Statistical reprogramming of macroscopic self-assembly with dynamic boundaries

Topological mechanical states in geometry-driven hyperuniform materials

Disordered hyperuniform materials are increasingly drawing attention due to their unique physical properties, associated with global isotropy and locally broken orientational symmetry, that set them apart from traditional crystalline materials. Using a dynamic space-partitioning process, we generate disordered hyperuniform cellular structures where distinct patterns of pentagonal and heptagonal topological defects emerge within hexagonal domains. The microscopic defect dynamics are guided by local topological transitions, commonly observed in viscoelastic systems. This leads to a reduction in the system's structural entropy as hyperuniformity is attained, marked by the rise and fall of certain locally favored motifs. Further, we introduce an elastic hyperuniform material that exhibits evolving topological mechanical states in the continuum. Through vibration experiments and numerical analysis, we show energy localization around these defects, which is tied to the topological band gaps inherent to our geometry-driven material. We suggest that this robust dynamic mechanism influences a broad spectrum of disordered systems, from synthetic materials to biological structures guided by stigmergic interactions.

Dynamics of self-propelled particles in vibrated dense granular media

We study a system consisting of a few self-propelled particles (SPPs) placed among a crowd of densely packed granular particles that are vertically vibrated in a two-dimensional circular confinement. Our experiments reveal two important findings. First, an SPP exhibits a fractal renewal process within the dense granular medium, which induces a superdiffusive behavior whose diffusion exponent increases with its aspect ratio. Second, the SPPs eventually reach the boundary and form a moving cluster, which transitions from the moving state to the static state as the number of SPPs is increased. These results suggest a simple and effective method of modulating the fluidity and directionality of granular systems via controlling the shape and the number of SPPs.

Controlled transitions between metastable states of 2D magnetocapillary crystals

Magnetocapillary interactions between particles allow to self-assemble floating crystals along liquid interfaces. For a fixed number of particles, different states possessing different symmetrical features, known as metastable states, coexist. In this paper, we demonstrate how to trigger the transition from one state to another, either by rearranging the crystal, or by controlling its growth. First, we show that externally controlled magnetic fields can squeeze the entire crystal to induce structural modifications, that upon relaxation can lead to a modified state. Second, we propose localized laser-induced thermocapillary flows that can be used to guide new particles towards an existing crystal in a desired direction, thus favoring a particular resulting state. The control of the formation of metastable states is a key ingredient to functionalize such assemblies, paving the way to self-assembled microrobots.

Microrobot collectives with reconfigurable morphologies, behaviors, and functions

Mobile microrobots, which can navigate, sense, and interact with their environment, could potentially revolutionize biomedicine and environmental remediation. Many self-organizing microrobotic collectives have been developed to overcome inherent limits in actuation, sensing, and manipulation of individual microrobots; however, reconfigurable collectives with robust transitions between behaviors are rare. Such systems that perform multiple functions are advantageous to operate in complex environments. Here, we present a versatile microrobotic collective system capable of on-demand reconfiguration to adapt to and utilize their environments to perform various functions at the air-water interface. Our system exhibits diverse modes ranging from isotropic to anisotrpic behaviors and transitions between a globally driven and a novel self-propelling behavior. We show the transition between different modes in experiments and simulations, and demonstrate various functions, using the reconfigurability of our system to navigate, explore, and interact with the environment. Such versatile microrobot collectives with globally driven and self-propelled behaviors have great potential in future medical and environmental applications.

Classification of emerging patterns in self-assembled two-dimensional magnetic lattices

Self-assembled granular materials can be utilized in many applications such as shock absorption and energy harvesting. Such materials are inherently discrete with an easy path to tunability through external applied forces such as stress or by adding more elements to the system. However, the self-assembly process is statistical in nature with no guarantee for repeatability, stability, or order of emergent final assemblies. Here we study both numerically and experimentally the two-dimensional self-assembly of free-floating disks with repulsive magnetic potentials confined to a boundary with embedded permanent magnets. Six different types of disks and seven boundary shapes are considered. An agent-based model is developed to predict the self-assembled patterns for any given disk type, boundary, and number of disks. The validity of the model is experimentally verified. While the model converges to a physical solution, these solutions are not always unique and depend on the initial position of the disks. The emerging patterns are classified into monostable patterns (i.e., stable patterns that emerge regardless of the initial conditions) and multistable patterns. We also characterize the emergent order and crystallinity of the emerging patterns. The developed model along with the self-assembly nature of the system can be key in creating re-programmable materials with exceptional nonlinear properties.

Physical intelligence as a new paradigm

Intelligence of physical agents, such as human-made (e.g., robots, autonomous cars) and biological (e.g., animals, plants) ones, is not only enabled by their computational intelligence (CI) in their brain, but also by their physical intelligence (PI) encoded in their body. Therefore, it is essential to advance the PI of human-made agents as much as possible, in addition to their CI, to operate them in unstructured and complex real-world environments like the biological agents. This article gives a perspective on what PI paradigm is, when PI can be more significant and dominant in physical and biological agents at different length scales and how bioinspired and abstract PI methods can be created in agent bodies. PI paradigm aims to synergize and merge many research fields, such as mechanics, materials science, robotics, mechanical design, fluidics, active matter, biology, self-assembly and collective systems, to enable advanced PI capabilities in human-made agent bodies, comparable to the ones observed in biological organisms. Such capabilities would progress the future robots and other machines beyond what can be realized using the current frameworks.

Self-assembly of millimeter-scale magnetic particles in suspension

Self-assembly is ubiquitous at all scales in nature. Most studies have focused on the self-assembly of micron-scale and nano-scale components. In this study, we explore the self-assembly of millimeter-scale magnetic particles in a bubble-column reactor to form 9 different structures. Two component systems (N-N and S-S particles) assemble faster than one-component systems (all particles have N-S poles) because they have more numerous bonding pathways. In addition, two-components add control to process initiation and evolution, and enable the formation of complex structures such as squares, tetrahedra and cubes. Self-assembly is collision-limited, thus, the formation time increases with the total number of bonds required to form the structure and the injected power. The dimensionless Mason number captures the interplay between hydrodynamic forces and magnetic interactions: self-assembly is most efficient at intermediate Mason numbers (the system is quasi-static at low Mason numbers with limited chances for particle interaction; on the other hand, hydrodynamic forces prevail over dipole-dipole interactions and hinder bonding at high Mason numbers). Two strategies to improve yield involve (1) the inclusion of pre-assembled nucleation templates to prevent the formation of incorrect initial structures that lead to kinetic traps, and (2) the presence of boundaries to geometrically filter unwanted configurations and to overcome kinetic traps through particle-wall collisions. Yield maximization involves system operation at an optimal Mason number, the inclusion of nucleation templates and the use of engineered boundaries (size and shape).

Self-assembly of self-propelled magnetic grains

In this work, we study bidisperse mixtures of self-propelled magnetic particles of different shapes via discrete element method simulations. We show how these particles self-assemble into clusters and how these clusters depend on the ratio of the mixture, the magnetic interaction, and the shape of the grains. It is found that the mix ratio of the system controls the cluster size. Besides, the intensity of the magnetic dipoles and the shape of the grains in the mixture rule the average number of neighbors in contact and the shape of the clusters. By varying the intensity of the interactions, globular, linear and branched clusters were obtained.