Dynamic Assembly of Active Colloids: Theory and Simulation

Open Questions of Chemically Powered Nano- and Micromotors

Chemically powered nano- and micromotors are microscopic devices that convert chemical energy into motion. Interest in these motors has grown over the past 20 years because they exhibit interesting collective behaviors and have found potential uses in biomedical and environmental applications. Understanding how these motors operate both individually and collectively and how environments affect their operation is of both fundamental and applied significance. However, there are still significant gaps in our knowledge. This Perspective highlights several open questions regarding the propulsion mechanisms of, interactions among, and impact of confinements on nano- and micromotors driven by self-generated chemical gradients. These questions are based on my own experience as an experimentalist. For each open question, I describe the problem and its significance, analyze the status-quo, identify the bottleneck problem, and propose potential solutions. An underlying theme for these questions is the interplay among reaction kinetics, physicochemical distributions, and fluid flows. Unraveling this interplay requires careful measurements as well as a close collaboration between experimentalists and theoreticians/numerical experts. The interdisciplinary nature of these challenges suggests that their solutions could bring new revelations and opportunities across disciplines such as colloidal sciences, material sciences, soft matter physics, robotics, and beyond.

How does a hyperuniform fluid freeze?

All phase transitions can be categorized into two different types: continuous and discontinuous phase transitions. Discontinuous phase transitions are normally accompanied with significant structural changes, and nearly all of them have the kinetic pathway of nucleation and growth, if the system does not suffer from glassy dynamics. Here, in a system of barrier-controlled reactive particles, we find that the discontinuous freezing transition of a nonequilibrium hyperuniform fluid into an absorbing state does not have the kinetic pathway of nucleation and growth, and the transition is triggered by long-wavelength fluctuations. The transition rate decreases with increasing the system size, which suggests that the metastable hyperuniform fluid could be kinetically stable in an infinitely large system. This challenges the common understanding of metastability in discontinuous phase transitions. Moreover, we find that the "metastable yet kinetically stable" hyperuniform fluid features a scaling in the structure factor [Formula: see text] in 2D, which is the third dynamic hyperuniform state in addition to the critical hyperuniform state with [Formula: see text] and the nonequilibrium hyperuniform fluid with [Formula: see text].

The interplay between chemo-phoretic interactions and crowding in active colloids

Many motile microorganisms communicate with each other and their environments via chemical signaling which leads to long-range interactions mediated by self-generated chemical gradients. However, consequences of the interplay between crowding and chemotactic interactions on their collective behavior remain poorly understood. In this work, we use Brownian dynamics simulations to investigate the effect of packing fraction on the formation of non-equilibrium structures in a monolayer of diffusiophoretic self-propelled colloids as a model for chemically active particles. Focusing on the case when a chemical field induces attractive positional and repulsive orientational interactions, we explore dynamical steady-states of active colloids of varying packing fractions and degrees of motility. In addition to collapsed, active gas, and dynamical clustering steady-states reported earlier for low packing fractions, a new phase-separated state emerges. The phase separation results from a competition between long-range diffusiophoretic interactions and motility and is observed at moderate activities and a wide range of packing fractions. Our analysis suggests that the fraction of particles in the largest cluster is a suitable order parameter for capturing the transition from an active gas and dynamical clustering states to a phase-separated state.

Mode-coupling theory for the dynamics of dense underdamped active Brownian particle system

We present a theory to study the inertial effect on glassy dynamics of the underdamped active Brownian particle (UABP) system. Using the assumption of the nonequilibrium steady-state, we obtain an effective Fokker-Planck equation for the probability distribution function (PDF) as a function of positions and momentums. With this equation, we achieve the evolution equation of the intermediate scattering function through the Zwanzig-Mori projection operator method and the mode-coupling theory (MCT). Theoretical analysis shows that the inertia of the particle affects the memory function and corresponding glass transition by influencing the structure factor and a velocity correlation function. The theory provides theoretical support and guidance for subsequent simulation work.

Light-Activated Colloidal Micromotors with Synthetically Tunable Shapes and Shape-Directed Propulsion

Controlling the propulsion modes of colloidal micromotors, from translational to spinning and helical motion, expands the versatility of their potential applications in microrobotics and micromachinery. Engineering colloidal shapes with designed asymmetry can regulate their propulsion behaviors, yet current methods rely on complicated and costly fabrication processes such as lithography. Herein, we present a solution-based synthesis of light-activated colloidal motors adopting straight and various tunable bent geometries, which feature controlled asymmetry and allow shape-directed propulsions. The keys for our strategy are the synthesis of bent silica rods with a tailored bending position and degree, together with the site-specific installation of a photoactive engine. Upon light illumination, the resulting particles propel autonomously, whereby their shape information is translated to various propulsion modes including linear locomotion, steering, and spinning. This low-cost, scalable method for fabricating micromotors with a high degree of control of shapes could promote study in microscale actuation, in active assembly, and eventually for fabrication of colloidal functional materials.

Obstacle-induced giant jammed aggregation of active semiflexible filaments

Filaments driven by bound motor proteins and chains of self-propelled colloidal particles are a typical example of active polymers (APs). Due to deformability, APs exhibit very rich dynamic behaviors and collective assembling structures. Here, we are concerned with a basic question: how APs behave near a single obstacle? We find that, in the presence of a big single obstacle, the assembly of APs becomes a two-state system, i.e. APs either gather nearly completely together into a giant jammed aggregate (GJA) on the surface of the obstacle or distribute freely in space. No partial aggregation is observed. Such a complete aggregation/collection is unexpected since it happens on a smooth convex surface instead of, e.g., a concave wedge. We find that the formation of a GJA experiences a process of nucleation and the curves of the transition between the GJA and the non-aggregate state form hysteresis-like loops. Statistical analysis of massive data on the growing time, chirality and angular velocity of both the GJAs and the corresponding nuclei shows the strong random nature of the phenomenon. Our results provide new insights into the behavior of APs in contact with porous media and also a reference for the design and application of polymeric active materials.

Variational methods and deep Ritz method for active elastic solids

Variational methods have been widely used in soft matter physics for both static and dynamic problems. These methods are mostly based on two variational principles: the variational principle of minimum free energy (MFEVP) and Onsager's variational principle (OVP). Our interests lie in the applications of these variational methods to active matter physics. In our former work [H. Wang, T. Qian and X. Xu, Soft Matter, 2021, 17, 3634-3653], we have explored the applications of OVP-based variational methods for the modeling of active matter dynamics. In the present work, we explore variational (or energy) methods that are based on MFEVP for static problems in active elastic solids. We show that MFEVP can be used not only to derive equilibrium equations, but also to develop approximate solution methods, such as the Ritz method, for active solid statics. Moreover, the power of the Ritz-type method can be further enhanced using deep learning methods if we use deep neural networks to construct the trial functions of the variational problems. We then apply these variational methods and the deep Ritz method to study the spontaneous bending and contraction of a thin active circular plate that is induced by internal asymmetric active contraction. The circular plate is found to be bent towards its contracting side. The study of such a simple toy system gives implications for understanding the morphogenesis of solid-like confluent cell monolayers. In addition, we introduce a so-called activogravity length to characterize the importance of gravitational forces relative to internal active contraction in driving the bending of the active plate. When the lateral plate dimension is larger than the activogravity length (about 100 micron), gravitational forces become important. Such gravitaxis behaviors at multicellular scales may play significant roles in the morphogenesis and in the up-down symmetry broken during tissue development.

Confined Assembly of Colloidal Nanorod Superstructures by Locally Controlling Free-Volume Entropy in Nonequilibrium Fluids

Long-range-ordered structures of nanoparticles with controllable orientation have advantages in applications toward sensors, photoelectric conversion, and field-effect transistors. The assembly process of nanorods in colloidal systems undergoes a nonequilibrium process from dispersion to aggregation. A variety of assembly methods such as solvent volatilization, electromagnetic field induction, and photoinduction are restricted to suppress local perturbations during the nonequilibrium concentration of nanoparticles, which are adverse to controlling the orientation and order of assembled structures. Here, a confined assembly method is reported by locally controlling free-volume entropy in nonequilibrium fluids to fabricate microstructure arrays based on colloidal nanorods with controllable orientation and long-range order. The unique fluid dynamics of the liquid bridge is utilized to form a local region, where the free volume entropy reduction triggers assembly near the three-phase contact line (TPCL), allowing nanorods to assemble in 2D closest packing parallel to the TPCL for the maximum Gibbs free energy reduction. By manipulating the orientation of liquid flow, microstructures are assembled with programmable geometry, which sustains polarized photoluminescence and polarization-dependent photodetection. This confined assembly method opens up perspectives on assemblies of nanomaterials with controllable orientation and long-range order as a platform for multifunctional integrated devices.

Structure and dynamics of an active polymer adsorbed on the surface of a cylinder

The structure and dynamics of an active polymer on a smooth cylindrical surface are studied by Brownian dynamics simulations. The effect of an active force on the polymer adsorption behavior and the combined effect of chain mobility, length N, rigidity κ, and cylinder radius, R, on the phase diagrams are systemically investigated. We find that complete adsorption is replaced by the irregular alternative adsorption/desorption process at a large driving force. Three typical (spiral, helix-like, and rod-like) conformations of the active polymer are observed, dependent on N, κ, and R. Dynamically, the polymer shows rotational motion in the spiral state, snake-like motion in the intermediate state, and straight translational motion without turning back in the rod-like state. In the spiral state, we find that the rotation velocity ω and the chain length follow a power-law relation ω ∼ N^-0.42, consistent with the torque-balance theory of general Archimedean spirals. And the polymer shows super-diffusive behavior along the cylinder for a long time in the helix-like and rod-like states. Our results highlight that the mobility, rigidity, and curvature of surface can be used to regulate the polymer behavior.

Dynamical clustering interrupts motility-induced phase separation in chiral active Brownian particles

One of the most intriguing phenomena in active matter has been the gas-liquid-like motility-induced phase separation (MIPS) observed in repulsive active particles. However, experimentally, no particle can be a perfect sphere, and the asymmetric shape, mass distribution, or catalysis coating can induce an active torque on the particle, which makes it a chiral active particle. Here, using computer simulations and dynamic mean-field theory, we demonstrate that the large enough torque of circle active Brownian particles in two dimensions generates a dynamical clustering state interrupting the conventional MIPS. Multiple clusters arise from the combination of the conventional MIPS cohesion, and the circulating current caused disintegration. The nonvanishing current in non-equilibrium steady states microscopically originates from the motility "relieved" by automatic rotation, which breaks the detailed balance at the continuum level. This suggests that no equilibrium-like phase separation theory can be constructed for chiral active colloids even with tiny active torque, in which no visible collective motion exists. This mechanism also sheds light on the understanding of dynamic clusters observed in a variety of active matter systems.

Learning self-driven collective dynamics with graph networks

Despite decades of theoretical research, the nature of the self-driven collective motion remains indigestible and controversial, while the phase transition process of its dynamic is a major research issue. Recent methods propose to infer the phase transition process from various artificially extracted features using machine learning. In this thesis, we propose a new order parameter by using machine learning to quantify the synchronization degree of the self-driven collective system from the perspective of the number of clusters. Furthermore, we construct a powerful model based on the graph network to determine the long-term evolution of the self-driven collective system from the initial position of the particles, without any manual features. Results show that this method has strong predictive power, and is suitable for various noises. Our method can provide reference for the research of other physical systems with local interactions.

Effect of polydispersity on the dynamics of active Brownian particles

We numerically study the dynamics and the phases of self-propelled disk-shaped particles of different sizes with soft repulsive potential in two dimensions. Size diversity is introduced by the polydispersity index (PDI) ε, which is the width of the uniform distribution of the particle's radius. The self-propulsion speed of the particles controls the activity v. We observe enhanced dynamics for large size diversity among the particles. We calculate the effective diffusion coefficient D_{eff} in the steady state. The system exhibits four distinct phases, jammed phase with small D_{eff} for small activity and liquid phase with enhanced D_{eff} for large activity. The number fluctuation is larger and smaller than the equilibrium limit in the liquid and jammed phases, respectively. Further, the jammed phase is of two types: solid jammed and liquid jammed for small and large PDI. Whereas the liquid phase is called motility induced phase separation (MIPS) liquid for small PDI and for large PDI, we find enhanced diffusivity and call it the pure liquid phase. The system is studied for three packing densities ϕ, and the response of the system for polydispersity is the same for all ϕ's. Our study can help understand the behavior of cells of various sizes in a tissue, artificial self-driven granular particles, or living organisms of different sizes in a dense environment.

Active noise-driven particles under space-dependent friction in one dimension

We study a Langevin equation describing the stochastic motion of a particle in one dimension with coordinate x, which is simultaneously exposed to a space-dependent friction coefficient γ(x), a confining potential U(x) and nonequilibrium (i.e., active) noise. Specifically, we consider frictions γ(x)=γ_{0}+γ_{1}|x|^{p} and potentials U(x)∝|x|^{n} with exponents p=1,2 and n=0,1,2. We provide analytical and numerical results for the particle dynamics for short times and the stationary probability density functions (PDFs) for long times. The short-time behavior displays diffusive and ballistic regimes while the stationary PDFs display unique characteristic features depending on the exponent values (p,n). The PDFs interpolate between Laplacian, Gaussian, and bimodal distributions, whereby a change between these different behaviors can be achieved by a tuning of the friction strengths ratio γ_{0}/γ_{1}. Our model is relevant for molecular motors moving on a one-dimensional track and can also be realized for confined self-propelled colloidal particles.

Onsager's variational principle in active soft matter

Onsagers variational principle (OVP) was originally proposed by Lars Onsager in 1931 [L. Onsager, Phys. Rev., 1931, 37, 405]. This fundamental principle provides a very powerful tool for formulating thermodynamically consistent models. It can also be employed to find approximate solutions, especially in the study of soft matter dynamics. In this work, OVP is extended and applied to the dynamic modeling of active soft matter such as suspensions of bacteria and aggregates of animal cells. We first extend the general formulation of OVP to active matter dynamics where active forces are included as external non-conservative forces. We then use OVP to analyze the directional motion of individual active units: a molecular motor walking on a stiff biofilament and a toy two-sphere microswimmer. Next we use OVP to formulate a diffuse-interface model for an active polar droplet on a solid substrate. In addition to the generalized hydrodynamic equations for active polar fluids in the bulk region, we have also derived thermodynamically consistent boundary conditions. Finally, we consider the dynamics of a thin active polar droplet under the lubrication approximation. We use OVP to derive a generalized thin film equation and then employ OVP as an approximation tool to find the spreading laws for the thin active polar droplet. By incorporating the activity of biological systems into OVP, we develop a general approach to construct thermodynamically consistent models for better understanding the emergent behaviors of individual animal cells and cell aggregates or tissues.