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Optical tweezers are an established and versatile tool for the optical trapping and manipulation of microscopic object using light. Modelling the interaction between particles and light, i.e., being able to calculate the optical force and torque the light exerts on the particle, is important to both understand the outcome of experiments and help designing the experimental setup to the obtain a certain outcome. Different modelling approximation and relative calculation techniques are employed depending on the size of the particle and the features of the trapping light. In this work, we will focus on the geometrical optics regime, which hold for particles whose size is significantly larger than the wavelength of the light. In this approximation, optical forces and torques are calculated by discretizing the trapping light beam into a set of rays. Each ray, impinging on the particle, is reflected and refracted multiple times and, in this scattering process, transfers momentum and angular momentum to the particle. However, the choice of the discretization, i.e., which and how many rays we use to represent a beam, sets a trade-off between calculation speed and accuracy. Here, we show that using neural networks allows overcoming this limitation, obtaining not only faster but also more accurate simulations. We demonstrate this using an optically trapped spherical particle for which we obtain an analytical solution to use as ground truth. Then, we exploit our neural networks method to study the dynamics of ellipsoidal particles in a double trap, a system that would be computationally impossible otherwise.

Reference
David Bronte Ciriza, Alessandro Magazzù, Agnese Callegari, Gunther Barbosa, Antonio A. R. Neves, Maria A. Iatì, Giovanni Volpe, Onofrio M. Maragò, Faster and more accurate geometrical-optics optical force calculation using neural networks, ACS Photonics 10, 234–241 (2023)

Janus particles are microscopic objects characterized by one feature with dual properties. Typical examples of Janus particles are metal-coated silica particles, widely used in soft and active matter applications because of their versatility and relative simplicity of their fabrication. Janus particles are often utilized in the presence of optical potentials. Given the non-homogeneous nature of their refractive index composition, the interaction between the Janus particle and light is non-trivial to model: in addition to the optical force, the particle experiences an optical torque, even in the case of spherically shaped Janus particles, and its metallic cap can also absorb part of the optical power impinging on the particle. Here, we provide a description of the Janus particle in the geometrical optics approximation, and an implementation for calculating forces, torques, and absorption on partially coated Janus particles of spherical and ellipsoidal shape. This implementation is based on the existing OTGO toolbox, developed in Matlab for calculating optical forces and torques in the geometrical optics regime. We first validate our model against the known experimental results and show that interesting dynamical effects arise in the presence of travelling-wave optical potential.

In the last 20 years, active matter has been a very successful research field, bridging the fundamental physics of nonequilibrium thermodynamics with applications in robotics, biology, and medicine. This field deals with active particles, which, differently from passive Brownian particles, can harness energy to generate complex motions and emerging behaviors. Most active-matter experiments are performed with microscopic particles and require advanced microfabrication and microscopy techniques. Here, we propose some macroscopic experiments with active matter employing commercially available toy robots, i.e., the Hexbugs. We demonstrate how they can be easily modified to perform regular and chiral active Brownian motion. We also show that Hexbugs can interact with passive objects present in their environment and, depending on their shape, set them in motion and rotation. Furthermore, we show that, by introducing obstacles in the environment, we can sort the robots based on their motility and chirality. Finally, we demonstrate the emergence of Casimir-like activity-induced attraction between planar objects in the presence of active particles in the environment.

Reference
Angelo Barona Balda, Aykut Argun, Agnese Callegari, Giovanni Volpe, Playing with Active Matter, arXiv: 2209.04168