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We consider sedimented at a solid wall particles that are immersed in water containing small additives of photosensitive ionic surfactants. It is shown that illumination with an appropriate wavelength, a beam intensity profile, shape and size could lead to a variety of dynamic, both unsteady and steady state, configurations of particles. These dynamic, well-controlled and switchable particle patterns at the wall are due to an emerging diffusio-osmotic flow that takes its origin in the adjacent to the wall electrostatic diffuse layer, where the concentration gradients of surfactant are induced by light. The conventional nonporous particles are passive and can move only with already generated flow. However, porous colloids actively participate themselves in the flow generation mechanism at the wall, which also sets their interactions that can be very long ranged. This light-induced diffusio-osmosis opens novel avenues to manipulate colloidal particles and assemble them to various patterns. We show in particular how to create and split optically the confined regions of particles of tunable size and shape, where well-controlled flow-induced forces on the colloids could result in their crystalline packing, formation of dilute lattices of well-separated particles, and other states.
On the effects of disorder on the ability of oscillatory or directional dynamics to synchronize
(2024)
In this thesis I present a collection of publications of my work, containing analytic results and observations in numerical experiments on the effects of various inhomogeneities, on the ability of coupled oscillators to synchronize their collective dynamics. Most of these works are concerned with the effects of Gaussian and non-Gaussian noise acting on the phase of autonomous oscillators (Secs. 2.1-2.4) or on the direction of higher dimensional state vectors (Secs. 2.5,2.6). I obtain exact and approximate solutions to the non-linear equations governing the distributions of phases, or perform linear stability analysis of the uniform distribution to obtain the transition point from a completely disordered state to partial order or more complicated collective behavior. Other inhomogeneities, that can affect synchronization of coupled oscillators, are irregular, chaotic oscillations or a complex, and possibly random structure in the coupling network. In Section 2.9 I present a new method to define the phase- and frequency linear response function for chaotic oscillators. In Sections 2.4, 2.7 and 2.8 I study synchronization in complex networks of coupled oscillators. Each section in Chapter 2 - Manuscripts, is devoted to one research paper and begins with a list of the main results, a description of my contributions to the work and a short account of the scientific context, i.e. the questions and challenges which started the research and the relation of the work to my other research projects. The manuscripts in this thesis are reproductions of the arXiv versions, i.e. preprints under the creative commons licence.
Materials realizing the XY model in two dimensions are sparse.
Here we use neutron triple-axis spectroscopy to investigate the critical static and dynamical magnetic fluctuations in the square-lattice antiferromagnets Ca2RuO4 and Ca3Ru2O7.
We probe the temperature dependence of the antiferromagnetic Bragg intensity, the Q width, the amplitude, and the energy width of the magnetic diffuse scattering in the vicinity of the Neel temperature T-N to determine the critical behavior of the magnetic order parameter M, correlation length xi, susceptibility chi, and the characteristic energy Gamma with the corresponding critical exponents beta, nu, gamma, and z, respectively.
We find that the critical behaviors of the single-layer compound Ca2RuO4 follow universal scaling laws that are compatible with predictions of the two-dimensional (2D) XY model.
The bilayer compound Ca3Ru2O7 is only partly consistent with the 2D XY theory and best described by the three-dimensional (3D) Ising model, which is likely a consequence of the intrabilayer exchange interactions in combination with an orthorhombic single-ion anisotropy.
Hence, our results suggest that layered ruthenates are promising solid-state platforms for research on the 2D XY model and the effects of 3D interactions and additional spin-space anisotropies on the magnetic fluctuations.
In star-forming galaxies, the far-infrared (FIR) and radio-continuum luminosities obey a tight empirical relation over a large range of star-formation rates (SFR).
To understand the physics, we examine magnetohydrodynamic galaxy simulations, which follow the genesis of cosmic ray (CR) protons at supernovae and their advective and anisotropic diffusive transport.
We show that gravitational collapse of the proto-galaxy generates a corrugated accretion shock, which injects turbulence and drives a small-scale magnetic dynamo. As the shock propagates outwards and the associated turbulence decays, the large velocity shear between the supersonically rotating cool disc with respect to the (partially) pressure-supported hot circumgalactic medium excites Kelvin-Helmholtz surface and body modes.
Those interact non-linearly, inject additional turbulence and continuously drive multiple small-scale dynamos, which exponentially amplify weak seed magnetic fields.
After saturation at small scales, they grow in scale to reach equipartition with thermal and CR energies in Milky Way-mass galaxies. In small galaxies, the magnetic energy saturates at the turbulent energy while it fails to reach equipartition with thermal and CR energies.
We solve for steady-state spectra of CR protons, secondary electrons/positrons from hadronic CR-proton interactions with the interstellar medium, and primary shock-accelerated electrons at supernovae.
The radio-synchrotron emission is dominated by primary electrons, irradiates the magnetized disc and bulge of our simulated Milky Way-mass galaxy and weakly traces bubble-shaped magnetically loaded outflows.
Our star-forming and star-bursting galaxies with saturated magnetic fields match the global FIR-radio correlation (FRC) across four orders of magnitude. Its intrinsic scatter arises due to (i) different magnetic saturation levels that result from different seed magnetic fields, (ii) different radio synchrotron luminosities for different specific SFRs at fixed SFR, and (iii) a varying radio intensity with galactic inclination.
In agreement with observations, several 100-pc-sized regions within star-forming galaxies also obey the FRC, while the centres of starbursts substantially exceed the FRC.
In this topical review, we give an overview of the structure and dynamics of a single polymer chain in active baths, Gaussian or non-Gaussian.
The review begins with the discussion of single flexible or semiflexible linear polymer chains subjected to two noises, thermal and active.
The active noise has either Gaussian or non-Gaussian distribution but has a memory, accounting for the persistent motion of the active bath particles. This finite persistence makes the reconfiguration dynamics of the chain slow as compared to the purely thermal case and the chain swells.
The active noise also results superdiffusive or ballistic motion of the tagged monomer. We present all the calculations in details but mainly focus on the analytically exact or almost exact results on the topic, as obtained from our group in recent years.
In addition, we briefly mention important works of other groups and include some of our new results. The review concludes with pointing out the implications of polymer chains in active bath in biologically relevant context and its future directions.
How predictable is the next move of an animal? Specifically, which factors govern the short- and long-term motion patterns and the overall dynamics of land-bound, plant-eating animals in general and ruminants in particular? To answer this question, we here study the movement dynamics of springbok antelopes Antidorcas marsupialis. We propose several complementary statistical-analysis techniques combined with machine-learning approaches to analyze—across multiple time scales—the springbok motion recorded in long-term GPS tracking of collared springboks at a private wildlife reserve in Namibia. As a result, we are able to predict the springbok movement within the next hour with a certainty of about 20%. The remaining about 80% are stochastic in nature and are induced by unaccounted factors in the modeling algorithm and by individual behavioral features of springboks. We find that directedness of motion contributes approximately 17% to this predicted fraction. We find that the measure for directedeness is strongly dependent on the daily cycle of springbok activity. The previously known daily affinity of springboks to their water points, as predicted from our machine-learning algorithm, overall accounts for only about 3% of this predicted deterministic component of springbok motion. Moreover, the resting points are found to affect the motion of springboks at least as much as the formally studied effects of water points. The generality of these statements for the motion patterns and their underlying behavioral reasons for other ruminants can be examined on the basis of our statistical-analysis tools in the future.
We propose a generalization of the widely used fractional Brownian motion (FBM), memory-multi-FBM (MMFBM), to describe viscoelastic or persistent anomalous diffusion with time-dependent memory exponent α(t ) in a changing environment. In MMFBM the built-in, long-range memory is continuously modulated by α(t ). We derive the essential statistical properties of MMFBM such as its response function, mean-squared displacement (MSD), autocovariance function, and Gaussian distribution. In contrast to existing forms of FBM with time-varying memory exponents but a reset memory structure, the instantaneous dynamic of MMFBM is influenced by the process history, e.g., we show that after a steplike change of α(t ) the scaling exponent of the MSD after the α step may be determined by the value of α(t ) before the change. MMFBM is a versatile and useful process for correlated physical systems with nonequilibrium initial conditions in a changing environment.
We present real-world data processing on measured electron time-of-flight data via neural networks. Specifically, the use of disentangled variational autoencoders on data from a diagnostic instrument for online wavelength monitoring at the free electron laser FLASH in Hamburg. Without a-priori knowledge the network is able to find representations of single-shot FEL spectra, which have a low signal-to-noise ratio. This reveals, in a directly human-interpretable way, crucial information about the photon properties. The central photon energy and the intensity as well as very detector-specific features are identified. The network is also capable of data cleaning, i.e. denoising, as well as the removal of artefacts. In the reconstruction, this allows for identification of signatures with very low intensity which are hardly recognisable in the raw data. In this particular case, the network enhances the quality of the diagnostic analysis at FLASH. However, this unsupervised method also has the potential to improve the analysis of other similar types of spectroscopy data.
In recurrence analysis, the tau-recurrence rate encodes the periods of the cycles of the underlying high-dimensional time series. It, thus, plays a similar role to the autocorrelation for scalar time-series in encoding temporal correlations.
However, its Fourier decomposition does not have a clean interpretation. Thus, there is no satisfactory analogue to the power spectrum in recurrence analysis.
We introduce a novel method to decompose the tau-recurrence rate using an over-complete basis of Dirac combs together with sparsity regularization.
We show that this decomposition, the inter-spike spectrum, naturally provides an analogue to the power spectrum for recurrence analysis in the sense that it reveals the dominant periodicities of the underlying time series.
We show that the inter-spike spectrum correctly identifies patterns and transitions in the underlying system in a wide variety of examples and is robust to measurement noise.
Organic thin films are widely used in organic electronics and coatings. Such films often feature film-depth dependent variations of composition and optoelectronic properties. State-of-the-art depth profiling methods such as mass spectroscopy and photoelectron spectroscopy rely on non-intrinsic species (vaporized ions, etching-induced surface defects), which are chemically and functionally different from the original materials. Here we introduce an easily-accessible and generally applicable depth profiling method: film-depth-dependent infrared (FDD-IR) spectroscopy profilometry based on directly measuring the intrinsic material after incremental surface-selective etching by a soft plasma, to study the material variations along the surface-normal direction. This depth profiling uses characteristic vibrational signatures of the involved compounds, and can be used for both conjugated and non-conjugated, neutral and ionic materials. A film-depth resolution of one nanometer is achieved. We demonstrate the application of this method for investigation of device-relevant thin films, including organic field-effect transistors and organic photovoltaic cells, as well as ionized dopant distributions in doped semiconductors.