Marc Miskin

Particle shape effects on the stress response of granular packings

We present measurements of the stress response of packings formed from a wide range of particle shapes. Besides spheres these include convex shapes such as the Platonic solids, truncated tetrahedra, and triangular bipyramids, as well as more complex, non-convex geometries such as hexapods with various arm lengths, dolos, and tetrahedral frames. All particles were 3D-printed in hard resin. Well-defined initial packing states were established through preconditioning by cyclic loading under given confinement pressure. Starting from such initial states, stress-strain relationships for axial compression were obtained at four different confining pressures for each particle type. While confining pressure has the largest overall effect on the mechanical response, we find that particle shape controls the details of the stress-strain curves and can be used to tune packing stiffness and yielding. By correlating the experimentally measured values for the effective Youngs modulus under compression, yield stress and energy loss during cyclic loading, we identify trends among the various shapes that allow for designing a packings aggregate behavior.

Evolving design rules for the inverse granular packing problem

If a collection of identical particles is poured into a container, different shapes will fill to different densities. But what is the shape that fills a container as close as possible to a pre-specified, desired density? We demonstrate a solution to this inverse-packing problem by framing it in the context of artificial evolution. By representing shapes as bonded spheres, we show how shapes may be mutated, simulated, and selected to produce particularly dense or loose packing aggregates, both with and without friction. Moreover, we show how motifs emerge linking these shapes together. The result is a set of design rules that function as an effective solution to the inverse packing problem for given packing procedures and boundary conditions. Finally, we show that these results are verified by experiments on 3D-printed prototypes used to make packings in the real world.

Droplet formation and scaling in dense suspensions

When a dense suspension is squeezed from a nozzle, droplet detachment can occur similar to that of pure liquids. While in pure liquids the process of droplet detachment is well characterized through self-similar profiles and known scaling laws, we show here the simple presence of particles causes suspensions to break up in a new fashion. Using high-speed imaging, we find that detachment of a suspension drop is described by a power law; specifically we find the neck minimum radius, rm, scales like near breakup at time τ = 0. We demonstrate data collapse in a variety of particle/liquid combinations, packing fractions, solvent viscosities, and initial conditions. We argue that this scaling is a consequence of particles deforming the neck surface, thereby creating a pressure that is balanced by inertia, and show how it emerges from topological constraints that relate particle configurations with macroscopic Gaussian curvature. This new type of scaling, uniquely enforced by geometry and regulated by the particles, displays memory of its initial conditions, fails to be self-similar, and has implications for the pressure given at generic suspension interfaces.

Evolutionary pattern design for copolymer directed self-assembly

Directed assembly of block polymers is rapidly becoming a viable strategy for lithographic patterning of nanoscopic features. One of the key attributes of directed assembly is that an underlying chemical or topographic substrate pattern used to direct assembly need not exhibit a direct correspondence with the sought after block polymer morphology, and past work has largely relied on trial-and-error approaches to design appropriate patterns. In this work, a computational evolutionary strategy is proposed to solve this optimization problem. By combining the Cahn–Hilliard equation, which is used to find the equilibrium morphology, and the covariance-matrix evolutionary strategy, which is used to optimize the combined outcome of particular substrate–copolymer combinations, we arrive at an efficient method for design of substrates leading to non-trivial, desirable outcomes.

Turning statistical physics models into materials design engines

Significance A fundamental tenet of science is that the properties of a material are intimately linked to the nature of the constituent components. Although there are powerful methods to predict such properties for given components, a key challenge for materials design is the inverse process: identifying the required components and their structural configuration for given target properties. This paper presents a new approach to this challenge. A formalism is introduced that generates algorithms for materials design both under equilibrium and under nonequilibrium conditions and operates without the need for user input beyond a design goal. This formalism is broadly applicable, fast, and robust, and it provides a powerful tool for materials optimization as well as discovery. Despite the success statistical physics has enjoyed at predicting the properties of materials for given parameters, the inverse problem, identifying which material parameters produce given, desired properties, is only beginning to be addressed. Recently, several methods have emerged across disciplines that draw upon optimization and simulation to create computer programs that tailor material responses to specified behaviors. However, so far the methods developed either involve black-box techniques, in which the optimizer operates without explicit knowledge of the material’s configuration space, or require carefully tuned algorithms with applicability limited to a narrow subclass of materials. Here we introduce a formalism that can generate optimizers automatically by extending statistical mechanics into the realm of design. The strength of this approach lies in its capability to transform statistical models that describe materials into optimizers to tailor them. By comparing against standard black-box optimization methods, we demonstrate how optimizers generated by this formalism can be faster and more effective, while remaining straightforward to implement. The scope of our approach includes possibilities for solving a variety of complex optimization and design problems concerning materials both in and out of equilibrium.

Graphene-based bimorphs for micron-sized, autonomous origami machines

Significance We build origami machines the size of cells by folding them out of atomically thin paper. At the heart of our approach is an actuator technology made from graphene and a nanometer-thick layer of glass. We use these actuators to fold 2D patterns into targeted 3D structures. The resulting machines are freely deployed in solutions, can change shape in fractions of a second, carry loads large enough to support embedded electronics, and can be fabricated en masse. This work opens the door to a generation of small machines for sensing, robotics, energy harvesting, and interacting with biological systems on the cellular level. Origami-inspired fabrication presents an attractive platform for miniaturizing machines: thinner layers of folding material lead to smaller devices, provided that key functional aspects, such as conductivity, stiffness, and flexibility, are persevered. Here, we show origami fabrication at its ultimate limit by using 2D atomic membranes as a folding material. As a prototype, we bond graphene sheets to nanometer-thick layers of glass to make ultrathin bimorph actuators that bend to micrometer radii of curvature in response to small strain differentials. These strains are two orders of magnitude lower than the fracture threshold for the device, thus maintaining conductivity across the structure. By patterning 2-𝝁m-thick rigid panels on top of bimorphs, we localize bending to the unpatterned regions to produce folds. Although the graphene bimorphs are only nanometers thick, they can lift these panels, the weight equivalent of a 500-nm-thick silicon chip. Using panels and bimorphs, we can scale down existing origami patterns to produce a wide range of machines. These machines change shape in fractions of a second when crossing a tunable pH threshold, showing that they sense their environments, respond, and perform useful functions on time and length scales comparable with microscale biological organisms. With the incorporation of electronic, photonic, and chemical payloads, these basic elements will become a powerful platform for robotics at the micrometer scale.

From nanoscale cohesion to macroscale entanglement: Opportunities for designing granular aggregate behavior by tailoring grain shape and interactions

The packing arrangement of individual particles inside a granular material and the resulting response to applied stresses depend critically on particle-particle interactions. One aspect that recently received attention are nanoscale surface features of particles, which play an important role in determining the strength of cohesive van der Waals and capillary interactions and also affect tribo-charging of grains. We describe experiments on freely falling granular streams that can detect the contributions from all three of these forces. We show that it is possible to measure the charge of individual grains and build up distributions that are detailed enough to provide stringent tests of tribo-charging models currently available. A second aspect concerns particle shape. In this case steric interactions become important and new types of aggregate behavior can be expected when non-convex particle shapes are considered that can interlock or entangle. However, a general connection between the mechanical response...

Measuring and Manipulating the Adhesion of Graphene

We present a technique to precisely measure the surface energies between two-dimensional materials and substrates that is simple to implement and allows exploration of spatial and chemical control of adhesion at the nanoscale. As an example, we characterize the delamination of single-layer graphene from monolayers of pyrene tethered to glass in water and maximize the work of separation between these surfaces by varying the density of pyrene groups in the monolayer. Control of this energy scale enables high-fidelity graphene-transfer protocols that can resist failure under sonication. Additionally, we find that the work required for graphene peeling and readhesion exhibits a dramatic rate-independent hysteresis, differing by a factor of 100. This work establishes a rational means to control the adhesion of 2D materials and enables a systematic approach to engineer stimuli-responsive adhesives and mechanical technologies at the nanoscale.

Transitions of Designs

A new host of materials science problems open up once the task of identifying extreme materials has been automized. This is because optimization condenses the wide range of possible material microstructures into only those that emphasize extraordinary behaviors. In this chapter we ask how materials that have been optimized for a particular task change when the parameters that define the optimization are varied. Specifically, we consider which particle shape cools the fastest in a granular gas. We show that the answer depends crucially on the density and number of elements in the shape and that transitions between classes of optimal shapes take place as these parameters are varied. We use these transitions to isolate new pieces of physics that describe the cooling processes of the gas. More generally, we show how material optimization enables a new workflow to isolate the relevant pieces of physics that govern a material’s bulk behavior.