Ureña Marcos, José Carlos (2023)
Modelling active particles in time-dependent fields.
Technische Universität Darmstadt
doi: 10.26083/tuprints-00024770
Ph.D. Thesis, Primary publication, Publisher's Version
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Item Type: | Ph.D. Thesis | ||||
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Type of entry: | Primary publication | ||||
Title: | Modelling active particles in time-dependent fields | ||||
Language: | English | ||||
Referees: | Liebchen, Prof. Dr. Benno ; Hardt, Prof. Dr. Steffen ; Klitzing, Prof. Dr. Regine von ; Braun, Prof. Dr. Jens | ||||
Date: | 7 November 2023 | ||||
Place of Publication: | Darmstadt | ||||
Collation: | xiv, 103 Seiten | ||||
Date of oral examination: | 1 November 2023 | ||||
DOI: | 10.26083/tuprints-00024770 | ||||
Abstract: | Active particles tap into energy from their environment to produce persistent motion. Examples can be found on different lengthscales in nature, ranging from motile bacteria or sperm cells to birds or fish. They exploit their ability to self-propel to perform key tasks in their survival, which has inspired the assembly of synthetic active particles (also known as ‘microswimmers’ when they are micron sized), such as Janus colloids or droplet swimmers. The development of these motile (micro)agents holds great promise for tackling environmental and medical issues such as water treatment, targeted drug delivery or microsurgery. However, many challenges arise when conceiving these agents. For instance, control schemes need to be engineered so that these agents can navigate through complex environments, and their design has to be suitable for these environments. As of today, these challenges have not yet been optimally met. In this thesis, the control and design of autonomous motile agents are addressed, with the focus on how active particles behave in time-dependent fields. Firstly, a novel control scheme based on the use of ac fields is introduced. It exploits the interplay between (weak) rotational inertia and fast, unbiased ac fields to induce a persistent particle orientation. This control scheme is generically relevant for different types of active particles, such as artificial self-propelled colloids or vibrated granulates, in different setups, as illustrated here with Langevin dynamics simulations. A most remarkable example consists in inertial active particles self-propelling against a gravitational field and sedimenting at the top wall of the simulation box. An analytical framework based on a separation of fast and slow timescales is also presented, revealing that control can be gained by stabilising equilibrium points in the orientation dynamics of the particles thanks to the application of rapidly oscillating ac fields. Secondly, a new kind of self-powered, all-aqueous droplet swimmer is presented, which shows a dynamical transition from linear to chiral self-propulsion. This droplet swimmer emits surface tension–lowering polymer molecules, thus creating concentration and flow fields which change with time due to Marangoni flows and the droplet motion. In this work, the full time-dependent hydrodynamics of the droplet swimmer are modelled, with self-propulsion resulting from surface tension gradients along the water–droplet interface. Numerical simulations based on this model reveal that the transition to chiral motion is caused by the interaction of the droplet with its own chemical history through Marangoni flows. This new droplet swimmer has a higher level of autonomy, in terms of self-sustainability and dynamical diversity, than similar droplet swimmers realised in the past. Its features make it suitable for applications in environments where refuelling is not easily possible, such as biological media. |
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Status: | Publisher's Version | ||||
URN: | urn:nbn:de:tuda-tuprints-247703 | ||||
Classification DDC: | 500 Science and mathematics > 530 Physics | ||||
Divisions: | 05 Department of Physics > Institute for Condensed Matter Physics > Theory of Soft Matter | ||||
Date Deposited: | 07 Nov 2023 15:57 | ||||
Last Modified: | 13 Dec 2023 12:01 | ||||
URI: | https://tuprints.ulb.tu-darmstadt.de/id/eprint/24770 | ||||
PPN: | 513391266 | ||||
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