Computer Simulations of Polymer Gels : Structure, Dynamics, and Deformation

This thesis presents the results of computer simulation studies of the structure, dynamics, and deformation of cross-linked polymer gels. Obtaining a fundamental understanding of the interrelation between the detailed structure and the properties of polymer gels is a challenge and a key issue towards designing materials for specific purposes. A new off-lattice method for constructing a closed network is presented that is free from defects, such as looping chains and dangling ends. Using these model networks in Brownian dynamics simulations, I show results for the structure and dynamics of bulk gels and describe a novel approach using spherical boundary conditions as an alternative to the periodic boundary conditions commonly used in simulations. This algorithm was also applied for simulating the diffusion of tracer particles within a static and dynamic network, to illustrate the quantitative difference and importance of including network mobility for large particles, as dynamic chains facilitate the escape of particles that become entrapped. I further investigate two technologically relevant properties of polymer gels: their stimuli-responsive behaviour and their mechanical properties. The collapse of core-shell nanogels was studied for a range of parameters, including the cross-linking degree and shell thickness. Two distinct regimes of gel collapse could be observed, with a rapid formation of small clusters followed by a coarsening stage. It is shown that in some cases, a collapsing shell may lead to an inversion of the core-shell particle which exposes the core polymer chains to the environment. This thesis also explores the deformation of bimodal gels consisting of both short and long chains, subject to uniaxial elongation, with the aim to understand the role of both network composition as well as structural heterogeneity on the mechanical response and the reinforcement mechanism of these materials. It is shown that a bimodal molecular weight distribution alone is sufficient to strongly alter the mechanical properties of networks compared to the corresponding unimodal networks with the same number-average chain length. Furthermore, it is shown that heterogeneities in the form of high-density short-chain clusters affect the mechanical properties relative to a homogeneous network, primarily by providing extensibility

Deformation Behavior and Failure of Bimodal Networks

Using computer simulations, we have investigated the deformation and stress–strain behavior of a series of ideal gels without any defects, with a bimodal molecular weight distribution, subject to tensile strains. These networks were prepared with a spatially homogeneous distribution of short and long chains, where all chains are elastically active, without needing to consider possible effects of chain aggregation or entanglements on the physical properties. For all fractions of short chains, the first chains to rupture were the short chains that were initially oriented along the strain axis. The average orientation of the short chains slightly increased with decreasing fraction of short chains. This could be explained by the detailed structure of the network at different compositions. Analysis of the stress–strain relation for the short and long chains showed that the stress was not uniformly shared. Instead, the short chains are more strongly deformed whereas the long chains only make a negligible contribution at smaller strains. The mechanical properties of the bimodal networks at lower fractions of short chains also deviated from the behavior of equivalent unimodal networks with the corresponding average chain length, showing that bimodality alone is sufficient to increase both the maximum extensibility and toughness

Unravelling the mechanism of pH-regulation in dinoflagellate luciferase

Dinoflagellates are the dominant source of bioluminescence in coastal waters. The luminescence reaction involves the oxidation of luciferin by a luciferase enzyme, which only takes place at low pH. The pH-dependence has previously been linked to four conserved histidines. It has been suggested that their protonation might induce a conformational change in the enzyme, thereby allowing substrate access to the binding pocket. Yet, the precise mechanism of luciferase activation has remained elusive. Here, we use computational tools to predict the open structure of the luciferase in Lingulodinium polyedra and to decipher the nature of the opening mechanism. Through accelerated molecular dynamics simulations, we demonstrate that the closed-open conformational change likely takes place via a tilt of the pH-regulatory helix-loop-helix domain. Moreover, we propose that the molecular basis for the transition is electrostatic repulsion between histidine-cation pairs, which destabilizes the closed conformation at low pH. Finally, by simulating truncated mutants, we show that eliminating the C-terminus alters the shape of the active site, effectively inactivating the luciferase

Deformation Behavior of Homogeneous and Heterogeneous Bimodal Networks

In this study, the effect of spatial heterogeneities on the deformation behavior during uniaxial elongation as well as the ultimate properties of bimodal gels consisting of both short and long chains was investigated by molecular simulations. Defect-free networks were created containing dense short-chain clusters and compared with gels having a homogeneous distribution of chains. In both cases, the first chains to rupture were the ones already aligned along the strain axis prior to imposing a strain. The presence of clusters was generally not found to improve the ultimate stress or toughness; the short chains within the clusters were effectively shielded from deformation, even at large fractions of short chains. The heterogeneous network tended to be weaker than the corresponding homogeneous network at a given fraction of short chains, fracturing before any significant deformation of clusters had taken place. The deformation behavior was, however, found to be sensitive to the degree of heterogeneity and the number of intercluster connections. At large fractions of short chains, clustering offered an improvement in the ultimate strain compared to a homogeneous bimodal network and also an equivalent unimodal network with the corresponding number-average chain length, thus providing a small improvement in toughness

Towards a Computational Ecotoxicity Assay

Thousands of anthropogenic chemicals are released into the environment each year, posing potential hazards to human and environmental health. Toxic chemicals may cause a variety of adverse health effects, triggering immediate symptoms or delayed effects over longer periods of time. It is thus crucial to develop methods that can rapidly screen and predict the toxicity of chemicals, to limit the potential harmful impacts of chemical pollutants. Computational methods are being increasingly used in toxicity predictions. Here, the method of molecular docking is assessed for screening potential toxicity of a variety of xenobiotic compounds, including pesticides, pharmaceuticals, pollutants and toxins deriving from the chemical industry. The method predicts the binding energy of the pollutants to a set of carefully selected receptors, under the assumption that toxicity in many cases is related to interference with biochemical pathways. The strength of the applied method lies in its rapid generation of interaction maps between potential toxins and the targeted enzymes, which could quickly yield molecularlevel information and insight into potential perturbation pathways, aiding in the prioritisation of chemicals for further tests. Two scoring functions are compared, Autodock Vina and the machine-learning scoring function RF-Score-VS. The results are promising, though hampered by the accuracy of the scoring functions. The strengths and weaknesses of the docking protocol are discussed, as well as future directions for improving the accuracy for the purpose of toxicity predictions.<br /