Bryan VanSaders

Acoustic levitation is frequently used for non-contact manipulation of objects and to study the impact of microgravity on physical and biological processes. While the force field produced by sound pressure lifts particles against gravity (primary acoustic force), multiple levitating objects in the same acoustic cavity interact via forces that arise from scattered sound (secondary acoustic forces). Current experimental techniques for obtaining these force fields are not well-suited for mapping the primary force field at high spatial resolution and cannot directly measure the secondary scattering force. Here we introduce a method that can measure both acoustic forces in situ, including secondary forces in the near-field limit between arbitrarily shaped, closely spaced objects. Operating similarly to an atomic force microscope, the method inserts into the acoustic cavity a suitably shaped probe tip at the end of a long, flexible cantilever and optically detects its deflection. This makes it possible to measure forces with a resolution better than 50 nN, and also to apply stress or strain in a controlled manner to manipulate levitated objects. We demonstrate this by extracting the acoustic potential present in a levitation cavity, directly measuring the acoustic scattering force between two objects, and applying tension to a levitated granular raft of acoustically-bound particles in order to obtain the force-displacement curve for its deformation.

Many biomolecular systems can be viewed as ratchets that rectify environmental noise through measurements and information processing. As miniaturized robots cross the scale of unicellular organisms, on-board sensing and feedback open new possibilities for propulsion strategies that exploit fluctuations rather than fight them. Here, we study extended media in which many constituents display a feedback control loop between measurement of their microstates and the capability to bias their noise-induced transitions. We dub such many body systems informational active matter and show how information theoretic arguments and kinetic theory derivations yield their macroscopic properties starting from microscopic agent strategies. These include the ability to self-propel without applying work and to print patterns whose resolution improves as noise increases. We support our analytical results with extensive simulations of a fluid of `thinker' type particles that can selectively change their diameters to bias scattering transitions. This minimal model can be regarded as a non-equilibrium analogue of entropic elasticity that exemplifies the key property of this class of systems: self-propulsive forces grow ever stronger as environmental noise increases thanks to measurements and control actions undertaken by the microscopic constituents. We envision applications of our ideas ranging from noise induced patterning performed by collections of microrobots to reinforcement learning aided identification of migration strategies for collections of organisms that exploit turbulent flows or fluctuating chemotactic fields.