SearcharxivSearch

arXiv subjects

Kay-Robert Dormann

Publications and source records attributed to Kay-Robert Dormann.

3 recordsLinked to original sources

Quantifying surfactant adsorption at fluid interfaces by combining X-ray reflectivity and simulations

Adsorption of surfactants to fluid interfaces occurs in numerous daily-life and technological contexts. The surfactant surface coverage $\Gamma$ governs interface characteristics like tension $\gamma$, viscoelastic properties, and the stability of thin foam films. Directly measuring $\Gamma$ as a function of the bulk concentration $c$ is highly desirable but challenging, particularly for non-ionic surfactants that lack easily detectable labels. Neutron reflectometry is currently the only generally applicable method, but it is not available for routine experiments. Here, we propose a simulation-assisted approach to deduce the adsorption isotherm $\Gamma(c)$ from X-ray reflectivity data: As a first step, we use atomistic molecular dynamics simulations of surfactant-loaded air/water interfaces with prespecified $\Gamma$ to obtain interfacial electron density profiles. From these profiles, we compute theoretical X-ray reflectivity curves and compare them with experimental measurements to determine the matching bulk concentration. We focus on two non-ionic surfactants (C$_{12}$EO$_6$ and $\beta$-C$_{12}$G$_2$}) with previously established force fields to illustrate how this combined approach of experiments and simulations can determine the adsorption isotherm. Additional insights are gained through comparison with the measured surface tension isotherms $\gamma(c)$, based on the equation of state $\gamma(\Gamma)$ from simulations.

cond-mat.soft

A general model for frictional contacts in colloidal systems

In simulations of colloidal matter, frictional contacts between particles are often neglected. For spherical colloids, such an approximation can be problematic, since frictional contacts couple translational and rotational degrees of freedom, which may affect the collective behavior of, e.g., colloids under shear and chiral active matter. Deterministic models for frictional contacts have been proposed in the granular matter community. On the colloidal scale, however, thermal fluctuations are important and should be included in a thermodynamically consistent manner. Here, we derive the correct fluctuation-dissipation relation for linear and nonlinear instantaneous frictional contact interactions. Among other, this generates a new generalized class of dissipative particle dynamics (DPD) thermostats with rotation-translation coupling. We demonstrate effects of frictional contact interactions using the examples of Poiseuille flow and motility induced phase separation in active Langevin particles.

cond-mat.soft

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