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Naturally occurring materials are often disordered, with their bulk properties being challenging to predict from the structure, due to the lack of underlying crystalline axes. In this paper, we develop a digital pipeline from algorithmically-created configurations with tunable disorder to 3D printed materials, as a tool to aid in the study of such materials, using electrical resistance as a test case. The designed material begins with a random point cloud that is iteratively evolved using Lloyd's algorithm to approach uniformity, with the points being connected via a Delaunay triangulation to form a disordered network metamaterial. Utilizing laser powder bed fusion additive manufacturing with stainless steel 17-4 PH and titanium alloy Ti-6Al-4V, we are able to experimentally measure the bulk electrical resistivity of the disordered network. We found that the graph Laplacian accurately predicts the effective resistance of the structure, but is highly sensitive to anisotropy and global network topology, preventing a single network statistic or disorder characterization from predicting global resistivity. 

Models for slow flow of dense granular materials often treat the medium as incompressible, thereby neglecting the role of Reynolds dilatancy. However, recent particle simulations have demonstrated the presence of a significant coupling between the volume fraction and velocity fields. The model of Dsouza & Nott (J. Fluid Mech., vol. 888, 2020, R3) incorporates dilatancy and captures the coupling, but it has thus far lacked experimental validation. In this paper, we provide the first experimental demonstration of dilatancy and its coupling to the kinematics in a two-dimensional cylindrical Couette cell. We find a shear layer near the inner cylinder within which there is significant dilation. Within the shear layer, the azimuthal velocity decays roughly exponentially and the volume fraction rises with radial distance from the inner cylinder. The predictions of the model of Dsouza & Nott (2020) are in good agreement with the experimental data for a variety of roughness features of the outer cylinder. Moreover, by comparing the steady states resulting from different initial volume fraction profiles (but having the same average), we show the inter-dependence of the velocity and volume fraction fields, as predicted by the model. Our results establish the importance of shear dilatancy even in systems of constant volume. 

Naturally occurring materials are often disordered, with their bulk properties being challenging to predict from the structure, due to the lack of underlying crystalline axes. In this paper, we develop a digital pipeline from algorithmically-created configurations with tunable disorder to 3D printed materials, as a tool to aid in the study of such materials, using electrical resistance as a test case. The designed material begins with a random point cloud that is iteratively evolved using Lloyd's algorithm to approach uniformity, with the points being connected via a Delaunay triangulation to form a disordered network metamaterial. Utilizing laser powder bed fusion additive manufacturing with stainless steel 17-4 PH and titanium alloy Ti-6Al-4V, we are able to experimentally measure the bulk electrical resistivity of the disordered network. We found that the graph Laplacian accurately predicts the effective resistance of the structure, but is highly sensitive to anisotropy and global network topology, preventing a single network statistic or disorder characterization from predicting global resistivity. 

Earth's surface materials constitute the basis for life and natural resources. Most of these materials can be catergorized as soft matter, yet a general physical understanding of the ground beneath our feet is still lacking. Here we provide some perspectives. 

Abstract The surfaces of many planetary bodies, including asteroids and small moons, are covered with dust to pebble-sized regolith held weakly to the surface by gravity and contact forces. Understanding the reaction of regolith to an external perturbation will allow for instruments, including sensors and anchoring mechanisms for use on such surfaces, to implement optimized design principles. We analyze the behavior of a flexible probe inserted into loose regolith simulant as a function of probe speed and ambient gravitational acceleration to explore the relevant dynamics. The EMPANADA experiment (Ejecta-Minimizing Protocols for Applications Needing Anchoring or Digging on Asteroids) flew on several parabolic flights. It employs a classic granular physics technique, photoelasticity, to quantify the dynamics of a flexible probe during its insertion into a system of bi-disperse, centimeter-sized model grains. We identify the force chain structure throughout the system during probe insertion at a variety of speeds and for four different levels of gravity: terrestrial, Martian, lunar, and microgravity. We identify discrete, stick-slip failure events that increase in frequency as a function of the gravitational acceleration. In microgravity environments, stick-slip behaviors are negligible, and we find that faster probe insertion can suppress stick-slip behaviors where they are present. We conclude that the mechanical response of regolith on rubble-pile asteroids is likely quite distinct from that found on larger planetary objects, and scaling terrestrial experiments to microgravity conditions may not capture the full physical dynamics. 

Abstract Fluid droplets can be induced to move over rigid or flexible surfaces under external or body forces. We describe the effect of variations in material properties of a flexible substrate as a mechanism for motion. In this paper, we consider a droplet placed on a substrate with either a stiffness or surface energy gradient and consider its potential for motion via coupling to elastic deformations of the substrate. In order to clarify the role of contact angles and to obtain a tractable model, we consider a 2D droplet. The gradients in substrate material properties give rise to asymmetric solid deformation and to unequal contact angles, thereby producing a force on the droplet. We then use a dynamic viscoelastic model to predict the resulting dynamics of droplets. Numerical results quantifying the effect of the gradients establish that it is more feasible to induce droplet motion with a gradient in surface energy. The results show that the magnitude of elastic modulus gradient needed to induce droplet motion exceeds experimentally feasible limits in the production of soft solids and is therefore unlikely as a passive mechanism for cell motion. In both cases, of surface energy or elastic modulus, the threshold to initiate motion is achieved at lower mean values of the material properties.