Session F57: DSOFT Early Career and Student Awards Session

Prize Talk: Early Career Award for Soft Matter Research

Absorbing shocks and vibrations and mitigating fracture is a formidable challenge in an increasing number of applications. In this talk, I will present how mechanical materials can be used to mitigate vibrations, shocks and fracture in extremely lightweight metamaterials and could be used in the context of automotive and aerospace applications. To this end, I will first show how one can navigate the geometrical design space using a combination of geometrical, combinatorial, and AI techniques to design metamaterials with on-demand static responses. Second I will illustrate how one can leverage the dissipative and nonlinear nature of the constitutive material itself to create metamaterials at full scale with target dynamical responses.    

How freezing affects morphology

The solidification of liquids containing insoluble particles, whether rigid or soft, is a fundamental phenomenon with widespread relevance in both natural and industrial contexts. Investigating how the interplay of particle rejection and engulfment shapes the microstructure of the solidifying material is essential for numerous applications. In this presentation, we delve into the intricacies of this process using dilute oil-in-water emulsions and suspensions as experimental model systems, while gradually freezing them. We begin by showcasing how initially spherical oil droplets assume pointy, tear-like shapes during their incorporation into ice, with the extent of deformation being linked to the freezing rate. Shifting our focus to multi-component compound drops replacing the uniform oil droplets, we highlight the profound increase in complexity during the freezing process. Intriguingly, these compound drops undergo sudden topological transitions within the ice, leading to unanticipated local microstructural changes. Finally, we explore the interaction between neighboring particles at the ice interface. Surprisingly, we observe that particles with greater thermal conductivity than water exhibit attractive behavior and tend to form clusters once frozen, whereas less conductive particles tend to separate. Our research sheds light on the fascinating physics underlying the solidification of complex liquid systems, offering valuable insights for various practical applications.   

Sheared amorphous solids, pure and simple

Disordered materials, such as jammed packings of soft spheres, will become unstable and rearrange when strained past their elastic limit. A great deal of work has been focused on predicting where and when these rearrangements will occur, yet much remains unexplored about the instabilities themselves. Here, we probe particle rearrangements in a simulation with periodic boundary conditions. We continuously control the direction of the applied shear strain, θ, by combining pure and simple shear with different amplitudes. This gives rise to a two-dimensional parameter space with, for example, θ and shear amplitude, γ, as the two axes. We find that some instabilities persist over a surprisingly large variation of the shear direction with a characteristic shape γ(θ). We use this new technique to map out pairs of instabilities that pass through each other and to measure the persistence of paired rearrangements that are elementary units of hysteretic behavior in the packing.   

Abusing symmetry: Realizing complex 2D and 3D nanostructures with pluripotent DNA origami colloids

The explosion in the diversity and complexity of nanoparticles that can be synthesized in recent years empowers programmable self-assembly of more and more complex nanostructures. However, the inverse design of different structures, even simple geometries like 2D planar crystals, is difficult. Here, we develop a symmetry-based method to generate the interaction matrices that specify the assembly of 2D tilings of equilateral triangles. We also show that the application of our symmetry-based approach is not limited to two-dimensional tilings. By considering the angles between two neighboring triangles as an additional degree of freedom, we show how various 2D tilings can be wrapped onto different three-dimensional objects, including polyhedra, tubules, and toroids. To demonstrate the utility of our design approach, we synthesize DNA origami triangles that can bind edge-to-edge through DNA hybridization. By encoding the interaction specificity and the dihedral angles for each triangle, we assemble 2D tilings with various symmetries, as well as polyhedra and tubules using multiple species of triangles. Going forward, this work opens up pathways towards designing and assembling nanostructures with ever increasing complexity.   

Training Deflected States in 2D Magnetoelastic Sheets

The concept of training is largely associated to living beings preparing themselves for a task in the near future. However, it is also possible to train inanimate matter. Trainable materials can be taught to which degree to alter their properties in response to environmental changes. To be able to "learn", materials must have reconfigurable internal degrees of freedom, and they must be able to retain these newly reconfigured states. Here, we use Molecular Dynamics simulations to study the mechanics and training of magnetoelastic sheets, composed of hydrocarbon-coated superparamagnetic nanoparticles and manipulated with magnetic fields. Experiments show that repeated deflection and heating cause the sheet to retain more and more deflection each cycle, resulting in a deflected trained state. Our MD simulations show how this training can result from microscopic configurational changes, suggesting a rearrangement of the hydrocarbons in the deflected state when heated. Due to the combined insights of simulation and experiments, magnetic nanoparticle sheets are an ideal platform for the exploration of general principles of mechanical training of materials.   

Large-scale synchronized temporal oscillation in epithelial collectives

Collective cell migration presents a promising field for the application of non-equilibrium physics, exposing a variety of phenomena including pattern formation, glassy dynamics, and active turbulence. The underlying physics offers a fresh viewpoint for understanding structural and functional properties of epithelia. Despite numerous studies on the spatial patterns of cell migration, the temporal dynamics have been largely overlooked. Here, we investigate the sustained expansion and contraction of cell packs emerging within epithelia, showing temporally semi-periodic velocity divergence. By extracting the phase from divergence field, we observe surprisingly large-scale oscillatory patterns that resemble bioelectrical signals reported in the heart and brain. Interestingly, the divergence phase dynamics in a developing epithelium shows density-dependent synchronization and de-synchronization behaviors, which align with the epithelial jamming transition. Furthermore using the breast cancer models, we see stronger persistence and fewer topological defects in phase evolution when cancer cells become more malignant. As we forge ahead, such temporal analysis has the potential to assess tissue developmental processes, to distinguish healthy and diseased tissues, and to provide a new perspective for histology.    

Spacial correlation in active matter

The non-equilibrium properties of life have been deeply intriguing, and one promising approach to understanding these properties lies in the study of active matter. In biological systems, collective behavior often manifests in intricate dynamics. Collective cell groups display organized migration patterns, including vortex or parallel flows, which show apparent correlation properties. The mechanisms underlying collective cell movement depend on both intrinsic correlations within the cells and extrinsic correlations shaped by environmental factors, including the size of the confinements.

These correlations not only influence dynamic cell migration but also play a pivotal role in collective cell alignment. Correlation among cells in a monolayer is fundamental for generating topological defects, which in turn drive tissue morphogenesis. The correlation function serves as an indispensable analytical tool, shedding light on the nuances of collective cell behaviors and facilitating a deeper comprehension of complex biological systems. Though correlation as a concept has long been used in statistical physics to describe the statistical relationship between random variables about spatial or temporal distance, its potential within the realm of biology remains underexplored.

In this study, we delve into the intricacies of correlations in collective cell systems. Our objective is to clarify ambiguities related to the definition of the correlation function within biomechanics. By identifying and rectifying prevalent inconsistencies, we aspire to enhance the interpretation clarity of correlations in the active matter realm. Our ultimate aim is to provide the biophysical community with insightful perspectives on the significance of correlations in steering active matters.   

The wake of a sphere in a chiral fluid

Systems composed of spinning particles or driven by a magnetic field break mirror symmetry at the microscopic level. These chiral fluids can be described by adding additional so-called "odd" viscosities, which do not dissipate energy, in the Navier-Stokes equation. Here, we ask: how does odd viscosity affect the wake of a sphere as the Reynolds number increases? In ordinary fluids, the wake undergoes several bifurcations, first from an axisymmetric to a non-axisymmetric steady state, and then to a state where it periodically sheds vortices, similar to the von Karman vortex street familiar from everyday fluid flows. Using a combination of numerical and analytical methods, we describe the transitional flow regime in a chiral fluid. We find that odd viscosity reshapes the vortex structure of the wake, which in turn affects the onset and nature of the periodic vortex shedding state. Our work sheds light on the transition to turbulence in chiral fluids, a regime that could be realized experimentally in collections of spinning inertial particles.   

Enhancing nanocrystal superlattice self-assembly near a metastable liquid binodal

Self-assembly of colloidal nanocrystals (NCs) into superlattices (SLs) is an appealing strategy to design hierarchically organized materials with new functionalities. Mechanistic studies are needed to uncover SL self-assembly design principles, but they are difficult to perform due to the fast time- and short length scales of NC systems. To address this challenge, we developed an apparatus to directly measure in situ and in real time solutions of electrostatically stabilized NCs as they are quenched to form SLs using small angle X-ray scattering. By developing a quantitative model, we fit the time-dependent scattering patterns to obtain the phase diagram of the system and the kinetics as a function of varying quench conditions. The extracted phase diagram is consistent with particles whose interactions are short in range relative to their size. As a result, we can direct the self-assembly through one-step or two-step pathways in which SLs form either directly from the colloidal phase or from a metastable liquid intermediate whose fluctuations we quantified using X-ray photon correlation spectroscopy. By combining this in situ methodology with quantitative analysis tools and simulation, we propose design principles and kinetic strategies to optimize nanoscale self-assembly.   

Super-Universal Behavior of Outliers Diffusing in (Space-Time) Random Environments

Random walks have historically been used to model diffusive systems, but require many simplifying assumptions. Recently there has been increasing interest in a new model which accounts for space-time correlations between the particles through a shared environment (the Random Walks in a Random Environment model). This model for diffusion recovers the same bulk properties as classical diffusion (i.e. statistics of a typical particle). However, the tail of the distribution (i.e. particles that have moved the farthest, the fastest) displays fluctuations relating to the Kardar-Parisi-Zhang (KPZ) universality class. This model has been studied in the case of random walks on a 1D lattice where particles can only move to neighboring sites and the transition probabilities at each site and time are drawn from a random distribution. We study a generalization of this model where particles can move to any site on the lattice and show that the first two moments of the tail probability converge to those of the KPZ equation. We translate these results into predictions of physical measurements of a system of diffusing particles – the position of the maximum particle and fastest first passage time – and verify the predictions numerically. We find the scaling exponents demonstrate super-universal behavior as determined by the statistics of the random environment.   

Active surface flows modify stability of phase-separated lipid membrane domains

Phase separation of multicomponent lipid membranes is characterized by circular domains, which nucleate and coarsen slowly in time as ∼ t^1/3, following classical theories of coalescence and Ostwald ripening. In this work, we study both the coarsening kinetics and morphology of phase-separating lipid membranes subjected to nonequilibrium forces and flows transmitted by motor-driven gliding actin filaments. We experimentally observe that surface flows, driven by an adsorbed contractile actomyosin cortex, trigger rapid coarsening of non-circular membrane domains that grow as ∼ t^2/3, a 2× acceleration in the growth exponent compared to passive coalescence and Ostwald ripening. We describe these results using analytical theories based on the Smoluchowski coagulation model and the phase field model to predict the domain growth in the presence of active flows. Moreover, we demonstrate that in an alternative flow field, in which actin is instead driven along a substrate by adsorbed myosin, active flows destabilize lipid domain interfaces. As these interfaces become unstable, the domains break up, refining the overall structure, as evidenced by a shift in the structure factor toward higher wavenumbers. Our work demonstrates that active matter forces may be used to control the growth and morphology of membrane domains driven out of equilibrium.   

The Elasto-Adhesive-Capillary Length: An Emergent Length Scale in Soft, Sticky Materials

Surface effects in soft materials and soft-soft interfaces become of comparable magnitudes to bulk effects at certain, small length scales. These significant surface effects, such as adhesion and surface tension, in the presence of nonlinear, large deformations are challenges that present themselves in modeling the interfacial behavior of soft matter. Understanding the surface-driven interfacial failure of these materials is fundamental to harnessing their full potential in small-scale soft matter applications such as functional tissue-like design. Here, we develop an interface-enriched, size-dependent isogeometric finite element framework to quantify the separation mechanics of soft-hard and soft-soft interfaces endowed by low-dimensional energetics. The critical energy release rate, which dictates how much energy is needed to create a unit area of crack surface, is hypothesized to be a function of the surface tension of the material at these scales where surface effects are prominent. Through the developed numerical framework, we aim to characterize this dependence by defining a novel length-scale called the "elasto-adhesive-capillary length", reporting it for a wide range of material parameters and geometries. These numerical results will enable us to characterize the adhesive properties of soft materials at small scales, setting the stage for the design and assembly of miniaturized tissue-like structures.   

3D Printing Soft Silicones: Additive Manufacturing at Ultra-low Interfacial Tension

Soft silicone structures are difficult to fabricate using the conventional FDM approach to 3D printing. Without adding rheological modifiers to silicone inks, printed features will sag and spread upon deposition from the nozzle. Embedded printing into jammed support materials helps to overcome this challenge by trapping the silicone ink in space as it is printed, eliminating the need for modifying the ink formulation with additives. However, previously developed support materials for 3D silicone printing exhibit a large interfacial tension against silicone inks, creating instabilities that deform and break up fine features, setting a minimum stable feature size. These instabilities can be prevented by developing a jammed support material that is chemically similar to silicone oil and, consequently, has ultra-low interfacial tension against silicone inks. Such a material would enable 3D printing of accurate and high-resolution structures having fine feature sizes. In the work presented here, we developed a silicone oil-based support material having tunable rheological properties that can be optimized for embedded 3D printing of soft silicone. We show that by practically eliminating interfacial tension between silicone ink and the silicone-based support material, features as small as 8 microns in diameter can fabricated; these fine features are indefinitely stable over time. Using this new material, we are able to fabricate robust, accurate, and complex structures like a model brain aneurysm for surgical simulation and a functional tri-leaflet heart valve model for potential biomedical applications.