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. Here we investigate the behaviour of these entities when confined in a two-dimensional harmonic potential well. A discrete set of stable orbits is observed, in the shape of successive generalized Cassinian-like curves (circles, ovals, lemniscates, trefoils...). Along these specific trajectories, the droplet motion is characterized by a double quantization of the orbit spatial extent and of the angular momentum. We show that these trajectories are intertwined with the dynamical build-up of central wave-field modes. These dual self-organized modes form a basis of eigenstates on which more complex motions are naturally decomposed

Physical learning beyond the quasistatic limit

Physical networks, such as biological neural networks, can learn desired functions without a central processor, using local learning rules in space and time to learn in a fully distributed manner. Learning approaches such as equilibrium propagation, directed aging, and coupled learning similarly exploit local rules to accomplish learning in physical networks such as mechanical, flow, or electrical networks. In contrast to certain natural neural networks, however, such approaches have so far been restricted to the quasistatic limit, where they learn on time scales slow compared to their physical relaxation. This quasistatic constraint slows down learning, limiting the use of these methods as machine learning algorithms, and potentially restricting physical networks that could be used as learning platforms. Here we explore learning in an electrical resistor network that implements coupled learning, both in the lab and on the computer, at rates that range from slow to far above the quasistatic limit. We find that up to a critical threshold in the ratio of the learning rate to the physical rate of relaxation, learning speeds up without much change of behavior or error. Beyond the critical threshold, the error exhibits oscillatory dynamics but the networks still learn successfully.Comment: 26 pages, 5 figure

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

High Energy Density Picoliter Zn-Air Batteries for Colloidal Robots and State Machines

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 micro-fabrication techniques, creating significant challenges to realizing microscale energy systems. Herein, we photolithographically pattern a microscale Zn/Pt/SU-8 system to generate the highest energy density microbattery at the picoliter (10^-12 L) scale. The device scavenges ambient or solution dissolved oxygen for a Zn oxidation reaction, achieving an energy density ranging from 760 to 1070 Wh L-1 at scales below 100 μm lateral and 2 μm thickness in size. More than 10,000 devices per wafer can be released into solution as functional colloids with energy stored onboard. Within a volume of only 2 pL each, these microbatteries can deliver open circuit voltages of 1.16 V with total energies ranging from 5.5 ± 0.3 to 7.7 ± 1.0 μJ and a maximum power near 2.7 nW. We demonstrate that such systems can reliably power a micron-sized memristor circuit, providing access to non-volatile memory. We also cycle power to drive the reversible bending of microscale bimorph actuators at 0.05 Hz for mechanical functions of colloidal robots. Additional capabilities such as powering two distinct nanosensor types and a clock circuit are also demonstrated. The high energy density, low volume and simple configuration promise the mass fabrication and adoption of such picoliter Zn-air batteries for micron-scale, colloidal robotics with autonomous functions