Many animal species organize within groups to achieve advantages compared to being isolated. Such advantages can be found e.g. in collective responses which are less prone to individual failures or noise and thus provide better group performance. Inspired by social animals, here we demonstrate with a swarm of microrobots made from programmable active colloidal particles (APs) that their escape from a hazardous area can originate from a cooperative group formation. As a consequence, the escape efficiency remains almost unchanged even when half of the APs are not responding to the threat. Our results not only confirm that incomplete or missing individual information in robotic swarms can be compensated by other group members but also suggest strategies to increase the responsiveness and fault-tolerance of robotic swarms when performing tasks in complex environments.
Press release at Universität Konstanz website: How animal swarms respond to threats: With the help of microrobots, Konstanz physicists decode how swarms of animals respond effectively to danger [in English]
We discuss the dynamics of a Brownian particle under the influence of a spatially periodic noise strength in one dimension using analytical theory and computer simulations. In the absence of a deterministic force, the Langevin equation can be integrated formally exactly. We determine the short- and long-time behaviour of the mean displacement (MD) and mean-squared displacement (MSD). In particular we find a very slow dynamics for the mean displacement, scaling as t^(-1/2) with time t. Placed under an additional external periodic force near the critical tilt value we compute the stationary current obtained from the corresponding Fokker-Planck equation and identify an essential singularity if the minimum of the noise strength is zero. Finally, in order to further elucidate the effect of the random periodic driving on the diffusion process, we introduce a phase factor in the spatial noise with respect to the external periodic force and identify the value of the phase shift for which the random force exerts its strongest effect on the long-time drift velocity and diffusion coefficient.
The ability of artificial microswimmers to respond to external stimuli and the mechanistical details of their origins belong to the most disputed challenges in interdisciplinary science. Therein, the creation of chemical gradients is technically challenging, because they quickly level out due to diffusion. Inspired by pivotal stopped ow experiments in chemical kinetics, we show that microfluidics gradient generation combined with a pressure feedback loop for precisely controlling the stop of the flows, can enable us to study mechanistical details of chemotaxis of artificial Janus micromotors, based on a catalytic reaction. We find that these copper Janus particles display a chemotactic motion along the concentration gradient in both, positive and negative direction and we demonstrate the mechanical reaction of the particles to unbalanced drag forces, explaining this behaviour.
Tire and road wear particles (TRWP) are non-exhaust particulate matter generated by road transport means during the mechanical abrasion of tires, brakes and roads. TRWP accumulate on the roadsides and are transported into the aquatic ecosystem during stormwater runoffs. Due to their size (sub-millimetric) and rubber content (elastomers), TRWP are considered microplastics (MPs). While the amount of the MPs polluting the water ecosystem with sizes from ∼5 μm to more than 100 μm is known, the fraction of smaller particles is unknown due to the technological gap in the detection and analysis of <5 μm MPs. Here we show that Raman tweezers, a combination of optical tweezers and Raman spectroscopy, can be used to trap and chemically analyze individual TRWPs in a liquid environment, down to the sub-micrometric scale. Using tire particles mechanically grinded from aged car tires in water solutions, we show that it is possible to optically trap individual sub-micron particles, in a so-called 2D trapping configuration, and acquire their Raman spectrum in few tens of seconds. The analysis is then extended to samples collected from a brake test platform, where we highlight the presence of sub-micrometric agglomerates of rubber and brake debris, thanks to the presence of additional spectral features other than carbon. Our results show the potential of Raman tweezers in environmental pollution analysis and highlight the formation of nanosized TRWP during wear.
Microfluidics for Microswimmers: Engineering Novel Swimmers and Constructing Swimming Lanes on the Microscale, a Tutorial Review
Priyanka Sharan, Audrey Nsamela, Sasha Cai Lesher-Pérez and Juliane Simmchen
Small, 2007403 (2021)
Abstract: This paper provides an updated review of recent advances in microfluidics applied to artificial and biohybrid microswimmers. Sharing the common regime of low Reynolds number, the two fields have been brought together to take advantage of the fluid characteristics at the microscale, benefitting microswimmer research multifold. First, microfluidics offer simple and relatively low‐cost devices for high‐fidelity production of microswimmers made of organic and inorganic materials in a variety of shapes and sizes. Microscale confinement and the corresponding fluid properties have demonstrated differential microswimmer behaviors in microchannels or in the presence of various types of physical or chemical stimuli. Custom environments to study these behaviors have been designed in large part with the help of microfluidics. Evaluating microswimmers in increasingly complex lab environments such as microfluidic systems can ensure more effective implementation for in‐field applications. The benefits of microfluidics for the fabrication and evaluation of microswimmers are balanced by the potential use of microswimmers for sample manipulation and processing in microfluidic systems, a large obstacle in diagnostic and other testing platforms. In this review various ways in which these two complementary technology fields will enhance microswimmer development and implementation in various fields are introduced.
Effect of viscosity on microswimmers: a comparative study
Audrey Nsamela, Priyanka Sharan, Aidee Garcia-Zintzun, Sandra Heckel, Purnesh Chattopadhyay, Linlin Wang, Martin Wittmann, Thomas Gemming, James Saenz and Juliane Simmchen
Abstract: Although many biological fluids like blood and mucus exhibit high viscosities, there are still many open questions concerning the swimming behavior of microswimmers in highly viscous media, limiting research to idealized laboratory conditions instead of application‐oriented scenarios. Here, we analyze the effect of viscosity on the swimming speed and motion pattern of four kinds of microswimmers of different sizes which move by contrasting propulsion mechanisms: two biological swimmers (bovine sperm cells and Bacillus subtilis bacteria) which move by different bending patterns of their flagellaand two artificial swimmers with catalytic propulsion mechanisms (alginate microtubes and Janus Pt@SiO 2 spherical microparticles). Experiments consider two different media (glycerol and methylcellulose) with increasing viscosity, but also the impact of surface tension, catalyst activity and diffusion coefficients are discussed and evaluated.
Abstract: We numerically investigate the morphology and disclination line dynamics of active nematic droplets in three dimensions. Although our model incorporates only the simplest possible form of achiral active stress, active nematic droplets display an unprecedented range of complex morphologies. For extensile activity, fingerlike protrusions grow at points where disclination lines intersect the droplet surface. For contractile activity, however, the activity field drives cup-shaped droplet invagination, run-and-tumble motion, or the formation of surface wrinkles. This diversity of behavior is explained in terms of an interplay between active anchoring, active flows, and the dynamics of the motile disclination lines. We discuss our findings in the light of biological processes such as morphogenesis, collective cancer invasion, and the shape control of biomembranes, suggesting that some biological systems may share the same underlying mechanisms as active nematic droplets.
Optical tweezers in a dusty universe P. Polimeno, A. Magazzù, M. A. Iatì, R. Saija, L. Folco, D. Bronte Ciriza, M. G. Donato, A. Foti, P. G. Gucciardi, A. Saidi, C. Cecchi-Pestellini, A. Jimenez Escobar, E. Ammannito, G. Sindoni, I. Bertini, V. Della Corte, L. Inno, A. Ciaravella, A. Rotundi & O. M. Maragò
Eur. Phys. J. Plus 136, 339 (2021)
Abstract: Optical tweezers are powerful tools based on focused laser beams. They are able to trap, manipulate, and investigate a wide range of microscopic and nanoscopic particles in different media, such as liquids, air, and vacuum. Key applications of this contactless technique have been developed in many fields. Despite this progress, optical trapping applications to planetary exploration are still to be developed. Here we describe how optical tweezers can be used to trap and characterize extraterrestrial particulate matter. In particular, we exploit light scattering theory in the T-matrix formalism to calculate radiation pressure and optical trapping properties of a variety of complex particles of astrophysical interest. Our results open perspectives in the investigation of extraterrestrial particles on our planet, in controlled laboratory experiments, aiming for space tweezers applications: optical tweezers used to trap and characterize dust particles in space or on planetary bodies surface.
Improving epidemic testing and containment strategies using machine learning
Laura Natali, Saga Helgadottir, Onofrio M. Maragò, Giovanni Volpe
Machine Learning: Science and Technology (2021)
Containment of epidemic outbreaks entails great societal and economic
costs. Cost-effective containment strategies rely on efficiently
identifying infected individuals, making the best possible use of the
available testing resources. Therefore, quickly identifying the optimal
testing strategy is of critical importance. Here, we demonstrate that
machine learning can be used to identify which individuals are most
beneficial to test, automatically and dynamically adapting the testing
strategy to the characteristics of the disease outbreak. Specifically,
we simulate an outbreak using the archetypal
susceptible-infectious-recovered (SIR) model and we use data about the
first confirmed cases to train a neural network that learns to make
predictions about the rest of the population. Using these prediction, we
manage to contain the outbreak more effectively and more quickly than
with standard approaches. Furthermore, we demonstrate how this method
can be used also when there is a possibility of reinfection (SIRS model)
to efficiently eradicate an endemic disease.
Abstract: We study a Langevin equation describing the stochastic motion of a particle in one dimension with coordinate x, which is simultaneously exposed to a space-dependent friction coefficient γ (x), a confining potential U(x) and non-equilibrium (i.e., active) noise. Specically, we consider frictions γ (x) = γ0 + γ1|x|p and potentials U(x) ∝ |x|p with exponents p = 1; 2 and n = 0; 1; 2. We provide analytical and numerical results for the particle dynamics for short times and the stationary probability density functions (PDFs) for long times. The short-time behaviour displays diffusive and ballistic regimes while the stationary PDFs display unique characteristic features depending on the exponent values (p; n). The PDFs interpolate between Laplacian, Gaussian and bimodal distributions, whereby a change between these different behaviours can be achieved by a tuning of the friction strengths ratio γ0 / γ1. Our model is relevant for molecular motors moving on a one-dimensional track and can also be realized for confined self-propelled colloidal particles.