arXiv subjects

Benno Liebchen

Publications and source records attributed to Benno Liebchen.

At least 37 records · Page 2Linked to original sources

arXiv2024

Self-reverting vortices in chiral active matter

There is currently a strong interest in the collective behavior of chiral active particles that can propel and rotate themselves. In the presence of alignment interactions for many chiral particles, chiral self-propulsion can induce vortex patterns in the velocity fields. However, these emerging patterns are non-permanent, and do not induce global vorticity. Here we combine theoretical arguments and computer simulations to predict a so-far unknown class of collective behavior. We show that, for chiral active particles, vortices with significant dynamical coherence emerge spontaneously. They originate from the interplay between attraction interactions and chirality in the absence of alignment interactions. Depending on parameters, the vortices can either feature a constant vorticity or a vorticity that oscillates periodically in time, resulting in self-reverting vortices. Our results may guide future experiments to realize customized collective phenomena such as spontaneously rotating gears and patterns with a self-reverting order.

cond-mat.soft↗

arXiv2024

AMEP: The Active Matter Evaluation Package for Python

The Active Matter Evaluation Package (AMEP) is a Python library for analyzing simulation data of particle-based and continuum simulations. It provides a powerful and simple interface for handling large data sets and for calculating and visualizing a broad variety of observables that are relevant to active matter systems. Examples range from the mean-square displacement and the structure factor to cluster-size distributions, binder cumulants, and growth exponents. AMEP is written in pure Python and is based on powerful libraries such as NumPy, SciPy, Matplotlib, and scikit-image. Computationally expensive methods are parallelized and optimized to run efficiently on workstations, laptops, and high-performance computing architectures, and an HDF5-based data format is used in the backend to store and handle simulation data as well as analysis results. AMEP provides the first comprehensive framework for analyzing simulation results of both particle-based and continuum simulations (as well as experimental data) of active matter systems. In particular, AMEP also allows it to analyze simulations that combine particle-based and continuum techniques such as used to study the motion of bacteria in chemical fields or for modeling particle motion in a flow field. AMEP is available at https://amepproject.de and can be installed via conda and pip.

cond-mat.soft↗

arXiv2024

Motility-induced coexistence of a hot liquid and a cold gas

If two phases exist at the same time, such as a gas and a liquid, they have the same temperature. This fundamental law of equilibrium physics is known to apply even to many non-equilibrium systems. However, recently, there has been much attention in the finding that inertial self-propelled particles like Janus colloids in a plasma or microflyers could self-organize into a hot gas-like phase that coexists with a colder liquid-like phase. Here, we show that a kinetic temperature difference across coexisting phases can occur even in equilibrium systems when adding generic (overdamped) self-propelled particles. In particular, we consider mixtures of overdamped active and inertial passive Brownian particles and show that when they phase separate into a dense and a dilute phase, both phases have different kinetic temperatures. Surprisingly, we find that the dense phase (liquid) cannot only be colder but also hotter than the dilute phase (gas). This effect hinges on correlated motions where active particles collectively push and heat up passive ones primarily within the dense phase. Our results answer the fundamental question if a non-equilibrium gas can be colder than a coexisting liquid and create a route to equip matter with self-organized domains of different kinetic temperatures.

cond-mat.soft↗

arXiv2024

Active adaptolates: motility-induced percolating structures with an adaptive packing geometry

It is well known that periodic potentials can be used to induce freezing and melting in colloids. Here, we transfer this concept to active systems and find the emergence of a so-far unknown active matter phase in between the frozen solid-like phase and the molten phase. This phase of "active adaptolates" adopts the geometry of the underlying lattice like the frozen phase, maintains ballistic dynamics like the molten phase, and percolates. In particular, this finding creates a route to use external fields for designing the intrinsic structure of active systems without qualitatively affecting their dynamics.

cond-mat.soft↗

arXiv2023

Self-solidifying active droplets showing memory-induced chirality

Most synthetic microswimmers do not reach the autonomy of their biological counterparts in terms of energy supply and diversity of motion. Here we report the first all-aqueous droplet swimmer powered by self-generated polyelectrolyte gradients, which shows memory-induced chirality while self-solidifying. An aqueous solution of surface tension-lowering polyelectrolytes self-solidifies on the surface of acidic water, during which polyelectrolytes are gradually emitted into the surrounding water and induce linear self-propulsion via spontaneous symmetry breaking. The low diffusion coefficient of the polyelectrolytes leads to long-lived chemical trails which cause memory effects that drive a transition from linear to chiral motion without requiring any imposed symmetry breaking. The droplet swimmer is capable of highly efficient removal (up to 85%) of uranium from aqueous solutions within 90 min, benefiting from self-propulsion and flow-induced mixing. Our results provide a route to fueling self-propelled agents which can autonomously perform chiral motion and collect toxins.

cond-mat.soft↗

arXiv2023

Giant Activity-Induced Stress Plateau in Entangled Polymer Solutions

We study the viscoelastic properties of highly entangled, flexible, self-propelled polymers using Brownian dynamics simulations. Our results show that the active motion of the polymer increases the height of the stress plateau by orders of magnitude due to the emergence of grip forces at entanglement points. Identifying the activity-induced energy of a single polymer and the ratio of polymer length to self-propulsion velocity as relevant energy and time scales, we find the stress autocorrelation functions collapse across Péclet numbers. We predict that the long-time viscosity scales with polymer length squared $\sim L^2$, in contrast to equilibrium counterparts $\sim L^3$. These insights offer prospects for designing new materials with activity-responsive mechanical properties.

cond-mat.soft↗

arXiv2023

Writing in Water

Writing is an ancient communication technique dating back at least 30,000 years. While even sophisticated contemporary writing techniques hinge on solid surfaces for engraving or the deposition of ink, writing within a liquid medium requires a fundamentally different approach. We here demonstrate writing of lines, letters, and complex patterns in water by assembling lines of colloidal particles. Unlike established techniques for underwater writing on solid substrates, these lines are fully reconfigurable and do not require any fixation onto the substrate. Exploiting gravity, we roll an ion-exchange bead (pen) across a layer of sedimented colloidal particles (ink). The pen evokes a hydrodynamic flow collecting ink-particles into a durable, high-contrast line along its trajectory. Deliberate substrate-tilting sequences facilitate pen-steering and thus to draw and write. We complement our experiments with a minimal model that quantitatively predicts the observed parameter dependence for writing in fluids and highlights the generic character of writing by line-assembly. Overall, our approach opens a versatile route for writing, drawing, and patterning fluids - even at the micro-scale.

cond-mat.soft↗

arXiv2023

Collective behavior of active molecules: dynamic clusters, holes and active fractalytes

Recent experiments have led to active colloidal molecules which aggregate from non-motile building blocks and acquire self-propulsion through their non-reciprocal interactions. Here, we model the collective behavior of such active molecules and predict, besides dynamic clusters, the existence of a so-far unknown state of active matter made of 'active fractalytes' which are motile crystallites featuring internal holes, gaps and a fractal dimension. These structures could serve as a starting point for the creation of active materials with a low density and mechanical properties that can be designed through their fractal internal structure.

cond-mat.soft↗

arXiv2023

Charging of dielectric surfaces in contact with aqueous electrolyte -- the influence of CO$_2$

The charge state of dielectric surfaces in aqueous environments is of fundamental and technological importance. Here, we study the influence of dissolved molecular CO$_2$ on the charging of three, chemically different surfaces (SiO$_2$, Polystyrene, Perfluorooctadecyltrichlorosilane). We determine their charge state from electrokinetic experiments. We compare an ideal, CO$_2$-free reference system to a system equilibrated against ambient CO$_2$ conditions. In the reference system, the salt-dependence is weakened for SiO$_2$ and inverted for the organic surfaces. We show that screening and pH-driven charge regulation alone cannot explain the observed effects. As additional cause, we tentatively suggest dielectric regulation of surface charges due to a diffusively adsorbed thin layer of molecular CO$_2$. The formation of such a dynamic layer even at the hydrophilic and partially ionized silica surfaces is supported by a minimal theoretical model and results from molecular simulations.

cond-mat.soft↗

arXiv2023

Optimal active particle navigation meets machine learning

The question of how "smart" active agents, like insects, microorganisms, or future colloidal robots need to steer to optimally reach or discover a target, such as an odor source, food, or a cancer cell in a complex environment has recently attracted great interest. Here, we provide an overview of recent developments, regarding such optimal navigation problems, from the micro- to the macroscale, and give a perspective by discussing some of the challenges which are ahead of us. Besides exemplifying an elementary approach to optimal navigation problems, the article focuses on works utilizing machine learning-based methods. Such learning-based approaches can uncover highly efficient navigation strategies even for problems that involve e.g. chaotic, high-dimensional, or unknown environments and are hardly solvable based on conventional analytical or simulation methods.

cond-mat.soft↗

arXiv2022

Inverted Sedimentation of Active Particles in Unbiased ac Fields

Gaining control over the motion of active particles is crucial for applications ranging from targeted cargo delivery to nanomedicine. While much progress has been made recently to control active motion based on external forces, flows or gradients in concentration or light intensity, which all have a well-defined direction or bias, little is known about how to steer active particles in situations where no permanent bias can be realized. Here, we show that ac fields with a vanishing time average provide an alternative route to steering active particles. We exemplify this route for inertial active particles in a gravitational field, observing that a substantial fraction of them persistently travels in the upward direction upon switching on the ac field, resulting in an inverted sedimentation profile at the top wall of a confining container. Our results offer a generic control principle which could be used in the future to steer active motion, to direct collective behaviors and to purify mixtures.

cond-mat.soft↗

arXiv2022

Active Refrigerators Powered by Inertia

We present the operational principle for a refrigerator which uses inertial effects in active Brownian particles to locally reduce their (kinetic) temperature by two orders of magnitude below the environmental temperature. This principle exploits the peculiar but so-far unknown shape of the phase diagram of inertial active Brownian particles to initiate motility-induced phase separation in the targeted cooling regime only. Remarkably, active refrigerators operate without requiring isolating walls opening the route towards using them to systematically absorb and trap, e.g., toxic substances from the environment.

cond-mat.soft↗

arXiv2022

Resonantly-driven nanopores can serve as nanopumps

Inducing transport in electrolyte-filled nanopores with dc fields has led to influential applications ranging from nanosensors to DNA sequencing. Here we use the Poisson-Nernst-Planck and Navier-Stokes equations to show that unbiased ac fields can induce comparable directional flows in gated conical nanopores. This flow exclusively occurs at intermediate driving frequencies and hinges on the resonance of two competing timescales, representing space charge development at the ends and in the interior of the pore. We summarize the physics of resonant nanopumping in an analytical model that reproduces the results of numerical simulations. Our findings provide a generic route towards real-time controllable flow patterns, which might find applications in controlling the translocation of particles such as small molecules or nanocolloids.

cond-mat.soft↗

arXiv2022

Chiral Active Matter

Chiral active matter comprises particles which can self-propel and self-rotate. Examples range from sperm cells and bacteria near walls to asymmetric colloids and pea-shaped Quincke rollers. In this perspective article we focus on recent developments in chiral active matter. After briefly discussing chiral active motion at a single particle level, we discuss collective phenomena ranging from vortex arrays and patterns made of rotating micro-flocks to states featuring unusual rheological properties.

cond-mat.soft↗

arXiv2022

Hydrodynamics can Determine the Optimal Route for Microswimmer Navigation

Contrasting the well explored problem on how to steer a macroscopic agent like an airplane or a moon lander to optimally reach a target, "optimal microswimming", i.e. the quest for the optimal navigation strategy for microswimmers, remains unsolved. Here, we systematically explore this problem and show that the characteristic flow field of microswimmers crucially influences the required navigation strategy to reach a target fastest. The resulting optimal trajectories can have remarkable and non-intuitive shapes, which qualitatively differ from those of dry active particles or motile macroagents. Our results provide generic insights into the role of hydrodynamics and fluctuations on optimal navigation at the microscale and suggest that microorganisms might have survival advantages when strategically controlling their distance to remote walls. In particular, when fluctuations are present, choosing the optimal strategy, which appropriately respects hydrdynamics, can halve the time to reach the target compared to cases microswimmers head straight towards it.

physics.flu-dyn↗

arXiv2022

Mutation induced infection waves in diseases like COVID-19

After more than 6 million deaths worldwide, the ongoing vaccination to conquer the COVID-19 disease is now competing with the emergence of increasingly contagious mutations, repeatedly supplanting earlier strains. Following the near-absence of historical examples of the long-time evolution of infectious diseases under similar circumstances, models are crucial to exemplify possible scenarios. Accordingly, in the present work we systematically generalize the popular susceptible-infected-recovered model to account for mutations leading to repeatedly occurring new strains, which we coarse grain based on tools from statistical mechanics to derive a model predicting the most likely outcomes. The model predicts that mutations can induce a super-exponential growth of infection numbers at early times, which self-amplify to giant infection waves which are caused by a positive feedback loop between infection numbers and mutations and lead to a simultaneous infection of the majority of the population. At later stage -- if vaccination progresses too slowly -- mutations can interrupt an ongoing decrease of infection numbers and can cause infection revivals which occur as single waves or even as whole wave trains featuring alternative periods of decreasing and increasing infection numbers. This panorama of possible mutation-induced scenarios should be tested in more detailed models to explore their concrete significance for specific infectious diseases. Further, our results might be useful for discussions regarding the importance of a release of vaccine-patents to reduce the risk of mutation-induced infection revivals but also to coordinate the release of measures following a downwards trend of infection numbers.

q-bio.PE↗

arXiv2022

Reinforcement learning of optimal active particle navigation

The development of self-propelled particles at the micro- and the nanoscale has sparked a huge potential for future applications in active matter physics, microsurgery, and targeted drug delivery. However, while the latter applications provoke the quest on how to optimally navigate towards a target, such as e.g. a cancer cell, there is still no simple way known to determine the optimal route in sufficiently complex environments. Here we develop a machine learning-based approach that allows us, for the first time, to determine the asymptotically optimal path of a self-propelled agent which can freely steer in complex environments. Our method hinges on policy gradient-based deep reinforcement learning techniques and, crucially, does not require any reward shaping or heuristics. The presented method provides a powerful alternative to current analytical methods to calculate optimal trajectories and opens a route towards a universal path planner for future intelligent active particles.

cond-mat.soft↗

arXiv2021

Collective self-optimization of communicating active particles

The quest on how to collectively self-organize in order to maximize the survival chances of the members of a social group requires finding an optimal compromise between maximizing the well-being of an individual and that of the group. Here we develop a minimal model describing active individuals which consume or produce, and respond to a shared resource, such as the oxygen concentration for aerotactic bacteria or the temperature field for penguins, while urging for an optimal resource value. Notably, this model can be approximated by an attraction-repulsion model, but in general it features many-body interactions. While the former prevents some individuals from closely approaching the optimal value of the shared resource field, the collective many-body interactions induce aperiodic patterns, allowing the group to collectively self-optimize. Arguably, the proposed optimal-field-based collective interactions represent a generic concept at the interface of active matter physics, collective behavior, and microbiological chemotaxis. This concept might serve as a useful ingredient to optimize ensembles of synthetic active agents or to help unveiling aspects of the communication rules which certain social groups use to maximize their survival chances.

cond-mat.soft↗