Session G29: Active Materials

Drops of Active Matter on Soft Surfaces

Active physics has grown to describe flocking behavior of self-driven systems such as bacterial colonies, cell growth in tissues, self-propelling colloids, protein transport in cells, and many more at the macroscopic level. This has sparked interest in the field of interfacial science and many models have been developed to capture the physics of Active Drops on Solid Surfaces, such as capillarity, evaporation, traction and motion of these drops. However, these models are limitingly tied to solid surface assumption, which is a very rare environment for active systems, and hence cannot capture the dynamics that occur due to elastocapillary-driven interactions on soft substrates. We develop a framework based on gradients in energy functionals of combined solid-liquid-vapor phases that can accurately capture the elasto-capillarity in the presence of active matter. We observe that the contributions from active free energies and active stress tensor aid in altering the overall surface tension and hence, contact angle condition at the three-phase interface. We believe this is the first step in unraveling many new dynamics pertaining to the active systems that more closely resemble their natural habitat.   

Shape evolved structured liquids driven by active particles

Shape evolution enabled dynamic biological behaviors of cells and organisms, such as migration, reconfiguration and division, are driven by internal active matters. Production of synthetic active system that can mimic such dynamic behaviors of natural system remains challenge. Water-water interfaces or lipid bilayers with ultralow interfacial tension can be locally deformed by microtubules or self-propelled particles, but the deformed shapes will relax upon the energy dissipation of active matter and will not retain. Activity of these active matters relies on the concentration gradient of fuel. Thus, the manipulation of shapes lacks instant switch control. Emulsion droplets and lipid vesicles internally powered by active components can only realize shape changes and spontaneous motions at the micrometer scale level, which limit their applications, such as design of robotic systems at the millimeter scale or macroscale level. Here we develop a shape-evolved structured liquid driven by encapsulated active ferromagnetic particles to address all these limitations. Our fully synthetic system exhibits flexible tunability. The interfacial membrane assembled from oppositely charged polystyrene sulfonate (70k) and mono-amine-functionalized polyhedral oligomeric silsesquioxane (POSS-NH2) is reconfigurable. The interfacial tension of structured membrane is easily tuned by PH and concentration of surfactants within 4 orders of magnitude, which in turn adjusts the hardness of the wall (stop and go). The activity of ferromagnetic particles can also be instantly controlled by the strength and frequency of AC magnetic field. The tunable collective behaviors of active components and reconfigurable membrane give rise to the on-demand shape evolution, which enable the directional migration, division and reconfiguration of structured liquids at multiscale level. This strategy would provide a route to a new class of biomimetic, reconfigurable, and responsive materials, delivering mechanical responses unlike those of conventional materials and highlighting the design of autonomous synthetic machines powered by active matter.   

Deformations and Buckling in LCE Films

Liquid crystalline elastomer (LCE) devices represent a versatile material for actuators and soft robotics, capable of exhibiting large shape changes in response to heat or light stimuli. Practical applications require a thorough understanding of how these stimuli control and regulate LCE deformations. We use a combination of finite element modelling and semi-anaylitical methods to predict the out-of-plane buckling behavior of LCE strips and sheets. Building off of prior work on one-dimensional ssytems, we show how buckling can be controlled by light position and intensity, and used to reveal information encoded in the LCE device in the form of patterns in the nematic director.   

Deep learning-based optical flow outperforms PIV in obtaining velocity fields from experimental videos

Obtaining accurate flow velocities from experimental video data plays an important role in developing and testing models of physical systems. While Particle Image Velocimetry (PIV) is a well-known tool for estimating velocities, it has significant limitations and may produce highly inaccurate results for many active matter systems. In this work, we develop an optical flow algorithm for active materials, which produces accurate velocity fields from experimental videos that are well beyond the limitations of PIV algorithms. Deep learning-based optical flow is a machine learning technique that uses deep convolutional neural networks to extract features in a pair of adjacent video frames and uses those features to estimate the inter-frame motions of individual pixels. We test the performance of the optical flow algorithm on microtubule-based active nematics. In our experiments, the microtubules were sparsely and densely labelled using two different fluorescent dyes. The sparse labels on the microtubules serve as “seeding particles” that can be used by PIV to accurately compute the flow fields, while the dense labels, which visualize 100% of the microtubule bundles, pose a great challenge to PIV. Velocity ground truths were obtained by performing semi-automated particle tracking on a subset of microtubule segments that are illuminated by the sparse fluorescent labels. The evaluation shows that PIV and optical flow performed equally well using the sparse labels. When using the dense labels, optical flow produces accurate velocity fields, but PIV produces highly inaccurate, unphysical results. Since sparse labeling of seeding particles is infeasible for many systems, our results imply that the optical flow technique is much more applicable. Moreover, the optical flow implementation is simpler than PIV. Thus, we expect that optical flow can become a widely used tool for velocity estimation in a broad class of active and other soft matter systems.   

Thermal snap-through buckling of active bilayer arches under non-uniform longitudinal temperature distribution

Arches have been utilized in structural applications for thousands of years. However, investigation of arches that purposefully move under an environmental stimulus is a more recent pursuit. Surprisingly, there are limited examples in the literature exploring analytical and semi-analytical results for the snap-through of bilayer arches under non-uniform temperature distributions along the length of the arch. An area of particular interest is the combination of hard and soft layers, with the soft layers being in the form of responsive materials like aligned liquid crystal elastomers. In this work we present approximate semi-analytical results for the equilibrium of deformed bilayer arches under non-uniform, longitudinal temperature distributions. We will comment on the conditions in which the approximate results hold and potential future work, including experimental validation. This work has implications for sensors, actuators, and locomotion, especially under non-uniform environmental conditions.   

Microscopic theory for hyperuniformity in two-dimensional chiral active fluid

 Chiral active matter is one of the important classes of active matter in which each component performs left-right symmetry-broken motions. Two-dimensional chiral active fluids have a striking feature called hyperuniformity, an anomalous suppression of large-scale density fluctuations. This is in stark contrast to other types of active matter, including polar active fluids or active nematics, where large-scale density fluctuations are enhanced. Hyperuniformity in two-dimensional chiral active fluids has been observed both numerically and experimentally. However, a theoretical understanding from a microscopic point of view is still lacking. 

 Here, we develop a microscopic theory that can explain its mechanism qualitatively. Effective fluctuating hydrodynamic equations are derived from a simple particle model of chiral active matter. The linear analysis of the obtained equations explains hyperuniformity.   

The Brittle-to-Ductile Yielding Transition in Active Amorphous Solids

We want to develop a predictive theory for flow in very dense active matter, which models systems including bacterial swarms and biomimetic emulsions. Researchers have identified a direct link between the dynamics of sheared amorphous solids and dense active matter in the limit of small and intermediate strains. In sheared solids, rapidly quenched systems with high disorder exhibit a continuous, “ductile” yielding transition, while slowly quenched systems with low disorder fail in a brittle manner via system-spanning shear bands. Several researchers have provided evidence that this yielding transition is in the Random Field Ising Model universality class, while others dispute this finding. To shed light on this dispute, we analyze the yielding transition in dense active systems with varying disorder. Our preliminary data suggest that there is no brittle failure in dense active matter, even in deeply quenched systems. We alter the correlation length of the field of active forces, and find that failure becomes less ductile as the correlation length increases. This suggests that the orientational correlations between activated defects may play an important role in brittle failure, and that future theories should incorporate orientational effects.   

Tunable shape oscillations in adaptive droplets

Soft materials can undergo irreversible shape changes when driven out of equilibrium [1,2]. When shape changes are triggered by processes at the surface, geometry-dependent feedback can arise. Motivated by the mechanochemical feedback observed in multicellular systems [1,3-5], we study incompressible droplets that adjust their interfacial tensions in response to shape-dependent signals. We derive a minimal set of equations governing the mesoscopic droplet states controlled by just two dimensionless feedback parameters. We find that single adaptive droplets display different classes of excitability arising from a Bogdanov-Takens-Cusp bifurcation, and that interacting droplet pairs exhibit symmetry-breaking and tunable shape oscillations ranging from near-sinusoidal to relaxation-type, which stem from a saddle-node pitchfork bifurcation. Our tractable framework provides a paradigm for how soft active materials respond to shape-dependent signals, and suggests novel modes of self-organisation at the collective scale.

[1] Erzberger, Jacobo et al. Nat Phys (2020)

[2] Salbreux, Jülicher Phys Rev E (2017)

[3] Dullweber, Erzberger Curr Opin Syst Biol (2023)

[4] Corson, et al. Science (2017)

[5] Khait, et al. Cell Rep (2016)   

Emergence of run-and-tumble-like motion in active robots connected together with a rigid rod.

Motility is one of the most remarkable features of living organisms and active particles. Its emergence is a complex interplay of various components that are often also active. Even though there are numerous examples of living and artificial active particles, there is a lack of detailed understanding of how individual active components, in conjunction, give rise to such complex motility characteristics. Here, we perform experiments on a "compound robot" composed of two motile robots connected with a rigid rod. The robots are centimeters-long disk-shaped programmable active particles with off-centered pivot points around which the rod can freely rotate. A pivot located at the left/right side of the robot's orientation introduces right/left-handed chirality in the robot dynamics. We show that when individual robots are programmed to follow active Brownian motion, the left-right chiral compound robot can display run-and-tumble-like dynamics. We find that the spontaneously evolving run and tumble events correspond to the states where the velocities of the two robots are aligned and misaligned, respectively. We also discover a systematic dependence of run-time statistics on the rotational noise of the individual robots. Overall, we demonstrate how complex motility behaviour arises in an active particle composed of multiple active components and possible strategies to tune such a motion.   

Hydrogel Dynamics at charged surfaces as a function of salt concentration and surface ionization.

The understanding of gel dynamics at surfaces in various environments is essential for applications like underwater adhesion, medical sutures, contact lenses, etc. The dynamics of hydrogels at bulk has been explored with various light scattering technique, but little is known about the local network equilibrium and dynamics change at strongly interactive surfaces. Here, we use Differential Dynamic Microscopy (DDM) paired with confocal fluorescence imaging to study the network flactuation of negatively charged sodium hyaluronate (NaHA) gel within ~ 1 μm of negatively and positively charged surfaces as a function of ionic strength, which results in a change in the local network deformation and motion. We conclude that the network motion differs when swollen under different salt concentrations.   

Single-particle tracking of DNA origami-enzyme conjugates in the presence of their substrate

Recent experiments have shown that enzymes can exhibit enhanced diffusion in the presence of their chemical substrates. The fundamental physics governing the enhanced diffusion of enzymes, however, is still unknown. Therefore, new model experimental systems are needed. In this talk, I will describe our efforts to develop a suite of programmable active particles using DNA origami to test hypotheses for the enhanced diffusion of enzymes. In particular, we conjugate enzymes to the surface of DNA origami, in user-prescribed locations and patterns, and look for changes to their diffusion using total internal reflection fluorescence microscopy and single-particle tracking. With the conjugate trajectories in hand, we then calculate the mean squared displacement and the jump-length statistics to infer the diffusion coefficient and whether or not the motion is entirely Brownian. By exploiting the programmability and site-addressability of DNA origami, we anticipate that these experiments will help to shed light on the nature of enzyme motility.   

Extracting nontrivial work from ordinary chemical reactions

Recent studies propose that chemical reactions exemplify active matter. Testing this hypothesis experimentally, we embed achiral gear-shaped objects within the environment of what has become a prototypical chemical reaction:  the CuAAc click reaction. Our microscopy observations show that achiral gears rotate preferentially – clockwise and anticlockwise, according to the circumstances – as anticipated for active matter. Step-size distributions are especially informative when considering how they track the chemical reaction kinetics, from far-from-equilibrium to the equilibrium state.