Research Projects

The project investigates by molecular dynamics simulations the action of common disinfectants such as ethanol or isopropanol on the integrity of the external membrane of the coronavirus.

For further information go to: ccteamcov.blogspot

Disclaimer: We are using this channel instead of peer-reviewed publishing because of speed. We want to make results available to practitioners in time for them to be useful in the current crisis. Therefore, results may be preliminary, not quite converged or inaccurate. Still they might be useful, and they are the best we can do at the time. Use our results with care.

The development of polymer nanocomposites is of great scientific interest and especially carbon nanotubes show promising properties, such as electrical and thermal conductivity, impact strength or viscoelasticity. Material properties strongly depend on the alignment of nanoparticles and polymers. We study entangled polymers in confinement in order to get an insight into the mobility behavior of the polymers. We use Dissipative Partivle Dynamics (DPD) as our coarse-grained simulation method together with a slip-springs model, which has been developed in our group. This model has shown to reproduce entangled dynamics of polymer chains. For soft-core methods, like DPD, polymer dynamics are generally greatly accelerated. The confinement in this work consists of nanotubes, arranged in a hexagonal array. These nanotubes are modeled as excluded volume through a repulsive potential and we analyze structural and dynamic properties of the polymers. Our model yields the potential to be further refined by a more structured nanotube model and a more detailed analysis of the chain movement. Moreover, irregular arrays or the dispersion of nanotubes under a flow of entangled polymers could then be studied in future projects.

For further information please contact Simon Alberti

Over the past few decades, molecular simulation has proven to be a highly successful method for investigating the properties of a diverse range of physical systems. The recent integration of Machine Learning (ML) techniques has greatly enhanced molecular simulation capabilities, enabling us to study systems at much larger length and time scales with improved precision. Despite this, standard molecular dynamics (MD) and Monte Carlo (MC) simulation deal with spherical particles. However, there are numerous contexts where consideration of nonspherical particle in a simulation becomes essential to make any physically meaningful prediction. A few example includes (1) coarse-grained simulation with nonspherical particle to achieve correct dynamics in the system (2) simulating nonspherical granular particles in presence of friction etc. Extending the standard simulation methodologies to the nonspherical cases is non-trivial. To circumvent this problem, nonspherical bodies are typically modeled as a collection of constituent spherical objects. As the number of these constituent spherical objects grows, the simulations become computationally infeasible. Therefore, in this project we propose an alternative data driven approach, which aims to solve this computational bottleneck. The approach is based on a machine learning (ML) model, which predicts the interaction potential between two nonspherical bodies. This machine learned potential is further used in Monte Carlo simulation to predict the structural properties of the system.

For further information please contact: Saientan Bag

Achieving superoleophobic surfaces in air is more challenging than creating superhydrophobic surfaces as oils have a much lower surface tension. However, recent experimental studies have shown that a surface exhibiting superhydrophilicity in air can commonly show superoleophobicity when submerged in water. The underwater Cassie state, which is crucial for realizing underwater superoleophobicity, can be achieved by forming special micro/nano structures on hydrophilic surfaces or grafting hydrophilic polymer brushes onto them. In this project, we investigate the wetting behavior of underwater oil droplets on superhydrophilic surfaces, using many-body dissipative particle dynamics. The main aim of this project is to understand the relationship between the properties of the polymer brush layer and its underwater oil-repellent performance.

For further information please contact: Xinxin Deng

When simulating materials, one often encounters the situation that only in a small part of the material something “interesting” happens, but where the whole of the material needs to be described, as the less interesting regions influence directly or indirectly what is going on in the interesting region. An example is the failure of a polymer composite: When it breaks, complicated rearrangement of the polymer molecules take place near the crack tip. They are interesting from a fundamental and an application viewpoint. Away from the crack tip, the polymer is deformed under the load and this deformation will influence the processes at the crack tip. However, the deformation of the outer regions is not in itself interesting. Another example is the process of dry adhesion. For such cases, an algorithm is being developed by the Müller-Plathe group which allows us to conduct Molecular Dynamics – Finite Elements method (MD-FE) coupled Multi-scale Simulations.

For further information please contact Yash Jain

When a water droplet impacts on a hydrophobic solid surface with a proper velocity, it rebounds back. Recent experiments revealed that adding a small amount of polymer in a water droplet suppresses the rebound without substantial change of its macroscopic rheological properties. We investigate in this project the anti-rebounding behavior of a dilute polymer solution via multi-body dissipative particle dynamics simulation. The rebounding tendency of a droplet is simulated and is interpreted in terms of non-equilibrium dynamics in non-linear response regime. The aim of this project is to establish a concrete relation between rebounding tendency and rheological properties of the polymer solutions.

For further information please contact Eunsang Lee

Different methods and techniques have been developed to predict the change in structural and dynamic properties upon changing the formulation of polymeric material. In recent years, the molecular simulation of materials relies increasingly on coarse‐grained models. These models are simpler than fully atomistic models and allow the simulation of larger systems for longer time scales. Coarse-grained models include the most important degrees of freedom in the computation that are necessary to study the dynamical properties of the system. Because of the reduction in degrees of freedom, the dynamics of the coarse-grained system gets more accelerated as compared to the all-atomistic system or their experimental values. Based on different considerations, there are several different ways that explain the increased molecular mobility upon coarse-graining. In this project, we will choose an entirely different scheme that is based on the “mechanical” consideration is known as the Rough-Mob method. This method is basically an approach to linking the increase of molecular mobility to the loss of molecular surface roughness as we move from an all-atomistic to a coarse-grained model.

For further information contact: Manisha

Coarse-grained simulation models allow simulating large systems or on long time scales, such as simulations of a variety of polymers, surfactants and proteins. However, the dynamics of these models is accelerated compared to the parent atomistic models or experiments. An a-priori estimate of the acceleration would make predictions of realistic dynamical properties, such as diffusion coefficients and viscosities, possible. In this project, a recent and promising method to link the acceleration of dynamics, as one moves from atomistic to a coarse-grained model, to geometrical differences between the two models is under investigation. To this end, the decrease of molecular roughness is calculated by a numerical comparison of the molecular surfaces of both the atomistic and coarse-grained systems.

For further information please contact Melissa Meinel

The static and dynamic wetting behaviour of a droplet on a hydrophobic rough surface is influenced by multiple parameters such as the surface topography, droplet properties, and kinematic parameters. Using Multi-Body Dissipative Particles Dynamics (MDPD), we study the static and dynamic wetting behaviour of a droplet on hydrophobic surfaces structured with square-shaped pillars. We calculate the maximum spreading factor and the dynamic contact angle. Within the framework of the Molecular Kinetic Theory (MKT), we also focus on understanding the dynamic wetting process by obtaining the effective friction coefficient of the contact line and comparing them depending on the selected surface topography and the impact velocity. The goal of the project is to understand the influence of the surface topography and impact velocity on the wetting behaviour of the droplet.

For more information please contact: Dejian Qiu

Dielectric relaxation spectra (DRS) is one of the latest tools to be implemented for exploring molecular structure and dynamics of a system for better understanding of the existing inter/intra level interactions. It enables us to measure the complex permittivity of a specific material or mixture/solution at different temperatures and concentrations. Precise determination of permittivity can provide information like structure of molecules, polarization mechanisms, and as well as making and breaking of hydrogen bonds. As of now there are ample examples of performing DRS experiments to understand the underlying dynamics of Liquid Crystal (LCs) both in the isotropic and in the nematic phase, whereas, studies from the theoretical point of view is still lagging. In Müller-Plathe lab we use all-atom molecular dynamics simulation on a range of liquid crystal forming molecules and predict the dielectric relaxation spectra. Further analyses like mean square displacement hence the diffusion coefficient, velocity autocorrelation, and timescales from hydrogen bond autocorrelation helps us to conclude the origin of the spectral pattern obtained.

For further information please contact: Soham Sarkar

After hydration, some plant seed coats can produce the mucilage envelope. The adhesive property of this mucilage enables the seed to stick to the ground and dispersal over a long distance by sticking the seed to the bodies of animals. The main components of the mucilage are pectin, which is characterized by a strong capacity for hydration and for forming a gel-like envelope after wetting. From the experimental result, increasing the drying time of the mucilage seed decreases the contact angle and increases the adhesive force. This is because losing water makes mucilage becomes harder and stiffer. This project is about developing a coarse-grained model based on the chemical pectin oligosaccharide to reproduce mechanical properties. The effect of desiccation time on the adhesive force of pectin will be examined by multi-scale simulation. In addition, the adhesive property of pectin droplets with different compositions (pure pectin and pectin mixed with cellulose) will also be investigated.

For further information please contact: Kamonthira Wichai