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Anomalous Dynamics of Superparamagnetic Colloidal Microrobots with Tailored Statistics

Gentili, Alessia; Klages, Rainer; Volpe, Giorgio; (2025) Anomalous Dynamics of Superparamagnetic Colloidal Microrobots with Tailored Statistics. Small , Article e06538. 10.1002/smll.202506538. (In press). Green open access

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Abstract

Living organisms have developed advanced motion strategies for efficient space exploration, serving as inspiration for the movements of microrobots. These real‐life strategies often involve anomalous dynamics displaying random movement patterns that deviate from Brownian motion. Despite their biological inspiration, autonomous stochastic navigation strategies of current microrobots remain much less versatile than those of their living counterparts. Supported by theoretical reasoning, this work demonstrates superparamagnetic colloidal microrobots with fully customizable stochastic dynamics displaying the entire spectrum of anomalous diffusion, from subdiffusion to superdiffusion, across statistically significant spatial and temporal scales (covering at least two decades). By simultaneously tuning microrobots' step‐length distribution and, critically, their velocity autocorrelation function with magnetic fields, fundamental anomalous dynamics are reproduced with tailored properties mimicking Lévy walks and fractional Brownian motion. These findings pave the way for programmable microrobotic systems that replicate optimal stochastic navigation strategies found in nature for applications in medical robotics and environmental remediation.

Type: Article
Title: Anomalous Dynamics of Superparamagnetic Colloidal Microrobots with Tailored Statistics
Open access status: An open access version is available from UCL Discovery
DOI: 10.1002/smll.202506538
Publisher version: https://doi.org/10.1002/smll.202506538
Language: English
Additional information: © 2025 The Author(s). Small published by Wiley-VCH GmbH This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Chemistry
URI: https://discovery.ucl.ac.uk/id/eprint/10216496
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