# Forces and symmetry breaking of a living meso-swimmer

## TL;DR

This paper explores how the meso-organism Artemia swims by breaking time-reversal symmetry, offering insights for designing biomimetic meso-robots.

## Contribution

A universal force-based scaling law for meso-scale swimming is established using a novel combination of force sensors and image analysis.

## Key findings

- Artemia increases propulsive force by breaking time-reversal symmetry.
- A force-based scaling law applies to various micro- to meso-organisms.
- The study reveals fundamental dynamics of meso-scale biological swimming.

## Abstract

Swimming is ubiquitous in nature and crucial for the survival of a wide range of organisms. The physics of swimming at the viscosity-dominated microscale and inertia-dominated macroscale is well studied. However, in between lies a complicated mesoscale with swimmers affected by non-linear and time-dependent fluid mechanics. The intricate motility strategies, combined with complex and periodically changing body shapes add extra challenges for accurate meso-swimming modelling. Here, we have further developed the micropipette force sensor to directly probe the swimming forces of the meso-organism Artemia. Through deep neural network-based image analysis, we show how Artemia achieves an increased propulsive force by increasing its level of time-reversal symmetry breaking. We present a universal force-based scaling law for a wide range of micro- to meso-organisms with different body shapes, swimming strategies, and level of inertia at the mesoscale. These results capture fundamental aspects of biological meso-swimming dynamics and provide guidance for future biomimicking meso-robot designs.

The physics of swimming at the mesoscale, where nonlinear and time-dependent fluid mechanics prevail, remains poorly understood. Here, the authors use a micropipette force sensor and deep neural network–based image analysis to reveal how Artemia enhances propulsion, establishing a universal force-based scaling law that informs future biomimetic meso-robot designs.