arXiv subjects

Frank Cichos

Publications and source records attributed to Frank Cichos.

35 records · Page 2Linked to original sources

arXiv2021

Active-Particle Polarization Without Alignment Forces

Active-particle suspensions exhibit distinct polarization-density patterns in activity landscapes, even without anisotropic particle interactions. Such polarization without alignment forces is at work in motility-induced phase separation and betrays intrinsic microscopic activity to mesoscale observers. Using stable long-term confinement of a single hot microswimmer in a dedicated force-free particle trap, we examine the polarized interfacial layer at a motility step and confirm that it does not exert pressure onto the bulk. Our observations are quantitatively explained by an analytical theory that can also guide the analysis of more complex geometries and many-body effects.

cond-mat.soft↗

arXiv2020

Jarzynski Equality for Soret Equilibria -- Virtues of Virtual Potentials

The Jarzynski equality relates the free energy difference between two equilibrium states to the fluctuating irreversible work afforded to switch between them. The prescribed fixed temperature for the equilibrium states implicitly constrains the dissipative switching process that can take the system far from equilibrium. Here, we demonstrate theoretically and experimentally that such a relation also holds for the nonisothermal case, where the initial stationary state is not in equilibrium and the switching is effected by dynamically changing temperature gradients instead of a conservative force. Our demonstration employs a single colloidal particle trapped by optically induced thermophoretic drift currents. It relies on identifying suitable equivalents of classical work and heat and our ability to measure their distributions and express them in terms of a virtual potential.

cond-mat.stat-mech↗

arXiv2020

Enhanced force-field calibration via machine learning

The influence of microscopic force fields on the motion of Brownian particles plays a fundamental role in a broad range of fields, including soft matter, biophysics, and active matter. Often, the experimental calibration of these force fields relies on the analysis of the trajectories of these Brownian particles. However, such an analysis is not always straightforward, especially if the underlying force fields are non-conservative or time-varying, driving the system out of thermodynamic equilibrium. Here, we introduce a toolbox to calibrate microscopic force fields by analyzing the trajectories of a Brownian particle using machine learning, namely recurrent neural networks. We demonstrate that this machine-learning approach outperforms standard methods when characterizing the force fields generated by harmonic potentials if the available data are limited. More importantly, it provides a tool to calibrate force fields in situations for which there are no standard methods, such as non-conservative and time-varying force fields. In order to make this method readily available for other users, we provide a Python software package named DeepCalib, which can be easily personalized and optimized for specific applications.

physics.comp-ph↗

arXiv2020

Convolutional Neural Networks for Real-Time Localization and Classification in Feedback Digital Microscopy

We present an adapted single-shot convolutional neural network (YOLOv2) for the real-time localization and classification of particles in optical microscopy. As compared to previous works, we focus on the real-time detection capabilities of the system to allow for manipulation of microscopic objects in large heterogeneous ensembles with the help of feedback control. The network is capable of localizing and classifying several hundreds of microscopic objects even at very low signal-to-noise ratios for images as large as 416x416 pixels with an inference time of about 10 ms. We demonstrate the real-time detection performance by manipulating active particles propelled by laser-induced self-thermophoresis. In order to make our framework readily available for others, we provide all scripts and source code. The network is implemented in Python/Keras using the TensorFlow backend. A C library supporting GPUs is provided for the real-time inference.

cond-mat.soft↗

arXiv2019

The 2019 Motile Active Matter Roadmap

Activity and autonomous motion are fundamental in living and engineering systems. This has stimulated the new field of active matter in recent years, which focuses on the physical aspects of propulsion mechanisms, and on motility-induced emergent collective behavior of a larger number of identical agents. The scale of agents ranges from nanomotors and microswimmers, to cells, fish, birds, and people. Inspired by biological microswimmers, various designs of autonomous synthetic nano- and micromachines have been proposed. Such machines provide the basis for multifunctional, highly responsive, intelligent (artificial) active materials, which exhibit emergent behavior and the ability to perform tasks in response to external stimuli. A major challenge for understanding and designing active matter is their inherent nonequilibrium nature due to persistent energy consumption, which invalidates equilibrium concepts such as free energy, detailed balance, and time-reversal symmetry. Unraveling, predicting, and controlling the behavior of active matter is a truly interdisciplinary endeavor at the interface of biology, chemistry, ecology, engineering, mathematics, and physics. The vast complexity of phenomena and mechanisms involved in the self-organization and dynamics of motile active matter comprises a major challenge. Hence, to advance, and eventually reach a comprehensive understanding, this important research area requires a concerted, synergetic approach of the various disciplines.

cond-mat.soft↗

arXiv2018

Hot Microswimmers

Hot microswimmers are self-propelled Brownian particles that exploit local heating for their directed self-thermophoretic motion. We provide a pedagogical overview of the key physical mechanisms underlying this promising new technology. It covers the hydrodynamics of swimming, thermophoresis and -osmosis, hot Brownian motion, force-free steering, and dedicated experimental and simulation tools to analyze hot Brownian swimmers.

cond-mat.soft↗

arXiv2018

Reinforcement Learning of Artificial Microswimmers

The behavior of living systems is based on the experience they gained through their interactions with the environment [1]. This experience is stored in the complex biochemical networks of cells and organisms to provide a relationship between a sensed situation and what to do in this situation [2-4]. An implementation of such processes in artificial systems has been achieved through different machine learning algorithms [5, 6]. However, for microscopic systems such as artificial microswimmers which mimic propulsion as one of the basic functionalities of living systems [7, 8] such adaptive behavior and learning processes have not been implemented so far. Here we introduce machine learning algorithms to the motion of artificial microswimmers with a hybrid approach. We employ self-thermophoretic artificial microswimmers in a real world environment [9, 10] which are controlled by a real-time microscopy system to introduce reinforcement learning [11-13]. We demonstrate the solution of a standard problem of reinforcement learning - the navigation in a grid world. Due to the size of the microswimmer, noise introduced by Brownian motion if found to contribute considerably to both the learning process and the actions within a learned behavior. We extend the learning process to multiple swimmers and sharing of information. Our work represents a first step towards the integration of learning strategies into microsystems and provides a platform for the study of the emergence of adaptive and collective behavior.

cond-mat.soft↗

arXiv2018

Active Particles Bound by Information Flows

Self-organization is the generation of order out of local interactions in non-equilibrium [1]. It is deeply connected to all fields of science from physics, chemistry to biology where functional living structures self-assemble[2] and constantly evolve[3] all based on physical interactions. The emergence of collective animal behavior[4], of society or language are the results of self-organization processes as well though they involve abstract interactions arising from sensory inputs, information processing, storage and feedback[5-7]. Resulting collective behaviors are found for example in crowds of people, flocks of birds, schools of fish or swarms of bacteria[8,9]. Here we introduce such information based interactions to the behavior of active microparticles. A real time feedback of active particle positions controls the propulsion direction these active particles. The emerging structures are bound by dissipation and reveal frustrated geometries due to confinement to two dimensions. They diffuse like passive clusters of colloids, but possess internal dynamical degrees of freedom that are determined by the feed- back and the noise in the system. As the information processing in the feedback loops can be designed almost arbitrarily, new perspectives for self-organization studies involving coupled feedback systems with separate timescales, machine learning and swarm intelligence arise.

cond-mat.soft↗

arXiv2017

Microscopic Engine Powered by Critical Demixing

By converting energy into mechanical work, engines play a central role in most biological and technological processes. In particular, within the current trend towards the development of nanoscience and nanotechnology, microscopic engines have been attracting an ever-increasing interest. On the one hand, there has been a quest to understand how biological molecular motors work. On the other hand, several approaches have been proposed to realize artificial microscopic engines, which have been powered by the transfer of light momentum, by external magnetic fields, by in situ chemical reactions, or by the energy flow between hot and cold heat reservoirs, in scaled-down versions of macroscopic heat engines. Here, we experimentally demonstrate a microscopic engine powered by the local reversible demixing of a critical mixture. We show that, when an absorbing microsphere is optically trapped by a focused laser beam in a sub-critical mixture, it is set into rotation around the optical axis of the beam because of the emergence of diffusiophoretic propulsion; this behavior can be controlled by adjusting the optical power, the temperature, and the criticality of the mixture. Given its simplicity, this microscopic engine provides a powerful tool to power micro- and nanodevices. Furthermore, since many biological systems are tuned near criticality, this mechanism might already be at work within living organisms, for example in proteins and in cellular membranes.

cond-mat.soft↗

arXiv2016

Exact symmetries in the velocity fluctuations of a hot Brownian swimmer

Symmetries constrain dynamics. We test this fundamental physical principle, experimentally and by molecular dynamics simulations, for a hot Janus swimmer operating far from thermal equilibrium. Our results establish scalar and vectorial steady-state fluctuation theorems and a thermodynamic uncertainty relation that link the fluctuating particle current to its entropy production at an effective temperature. A Markovian minimal model elucidates the underlying non-equilbrium physics.

cond-mat.stat-mech↗

arXiv2016

Trapping of Single Nano-Objects in Dynamic Temperature Fields

In this article we explore the dynamics of a Brownian particle in a feedback-free dynamic thermophoretic trap. The trap contains a focused laser beam heating a circular gold structure locally and creating a repulsive thermal potential for a Brownian particle. In order to confine a particle the heating beam is steered along the circumference of the gold structure leading to a non-trivial motion of the particle. We theoretically find a stability condition by switching to a rotating frame, where the laser beam is at rest. Particle trajectories and stable points are calculated as a function of the laser rotations frequency and are experimentally confirmed. Additionally, the effect of Brownian motion is considered. The present study complements the dynamic thermophoretic trapping with a theoretical basis and will enhance the applicability in micro- and nanofluidic devices.

cond-mat.soft↗

arXiv2016

Thermo-osmotic flow in thin films

We report on the first micro-scale observation of the velocity field imposed by a non-uniform heat content along the solid/liquid boundary. We determine both radial and vertical velocity components of this thermo-osmotic flow field by tracking single tracer nanoparticles. The measured flow profiles are compared to an approximate analytical theory and to numerical calculations. From the measured slip velocity we deduce the thermo-osmotic coefficient for both bare glass and Pluronic F-127 covered surfaces. The value for Pluronic F-127 agrees well with Soret data for polyethylene glycol, whereas that for glass differs from literature values and indicates the complex boundary layer thermodynamics of glass-water interfaces.

physics.flu-dyn↗

arXiv2015

The physics of the photothermal detection of single absorbing nano-objects: A review

The literature on the theory of the photothermal imaging of single nano objects is reviewed. Several models have been devised to describe the signals magnitude, phase and shape in dependence on the experimental and sample parameters (particle position and optical properties, laser beams and their offset, modulation frequency, (thermo-)refractive coefficients, thermal constants). The benefits and limitations of these models are summarized allowing the proper choice of a framework for future investigations using photothermal microscopy of single absorbers.

physics.optics↗

arXiv2014

Energy Redistribution Signatures in Transmission Microscopy of Rayleigh- and Mie-particles

The transmission characteristics of a spherical (possibly multilayered) particle of arbitrary size under focused illumination are discussed within the generalized Lorenz-Mie theory. Expressions generalizing the total extinction and scattering cross-sections to their fractional counterparts are presented which allow for a convenient modeling of transmission signals, both on-axis and off-axis. The strong dependence of the signal on the collection angle and the complex polarizability are readily included in this minimal, yet accurate model. The precise signature of the energy redistribution and absorption are found for particles of arbitrary complex-valued polarizability. For perfect dielectrics, a transition from sensitive extinction to insensitive scattering signals is observed and quantified for apertures including angles larger than twice the beam's angle of divergence. Implications for positioning, temperature control, spectroscopy and optimized extinction measurements are discussed.

physics.optics↗

arXiv2012

Photonic Rutherford Scattering: A Classical and Quantum Mechanical Analogy in Ray- and Wave-Optics

Using Fermat's least optical path principle the family of ray-trajectories through a special but common type of a gradient refractive index lens, n(r)=n_0+Δn R/r, is solved analytically. The solution, i.e. the ray-equation r(phi), is shown to be closely related to the famous Rutherford scattering and therefore termed photonic Rutherford scattering. It is shown that not only do these classical limits correspond, but also the wave-mechanical pictures coincide: The time-independent Schrödingier equation and the inhomogeneous Helmholz equation permit the same mapping between massive particle scattering and diffracted optical scalar waves. Scattering of narrow wave-packets finally recovers the classical trajectories. The analysis suggests that photothermal single particle microscopy infact measures photonic Rutherford scattering in specific limits.

physics.optics↗

arXiv2011

Photothermal Single Particle Microscopy

Photothermal microscopy has recently complemented single molecule fluorescence microscopy by the detection of individual nano-objects in absorption. Photothermal techniques gain their superior sensitivity by exploiting a heat induced refractive index change around the absorbing nano-object. Numerous new applications to nanoparticles, nanorods and even single molecules have been reported all refering to the fact that photothermal microscopy is an extinction measurement on a heat induced refractive index profile. Here, we show that the actual physical mechanism generating a photothermal signal from a single molecule/particle is fundamentally different from the assumed extinction measurement. Combining photothermal microscopy, light scattering microscopy as well as accurate Mie scattering calculations to single gold nanoparticles, we reveal that the detection mechanism is quantitatively explained by a nanolensing effect of the long range refractive index profile. Our results lay the foundation for future developments and quantitative applications of single molecule absorption microscopy.

physics.optics↗

arXiv2010

Hot Brownian Motion

We derive the generalized Markovian description for the non-equilibrium Brownian motion of a heated particle in a simple solvent with a temperature-dependent viscosity. Our analytical results for the generalized fluctuation-dissipation and Stokes-Einstein relations compare favorably with measurements of laser-heated gold nano-particles and provide a practical rational basis for emerging photothermal technologies.

cond-mat.stat-mech↗