delivery systems that can help overcome the delivery challenge, many knowledge gaps remain, especially for diseased states such as cystic fibrosis (CF). This study provides fundamental insights into Cas9 mRNA or ribonucleoprotein-loaded LNP-mucus interactions in healthy and diseased states by assessing the impact of the genetic cargo, mucin sialylation, mucin concentration, ionic strength, pH, and polyethylene glycol (PEG) concentration and nature on LNP diffusivity leveraging experimental approaches and Brownian dynamics (BD) simulations. Taken together, this study identifies key mucus and LNP characteristics that are critical to enabling a rational LNP design for transmucosal delivery.

Elastic particle model for coil-stretch transition of dilute polymers in an elongational flow

The phenomenon of the ‘coil-stretch’ (C-S) transition, wherein a long-chain polymer initially in a coiled state undergoes a sudden configuration change to become fully stretched under steady elongational flows, has been widely recognized. This transition can display intricate hysteresis behaviours under specific critical conditions, giving rise to unique rheological characteristics in dilute polymer solutions. Historically, microscopic stochastic models and Brownian dynamics simulations have shed light on the underlying mechanisms of the transition by uncovering bistable configurations of polymer chains. Following the initial work by Cerf (J. Chem. Phys., vol. 20, 1952, pp. 395–402), we introduce a continuum model in this study to investigate the C-S transition in a constant uniaxial elongational flow. Our approach involves approximating the unfolding process of the polymer chain as an axisymmetric deformation of an elastic particle. We make the assumption that the particle possesses uniform material properties, which can be represented by a nonlinear, strain-hardening constitutive equation to replicate the finite extensibility of the polymer chain. Subsequently, we analytically solve for the steady-state deformation using a polarization method. By employing this reduced model, we effectively capture the C-S transition and establish its specific correlations with material and geometric properties. The hysteresis phenomena can be comprehended through a force-balance analysis, which involves comparing the externally applied viscous forces with the intrinsic elastic responsive forces. We demonstrate that our model, while simple, unveils rich elastohydrodynamics of the C-S transition.

Mechanism of DNA origami folding elucidated by mesoscopic simulations

Many experimental and computational efforts have sought to understand DNA origami folding, but the time and length scales of this process pose significant challenges. Here, we present a mesoscopic model that uses a switchable force field to capture the behavior of single- and double-stranded DNA motifs and transitions between them, allowing us to simulate the folding of DNA origami up to several kilobases in size. Brownian dynamics simulations of small structures reveal a hierarchical folding process involving zipping into a partially folded precursor followed by crystallization into the final structure. We elucidate the effects of various design choices on folding order and kinetics. Larger structures are found to exhibit heterogeneous staple incorporation kinetics and frequent trapping in metastable states, as opposed to more accessible structures which exhibit first-order kinetics and virtually defect-free folding. This model opens an avenue to better understand and design DNA nanostructures for improved yield and folding performance.

Magneto-Permeability Effect in Ferrofluid Flow Through Porous Media Studied via Multiparticle Collision Dynamics

AbstractAs more and more promising applications of magnetic nanoparticles in complicated environments are explored, their flow properties in porous media are of increasing interest. We here propose a hybrid approach based on the multiparticle collision dynamics method extended to porous media via friction forces and coupled with Brownian dynamics simulations of the rotational motion of magnetic nanoparticles’ magnetic moment. We simulate flow in planar channels homogeneously filled with a porous medium and verify our implementation by reproducing the analytical velocity profile of the Darcy–Brinkman model in the non-magnetic case. In the presence of an externally applied magnetic field, the non-equilibrium magnetization and friction forces lead to field-dependent velocity profiles that result in effective, field-dependent permeabilities. We provide a theoretical expression for this magneto-permeability effect in analogy with the magneto-viscous effect. Finally, we study the flow through planar channels, where only the walls are covered with a porous medium. We find a smooth crossover from the Poiseuille profile in the center of the channel to Brinkman–Darcy flow in the porous layers. We propose a simple estimate of the thickness of the porous layer based on the flow rate and maximum flow velocity.

Deciphering non-Gaussianity of diffusion based on the evolution of diffusivity

Non-Gaussian diffusion of nanoparticles in complex media disrupts Einstein's picture of Brownian motion, and non-Gaussianity is thought to be closely related to diffusing diffusivity generated by spatiotemporal heterogeneities. However, the correlation between non-Gaussianity and the dynamics of heterogeneous environments in anomalous diffusion remains uncertain. Inspired by a recent study by Alexandre [], we demonstrate that non-Gaussianity can be deciphered through the spatiotemporal evolution of heterogeneity-dependent diffusivity distribution. Using diffusion experiments in a linear temperature field and Brownian dynamics simulations, we found that short-time non-Gaussianity can be predicted based on the boundary ratio of the diffusivity distribution; the long-time non-Gaussianity either approaches an asymptotic value of −2 or scales with 1/t, depending on the dominance of particle migration. The temporal variation of non-Gaussianity is determined by an effective Péclet number, which represents a competition between the varying rate of diffusivity and the diffusivity of diffusivity and reveals whether the tail distribution expands or contracts. The tail is more Gaussian than exponential over long times, with exceptions significantly dependent on the diffusivity distribution. Our findings provide a versatile framework for understanding non-Gaussian diffusion in probability space, and shed light on establishing a diffusion spectrum in cells and characterizing nanomedicine transport in biological microenvironment using non-Gaussian statistics. Published by the American Physical Society 2024

On the Brownian motion of a colloid trapped in optical tweezers: Experiments and simulations

The trapping potential induced by the interaction of a highly focused laser light with a spherical dielectric particle can be accurately approximated by a parabolic potential. In this work, we revisit experimental and numerical methodologies used to characterize the Brownian motion of a colloidal particle under the influence of a simple harmonic potential produced by optical tweezers. A classic Brownian dynamics simulation is used to model the experimental results, focusing on statistical properties that can be measured by direct visualization of the system using videomicroscopy. This work represents a useful insight into the underlying physics behind the optical tweezers technique, also giving guidelines regarding programming protocols and experimental analysis methodologies, that may be of help for students working with such techniques, as well as for professors teaching undergraduate advanced optics courses.

Effects of chain resolution on the configurational and rheological predictions of dilute polymer solutions in flow fields with hydrodynamic interactions

In a recent study, the resolution of a polymer chain model was shown to significantly affect rheological predictions from Brownian dynamics (BD) simulations [Kumar and Dalal, “Effects of chain resolution on the configurational and rheological predictions from Brownian dynamics simulations of an isolated polymer chain in flow,” J. Non-Newtonian Fluid Mech. 315, 105017 (2023)], even in the absence of hydrodynamic interactions (HI) and excluded volume. In this study, we investigate the effects of chain resolution in the presence of HI. Toward this, we perform BD simulations of a long polymer chain, with the discretization level varying from a single Kuhn step (bead–rod model) to several tens of Kuhn-steps (bead–spring model). The chain models were subjected to flow fields of uniaxial extension (purely stretching) and steady shear (equal rates of stretching and rotation). Broadly, our results indicate an amplification of the differences observed between the differently resolved bead–rod and bead–spring models, in the presence of HI. Interestingly, all rheological predictions qualitatively fall in two groups for extensional flow, with the predictions from the bead–spring model with HI being close to those of the bead–rod model without HI. This indicates significantly reduced sensitivity of coarser bead–spring models to HI, relative to the one resolved to a single Kuhn step. However, in shear flow, the bead–spring rheological predictions fall between those of the bead–rod model with and without HI, forming a third group. This is linked to the presence of stretched and coiled states in the ensemble for shear flow. HI effects are large for the coiled states and weak for the stretched states, thereby yielding predictions that are intermediate between those for no HI and dominant HI. Thus, quite surprisingly, the quality of predictions of the bead–spring models is strongly affected by the physics of the flow field, irrespective of the parameterization.

Chiral active particles are sensitive reporters to environmental geometry

Chiral active particles (CAPs) are self-propelling particles that break time-reversal symmetry by orbiting or spinning, leading to intriguing behaviors. Here, we examined the dynamics of CAPs moving in 2D lattices of disk obstacles through active Brownian dynamics simulations and granular experiments with grass seeds. We find that the effective diffusivity of the CAPs is sensitive to the structure of the obstacle lattice, a feature absent in achiral active particles. We further studied the transport of CAPs in obstacle arrays under an external field and found a reentrant directional locking effect, which can be used to sort CAPs with different activities. Finally, we demonstrated that parallelogram lattices of obstacles without mirror symmetry can separate clockwise and counter-clockwise CAPs. The mechanisms of the above three novel phenomena are qualitatively explained. As such, our work provides a basis for designing chirality-based tools for single-cell diagnosis and separation, and active particle-based environmental sensors.

A Novel Fractional Brownian Dynamics Method for Simulating the Dynamics of Confined Bottle-Brush Polymers in Viscoelastic Solution.

In crowded fluids, polymer segments can exhibit anomalous subdiffusion due to the viscoelasticity of the surrounding environment. Previous single-particle tracking experiments revealed that such anomalous diffusion in complex fluids (e.g., in bacterial cytoplasm) can be described by fractional Brownian motion (fBm). To investigate how the viscoelastic media affects the diffusive behaviors of polymer segments without resolving single crowders, we developed a novel fractional Brownian dynamics method to simulate the dynamics of polymers under confinement. In this work, instead of using Gaussian random numbers ("white Gaussian noise") to model the Brownian force as in the standard Brownian dynamics simulations, we introduce fractional Gaussian noise (fGn) in our homemade fractional Brownian dynamics simulation code to investigate the anomalous diffusion of polymer segments by using a simple "bottle-brush"-type polymer model. The experimental results of the velocity autocorrelation function and the exponent that characterizes the subdiffusion of the confined polymer segments can be reproduced by this simple polymer model in combination with fractional Gaussian noise (fGn), which mimics the viscoelastic media.