Noise-enabled optical ratchets

Inferring potential landscapes from noisy trajectories of particles within an optical feedback trap

While particle trajectories encode information on their governing potentials, potentials can be challenging to robustly extract from trajectories. Measurement errors may corrupt a particle’s position, and sparse sampling of the potential limits data in higher energy regions such as barriers. We develop a Bayesian method to infer potentials from trajectories corrupted by Markovian measurement noise without assuming prior functional form on the potentials. As an alternative to Gaussian process priors over potentials, we introduce structured kernel interpolation to the Natural Sciences which allows us to extend our analysis to large datasets. Structured-Kernel-Interpolation Priors for Potential Energy Reconstruction (SKIPPER) is validated on 1D and 2D experimental trajectories for particles in a feedback trap.

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A feedback trap was used to generate noisy Langevin microbead trajectories

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The potential energy surface is recovered using a Bayesian formulation

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The formulation uses a structured-kernel-interpolation Gaussian process (SKI-GP) to tractably approximate Gaussian process regression for larger datasets

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Thanks to our adaptation of SKI-GP, we have broadened the use of Gaussian processes for natural science applications

Phase Dependent Vectorial Current Control in Symmetric Noisy Optical Ratchets

In this work, we demonstrate single microparticle transport in a symmetric noisy optical ratchet made with a linear array of 20 optical potentials, where each potential is a spatially symmetric low power (<2.5  mW) three-dimensional trap. Both the external force F(t) and the depth V_{0i}(t) of the optical potentials are dynamic and change at the same frequency ν=2  Hz. The depths of the individual optical potentials are random (uncorrelated noise) distributed around a mean value V_{0}, ⟨V_{0i}(t)⟩=V_{0}, while the external force is periodic and unbiased ⟨F(t)⟩=0. The system is completely symmetric for times t≫1/ν. Directed transport is possible as a result of the symmetry being broken at times on the order of 1/ν. We find that the direction and speed of motion (current) are coupled to the phase difference between the noise in the optical potentials and the external periodic force.

Microparticle transport networks with holographic optical tweezers and cavitation bubbles

Optical transport networks for active absorbing microparticles are made with holographic optical tweezers. The particles are powered by the optical potentials that make the network and transport themselves via random vapor-propelled hops to different traps. The geometries explored for the optical traps are square lattices, circular arrays, and random arrays. The degree distribution for the connections or possible paths between the traps are localized like in the case of random networks. The average travel times across n different traps scale as n^b with exponents in the range of 2.06 and 2.31, in agreement with random walks on connected networks (upper bound ∝n³). Finally, a particle traveling the network attracts others as a result of the vapor explosions enhancing transport.