Universität Stuttgart
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Item Open Access Charge regulation and swelling of weak polyelectrolyte nanogels in divalent salt solutions(2026) Beyer, David; Holm, ChristianWe use computer simulations to investigate the behavior of a weak polyelectrolyte nanogel in a solution containing divalent salt. In our simulations, we systematically vary the bulk pH value and the bulk concentration of divalent salt, allowing us to study the influence of charge regulation and divalent ions on the ionization behavior, ion partitioning, and nanogel swelling. With regard to the ionization behavior, we observe that, with an increasing concentration of divalent salt, the suppression of ionization becomes weaker. Moreover, we find that the strongly non‐uniform ionization profile observed in the absence of divalent counterions becomes increasingly uniform as the concentration of divalent salt is increased. We also study the partitioning of monovalent and divalent counterions between the nanogel and the bulk solution; our analysis shows that the uptake of divalent ions may be enhanced by up to tenfold as compared to the mean‐field prediction. Finally, we consider the influence of divalent ions on the pH‐dependent swelling behavior of the nanogel. Here, we observe a two‐stage swelling driven by charge regulation and ion partitioning. Overall, our results highlight the complex interplay of ionization equilibria, valency effects, and ion partitioning in weak polyelectrolyte systems.Item Open Access Overview: the Janus-nature of molecular CO2 in charge adjustment at wet surfaces(2026) Vogel, Peter; Qaisrani, Muhammad Nawaz; Rasenat, Mattis; Lützenkirchen, Johannes; Sulpizi, Marialore; Beyer, David; Holm, Christian; Palberg, ThomasMolecular CO2 readily dissolves in aqueous electrolyte solutions and partially dissociating to form carbonic acid. The decharging effects of the dissociation products mediated by the ensuing pH-shift and the additional salinity are well established. However, the effects of dissolved molecular CO2 have not been studied systematically. We summarize recent and novel investigations on the role of CO2 regarding charge control at surfaces submersed in aqueous electrolytes. In our electrokinetic and conductometric measurements on representative surfaces, we took special care to control and monitor the electrolyte composition in situ. We discriminate the effects of molecular and dissociated CO2via control experiments using HCl. Depending on the surface under investigation and the charging mechanisms involved, we find that molecular CO2 assists either charging, de-charging and/or recharging. This contrasting charge regulating behaviour reveals the Janus nature of dissolved molecular CO2 with respect to charge control at wet surfaces. In our complementary molecular dynamics simulations, Q4 silica and 9% ionized Q3 silica surfaces are studied as hydrophobic/hydrophilic, respectively charged/uncharged, analogues, as well as uncharged Q3 silica and molecularly rough Isoleucin-coated quartz surfaces. In all cases, we find that the charge-neutral CO2 molecule physisorbs in a thin diffusive layer close to the surface, which leads to pronounced re-structuring of the electric double layer. Based on this result, we suggest to interpret the experimentally observed Janus nature of molecular CO2 in terms of a local decrease of the dielectric permittivity. This in turn leads to a local strengthening of electrostatic interactions dominating the double layer structure next to charged surfaces. Specifically, we propose that CO2 induces a dielectric charge regulation for weakly acidic surface groups, assists the incorporation of OH− into the H-bond network at smooth inert surfaces, and induces significant ion-correlations promoting co-ion binding. Overall, we demonstrate that molecular CO2 allows for a controlled charge-adjustment in opposing directions. We anticipate that our findings on the one hand provide substantial challenges for analytical or numerical modelling as well as for controlled experimental work, but on the other hand bear important practical implications for applications ranging from desalination to bio-membranes.Item Open Access A brief review of capillary number and its use in capillary desaturation curves(2022) Guo, Hu; Song, Kaoping; Hilfer, R.Capillary number, understood as the ratio of viscous force to capillary force, is one of the most important parameters in enhanced oil recovery (EOR). It continues to attract the interest of scientists and engineers, because the nature and quantification of macroscopic capillary forces remain controversial. At least 41 different capillary numbers have been collected here from the literature. The ratio of viscous and capillary force enters crucially into capillary desaturation experiments. Although the ratio is length scale dependent, not all definitions of capillary number depend on length scale, indicating potential inconsistencies between various applications and publications. Recently, new numbers have appeared and the subject continues to be actively discussed. Therefore, a short review seems appropriate and pertinent.Item Open Access MDSuite : comprehensive post-processing tool for particle simulations(2023) Tovey, Samuel; Zills, Fabian; Torres-Herrador, Francisco; Lohrmann, Christoph; Brückner, Marco; Holm, ChristianParticle-Based (PB) simulations, including Molecular Dynamics (MD), provide access to system observables that are not easily available experimentally. However, in most cases, PB data needs to be processed after a simulation to extract these observables. One of the main challenges in post-processing PB simulations is managing the large amounts of data typically generated without incurring memory or computational capacity limitations. In this work, we introduce the post-processing tool: MDSuite. This software, developed in Python, combines state-of-the-art computing technologies such as TensorFlow, with modern data management tools such as HDF5 and SQL for a fast, scalable, and accurate PB data processing engine. This package, built around the principles of FAIR data, provides a memory safe, parallelized, and GPU accelerated environment for the analysis of particle simulations. The software currently offers 17 calculators for the computation of properties including diffusion coefficients, thermal conductivity, viscosity, radial distribution functions, coordination numbers, and more. Further, the object-oriented framework allows for the rapid implementation of new calculators or file-readers for different simulation software. The Python front-end provides a familiar interface for many users in the scientific community and a mild learning curve for the inexperienced. Future developments will include the introduction of more analysis associated with ab-initio methods, colloidal/macroscopic particle methods, and extension to experimental data.Item Open Access Collective variables in data-centric neural network training(2023) Nikolaou, KonstantinNeural Networks have become beneficial tools for physics research. While they provide a powerful tool for data-driven modeling, their success is accompanied by a lack of interpretability. This thesis aims to add transparency to the opaque nature of NNs by means of collective variables, a concept well-known in the field of statistical physics. Three collective variables are introduced that emerge from the interactions between neurons and data. These observables enable one to capture holistic behavior of the network and are used to conduct an analysis of neural network training, focusing on data. Through the investigations, the collective variables are applied to selections from a novel sampling method: Random Network Distillation (RND). Besides studying collective variables, the investigation of Random Network Distillation as a data selection method composes the second part of this thesis. The method is analyzed and optimized with respect to its components, aiming to understand and improve the data selection process. It is shown that RND can be used to select data sets that are beneficial for neural network training, giving rise to its application in fields like active learning. The collective variables are leveraged to further investigate the selection method and its effect on neural network training, revealing previously unknown properties of RND-selected data sets. The potential of the collective variables is demonstrated and discussed from a data-centric perspective. They are shown to be discriminative towards the information content of data and give rise to novel insights into the nature of neural network training. In addition to fundamental research on neural networks, the collective variables offer several potential applications including the identification of adversarial attacks and facilitating neural architecture search.Item Open Access Tuning the properties and microstructuring of ionic liquid mixtures at surfaces through atomistic modeling(2021) Kobayashi, Takeshi; Fyta, Maria (Prof. Dr.)Item Open Access PDADMAC/PSS oligoelectrolyte multilayers : internal structure and hydration properties at early growth stages from atomistic simulations(2020) Sánchez, Pedro A.; Vögele, Martin; Smiatek, Jens; Qiao, Baofu; Sega, Marcello; Holm, ChristianWe analyze the internal structure and hydration properties of poly(diallyl dimethyl ammonium chloride)/poly(styrene sulfonate sodium salt) oligoelectrolyte multilayers at early stages of their layer-by-layer growth process. Our study is based on large-scale molecular dynamics simulations with atomistic resolution that we presented recently [Sánchez et al., Soft Matter 2019, 15, 9437], in which we produced the first four deposition cycles of a multilayer obtained by alternate exposure of a flat silica substrate to aqueous electrolyte solutions of such polymers at 0.1M of NaCl. In contrast to any previous work, here we perform a local structural analysis that allows us to determine the dependence of the multilayer properties on the distance to the substrate. We prove that the large accumulation of water and ions next to the substrate observed in previous overall measurements actually decreases the degree of intrinsic charge compensation, but this remains as the main mechanism within the interface region. We show that the range of influence of the substrate reaches approximately 3 nm, whereas the structure of the outer region is rather independent from the position. This detailed characterization is essential for the development of accurate mesoscale models able to reach length and time scales of technological interest.Item Open Access Water structuring induces nonuniversal hydration repulsion between polar surfaces : quantitative comparison between molecular simulations, theory, and experiments(2024) Schlaich, Alexander; Daldrop, Jan O.; Kowalik, Bartosz; Kanduč, Matej; Schneck, Emanuel; Netz, Roland R.Polar surfaces in water typically repel each other at close separations, even if they are charge-neutral. This so-called hydration repulsion balances the van der Waals attraction and gives rise to a stable nanometric water layer between the polar surfaces. The resulting hydration water layer is crucial for the properties of concentrated suspensions of lipid membranes and hydrophilic particles in biology and technology, but its origin is unclear. It has been suggested that surface-induced molecular water structuring is responsible for the hydration repulsion, but a quantitative proof of this water-structuring hypothesis is missing. To gain an understanding of the mechanism causing hydration repulsion, we perform molecular simulations of different planar polar surfaces in water. Our simulated hydration forces between phospholipid bilayers agree perfectly with experiments, validating the simulation model and methods. For the comparison with theory, it is important to split the simulated total surface interaction force into a direct contribution from surface-surface molecular interactions and an indirect water-mediated contribution. We find the indirect hydration force and the structural water-ordering profiles from the simulations to be in perfect agreement with the predictions from theoretical models that account for the surface-induced water ordering, which strongly supports the water-structuring hypothesis for the hydration force. However, the comparison between the simulations for polar surfaces with different headgroup architectures reveals significantly different decay lengths of the indirect water-mediated hydration-force, which for laterally homogeneous water structuring would imply different bulk-water properties. We conclude that laterally inhomogeneous water ordering, induced by laterally inhomogeneous surface structures, shapes the hydration repulsion between polar surfaces in a decisive manner. Thus, the indirect water-mediated part of the hydration repulsion is caused by surface-induced water structuring but is surface-specific and thus nonuniversal.Item Open Access Insights into Hildebrand solubility parameters : contributions from cohesive energies or electrophilicity densities?(2023) Miranda‐Quintana, Ramón Alain; Chen, Lexin; Smiatek, JensWe introduce certain concepts and expressions from conceptual density functional theory (DFT) to study the properties of the Hildebrand solubility parameter. The original form of the Hildebrand solubility parameter is used to qualitatively estimate solubilities for various apolar and aprotic substances and solvents and is based on the square root of the cohesive energy density. Our results show that a revised expression allows the replacement of cohesive energy densities by electrophilicity densities, which are numerically accessible by simple DFT calculations. As an extension, the reformulated expression provides a deeper interpretation of the main contributions and, in particular, emphasizes the importance of charge transfer mechanisms. All calculated values of the Hildebrand parameters for a large number of common solvents are compared with experimental values and show good agreement for non‐ or moderately polar aprotic solvents in agreement with the original formulation of the Hildebrand solubility parameters. The observed deviations for more polar and protic solvents define robust limits from the original formulation which remain valid. Likewise, we show that the use of machine learning methods leads to only slightly better predictability.Item Open Access Self- and Fick diffusion coefficients in implicit solvent simulations : influence of local aggregation effects and thermodynamic factors(2025) Tovey, Samuel; Holm, Christian; Smiatek, JensIn this article, we discuss the relationship and transition between self- and Fick diffusion coefficients in continuous implicit solvents across different particle densities. By applying the established expressions for self-diffusion and Fick diffusion coefficients in binary solutions, we analyze how the local environment influences diffusion through thermodynamic factors, which can be readily evaluated within the framework of Kirkwood-Buff (KB) theory. These thermodynamic factors, originally defined as derivatives of thermodynamic activity, vary with changes in local particle densities, particularly in the presence of aggregation effects. Consequently, the transition from self- to Fick diffusion coefficients can be understood as a reflection of variations in these thermodynamic factors. Langevin Dynamics simulations at low number densities show excellent agreement with the analytical expressions derived. Overall, our findings provide deeper insight into how local structural environments shape particle dynamics, clarifying the connection between KB theory and the transition from self- to Fick diffusion coefficients.