High energy density picoliter-scale zinc-air microbatteries for colloidal robotics.

The recent interest in microscopic autonomous systems, including microrobots, colloidal state machines, and smart dust, has created a need for microscale energy storage and harvesting. However, macroscopic materials for energy storage have noted incompatibilities with microfabrication techniques, creating substantial challenges to realizing microscale energy systems. Here, we photolithographically patterned a microscale zinc/platinum/SU-8 system to generate the highest energy density microbattery at the picoliter (10 liter) scale.

Machine learning without a processor: Emergent learning in a nonlinear analog network.

Standard deep learning algorithms require differentiating large nonlinear networks, a process that is slow and power-hungry. Electronic (CLLNs) offer potentially fast, efficient, and fault-tolerant hardware for analog machine learning, but existing implementations are linear, severely limiting their capabilities. These systems differ significantly from artificial neural networks as well as the brain, so the feasibility and utility of incorporating nonlinear elements have not been explored.

Materials for electronically controllable microactuators.

Abstract: Electronically controllable actuators have shrunk to remarkably small dimensions, thanks to recent advances in materials science. Currently, multiple classes of actuators can operate at the micron scale, be patterned using lithographic techniques, and be driven by complementary metal oxide semiconductor (CMOS)-compatible voltages, enabling new technologies, including digitally controlled micro-cilia, cell-sized origami structures, and autonomous microrobots controlled by onboard semiconductor electronics. This field is poised to grow, as many of these actuator technologies are the firsts of their kind and much of the underlying design space remains unexplored.

Colloidal robotics.

Robots have components that work together to accomplish a task. Colloids are particles, usually less than 100 µm, that are small enough that they do not settle out of solution. Colloidal robots are particles capable of functions such as sensing, computation, communication, locomotion and energy management that are all controlled by the particle itself.

Emergent microrobotic oscillators via asymmetry-induced order.

Spontaneous oscillations on the order of several hertz are the drivers of many crucial processes in nature. From bacterial swimming to mammal gaits, converting static energy inputs into slowly oscillating power is key to the autonomy of organisms across scales. However, the fabrication of slow micrometre-scale oscillators remains a major roadblock towards fully-autonomous microrobots.

Microscopic robots with onboard digital control.

Autonomous robots-systems where mechanical actuators are guided through a series of states by information processing units to perform a predesigned function-are expected to revolutionize everything from health care to transportation. Microscopic robots are poised for a similar revolution in fields from medicine to environmental remediation. A key hurdle to developing these microscopic robots is the integration of information systems, particularly electronics fabricated at commercial foundries, with microactuators.

Cilia metasurfaces for electronically programmable microfluidic manipulation.

Cilial pumping is a powerful strategy used by biological organisms to control and manipulate fluids at the microscale. However, despite numerous recent advances in optically, magnetically and electrically driven actuation, development of an engineered cilial platform with the potential for applications has remained difficult to realize. Here we report on active metasurfaces of electronically actuated artificial cilia that can create arbitrary flow patterns in liquids near a surface.

Micrometer-sized electrically programmable shape-memory actuators for low-power microrobotics.

Shape-memory actuators allow machines ranging from robots to medical implants to hold their form without continuous power, a feature especially advantageous for situations where these devices are untethered and power is limited. Although previous work has demonstrated shape-memory actuators using polymers, alloys, and ceramics, the need for micrometer-scale electro-shape-memory actuators remains largely unmet, especially ones that can be driven by standard electronics (~1 volt). Here, we report on a new class of fast, high-curvature, low-voltage, reconfigurable, micrometer-scale shape-memory actuators.

Electronically integrated, mass-manufactured, microscopic robots.

Fifty years of Moore's law scaling in microelectronics have brought remarkable opportunities for the rapidly evolving field of microscopic robotics. Electronic, magnetic and optical systems now offer an unprecedented combination of complexity, small size and low cost, and could be readily appropriated for robots that are smaller than the resolution limit of human vision (less than a hundred micrometres). However, a major roadblock exists: there is no micrometre-scale actuator system that seamlessly integrates with semiconductor processing and responds to standard electronic control signals.

Bidirectional Self-Folding with Atomic Layer Deposition Nanofilms for Microscale Origami.

Origami design principles are scale invariant and enable direct miniaturization of origami structures provided the sheets used for folding have equal thickness to length ratios. Recently, seminal steps have been taken to fabricate microscale origami using unidirectionally actuated sheets with nanoscale thickness. Here, we extend the full power of origami-inspired fabrication to nanoscale sheets by engineering bidirectional folding with 4 nm thick atomic layer deposition (ALD) SiN-SiO bilayer films.

Capillary Origami with Atomically Thin Membranes.

Small-scale optical and mechanical components and machines require control over three-dimensional structure at the microscale. Inspired by the analogy between paper and two-dimensional materials, origami-style folding of atomically thin materials offers a promising approach for making microscale structures from the thinnest possible sheets. In this Letter, we show that a monolayer of molybdenum disulfide (MoS) can be folded into three-dimensional shapes by a technique called capillary origami, in which the surface tension of a droplet drives the folding of a thin sheet.

Atomic Layer Deposition for Membranes, Metamaterials, and Mechanisms.

Bending and folding techniques such as origami and kirigami enable the scale-invariant design of 3D structures, metamaterials, and robots from 2D starting materials. These design principles are especially valuable for small systems because most micro- and nanofabrication involves lithographic patterning of planar materials. Ultrathin films of inorganic materials serve as an ideal substrate for the fabrication of flexible microsystems because they possess high intrinsic strength, are not susceptible to plasticity, and are easily integrated into microfabrication processes.

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

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.

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.

Turning statistical physics models into materials design engines.

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.

Optimizing a reconfigurable material via evolutionary computation.

Rapid prototyping by combining evolutionary computation with simulations is becoming a powerful tool for solving complex design problems in materials science. This method of optimization operates in a virtual design space that simulates potential material behaviors and after completion needs to be validated by experiment. However, in principle an evolutionary optimizer can also operate on an actual physical structure or laboratory experiment directly, provided the relevant material parameters can be accessed by the optimizer and information about the material's performance can be updated by direct measurements.

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.

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.

Self-organization into quantized eigenstates of a classical wave-driven particle.

A growing number of dynamical situations involve the coupling of particles or singularities with physical waves. In principle these situations are very far from the wave particle duality at quantum scale where the wave is probabilistic by nature. Yet some dual characteristics were observed in a system where a macroscopic droplet is guided by a pilot wave it generates.

Adapting granular materials through artificial evolution.

Over 200 years after Coulomb's studies, a general connection between the mechanical response of a granular material and the constituents' shape remains unknown. The key difficulty in articulating this relationship is that shape is an inexhaustible parameter, making its systematic exploration infeasible. Here we show that the role of particle shape can, however, be explored efficiently when granular design is viewed in the context of artificial evolution.

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, r(m), scales like near breakup at time τ = 0.