arXiv subjects

Victor Tänzel

Publications and source records attributed to Victor Tänzel.

4 recordsLinked to original sources

arXiv2025

Relevance of Aggregate Anisotropy in Sheared Suspensions of Carbon Black

Carbon Black is a filler frequently used in conductive suspensions or nanocomposites, in which it forms networks supporting electric conductivity. Although Carbon Black aggregates originate from a presumably isotropic aggregation process, the resulting particles are inherently anisotropic. Therefore, they can be expected to interact with shear flow, which significantly influences material properties. In this study, we investigate sheared suspensions of Carbon Black aggregates to elucidate the impact of aggregate anisotropy on the rheological properties. We aim at concentrations below and above the conductivity percolation threshold and comprehensively characterize particle behavior under flow conditions. Aggregates assembled by a diffusion-limited aggregation process are simulated with Langevin dynamics in simple shear flow. The simulations reveal a clear alignment of the aggregates' long axis with the flow direction, an increase in tumbling frequency with higher shear rates, and a shear-thinning response. This behavior closely parallels that of rod-like particles and underlines the significance of the anisotropic nature of Carbon Black aggregates. These findings will facilitate the optimization of nanocomposite precursor processing and the tailoring of Carbon Black-based conductive suspensions.

cond-mat.soft↗

arXiv2024

Flow-induced anisotropy in a carbon black-filled silicone elastomer: electromechanical properties and structure

Carbon black (CB)-elastomers can serve as low-cost, highly deformable sensor materials, but hardly any work exists on their structure-property relationships. We report on flow-induced anisotropy, considering CB-silicone films generated via doctor blade coating. Cured films showed slight electrical anisotropy, with conductivity parallel to the coating direction being lower than perpendicular to it. Furthermore, piezoresistive sensitivity was much larger for stretch perpendicular to the coating direction than for parallel stretch. Structural analysis for length scales up to the CB agglomerate level yielded only weak evidence of anisotropy. Based on this evidence and insight from CB network simulations, we hypothesize that shear flow during coating fragments the CB network and then induces a preferential aggregate alignment, as well as increased inter-particle distances, parallel to the coating direction. As a practical conclusion, already weak anisotropic structuration suffices to cause significant electric anisotropy.

cond-mat.mtrl-sci↗

arXiv2024

Learning protein-ligand unbinding pathways via single-parameter community detection

Understanding the dynamics of biomolecular complexes, e.g., of protein-ligand (un)binding, requires the understanding of paths such systems take between metastable states. In MD simulation data, paths are usually not observable per se, but need to be inferred from simulation trajectories. Here we present a novel approach to cluster trajectories based on a community detection algorithm that requires the definition of only a single free parameter. Using the streptavidin-biotin complex as benchmark system and the A\textsubscript{2a} adenosine receptor in complex with the inhibitor ZM241385 as an elaborate application, we demonstrate how such clusters of trajectories correspond to pathways, and how the approach helps in the identification of reaction coordinates for a considered (un)binding process.

physics.comp-ph↗

arXiv2022

Ligand unbinding pathway and mechanism analysis assisted by machine learning and graph methods

We present two methods to reveal protein-ligand unbinding mechanisms in biased unbinding simulations by clustering trajectories into ensembles representing unbinding paths. The first approach is based on a contact principal component analysis for reducing the dimensionality of the input data, followed by identification of unbinding paths and training a machine learning model for trajectory clustering. The second approach clusters trajectories according to their pairwise mean Euclidean distance employing the neighbor-net algorithm, which takes into account input data bias in the distances set and is superior to dendrogram construction. Finally, we describe a more complex case where the reaction coordinate relevant for path identification is a single intra-ligand hydrogen bond, highlighting the challenges involved in unbinding path reaction coordinate detection.

physics.bio-ph↗