Worm-Inspired Active Filaments Sweep Disorder into Order
Every teenager knows that their room will not tidy up by itself. Without intervention, it will inevitably become messier, and they will need to do some work to turn disorder into order. When faced with a similar problem—particle collection—scientists have tried to get individual bacteria, robots, or other self-propelling units to put in the work [1, 2]. But unlike a teenager, a single such unit is usually insufficient to get the job done. Now Rosa Sinaasappel of the University of Amsterdam and her collaborators have proposed and tested a strategy that enables a single active filament to act as a sweeping agent [3]. Thanks to the versatility of polymer architectures, the investigation opens up a huge molecular-design space.
One of life’s most defining properties is its constant struggle against the second law of thermodynamics. At different scales, living organisms need to maintain complex structures or perform directed and persistent motion, feats that would be extraordinarily improbable in thermal equilibrium [4]. Organisms are able to sustain order against entropy by means of constant energy consumption, a feature called “activity.” Conceptually, the sweeping of small objects into piles is a similar problem. The goal is to reach a low-entropy state that is highly disfavored at equilibrium. Bacteria and other active particles, driven by their persistent motion, spontaneously aggregate, and they have been shown to induce clustering of passive particles [1, 2]. However, successful clustering typically requires using a large number of active particles or engineering a complex setting with a favorable geometry [5, 6]. These difficulties have hindered applicability to real-life scenarios.
The inspiration for using active filaments, rather than active particles, came from the behavior of worms (Fig. 1). These invertebrates continuously reshape their environment, influencing sediment aggregation, nutrient cycling, and oxygenation [7]. Notably, they do it via mechanical contact and not via long-range forces or hydrodynamic effects, which are typically negligible in their environment. The work of Sinaasappel and company builds on these premises and shows that the aggregation is not due to the worms’ behavior per se. Rather, it’s a consequence of the interplay between the filamentous nature of the objects and their correlated, active dynamics.
Sinaasappel and colleagues reached this result by comparing observations of particle collection by live worms with numerical simulations and with mechanical analogues made of connected robotic units. The researchers focused on the collection of inert particles—grains of sand for the worms, for example—confined within a circular tray. In all three cases—experiments, simulations, and robotic modeling—the kinetics of particle collection were similar. In particular, the characteristic aggregation time was remarkably consistent once it had been rescaled to account for the differences among the systems. (The rescaling was with respect to the time the filament takes to cross the circular confinement.) The rescaled aggregation time was also independent of polymeric properties, such as the filament rigidity. What’s more, the average size attained by the particle clusters after long times was systematically larger for more flexible filaments, although in this case the results did depend on the system even after rescaling.
To Sinaasappel and co-workers these observations suggested the presence of a general mechanism, untied from the details of the filament dynamics. The trend observed for the cluster size pointed at some elusive parameter controlling cluster dynamics. Going for a minimal theoretical description, the authors proposed an aggregation–fragmentation framework, governed by the “footprint” of the filament, that is, the average transverse extent of the pathway cleared along the trajectory. As clustering is driven only by contact forces, over time the filament pushes particles away from its path, promoting aggregation. But the filament also breaks aggregates if they are found in its path. This interpretation leads to a scaling equation that depends only on the footprint. The model could accurately reproduce all three studied systems, proving that it captures the essential underlying physics.
The study showed how a single extended body can reshape, by itself, the surrounding environment simply through contact forces, a mechanism that could be valid across different scales in soft media. The contact mechanism’s remarkable simplicity sets active filaments apart from other active units, which display more complex forms of interaction with the transported particles [8].
It remains to be seen whether the newly derived scaling relation will retain its validity for different filament designs (or architecture, in the jargon of polymer physics). Consequently, the ability to use this relation to guide future investigations is uncertain. However, the parameter space explored by Sinaasappel and company is already huge and open for optimization. Prototypes of soft robots for environmental manipulation already exist, but they need a substantial degree of external control [9]. The framework presented in this work could lead to the development of simpler robotic materials capable of performing tasks without the need for external control or feedback.
References
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- S. Gokhale et al., “Dynamic clustering of passive colloids in dense suspensions of motile bacteria,” Phys. Rev. E 105, 054605 (2022).
- R. Sinaasappel et al., “Particle sweeping and collection by active and living filaments,” Phys. Rev. X 16, 011003 (2026).
- P. Nelson, Biological Physics: Energy, Information, Life (W. H. Freeman, New York, 2003)[Amazon][WorldCat].
- S. Williams et al., “Confinement-induced accumulation and de-mixing of microscopic active-passive mixtures,” Nat. Commun. 13, 4776 (2022).
- H. Serna et al., “Sorting of binary active–passive mixtures in designed microchannels,” Soft Matter 21, 8781 (2025).
- K. W. Cummins and M. J. Klug, “Feeding ecology of stream invertebrates,” Ann. Rev. Ecol. Syst. 10, 147 (1979).
- R. Jeanneret et al., “Entrainment dominates the interaction of microalgae with micron-sized objects,” Nat. Commun. 7, 12518 (2016).
- K. Becker et al., “Active entanglement enables stochastic, topological grasping,” Proc. Natl. Acad. Sci. U.S.A. 119, e2209819119 (2022).