Molecular Dynamics Simulation of Polymer-Metal Bonds

Molecular simulation is becoming a very powerful tool for studying dynamic phenomena in materials. The simulation yields information about interaction at length and time scales unattainable by experimental measurements and unpredictable by continuum theories. This is especially meaningful when referring to bonding between a polymer and a metal substrate. A very important characteristic of polymers is that their physical properties do not rely on the detailed chemical structure of the molecular chains but only on their flexibility, and accordingly they will be able to adopt different conformations. In this paper, a molecular simulation of the bonding between vinyl ester polymer and steel is presented. Four different polymers with increasing chain lengths have been studied. Atomic co-ordinates are adjusted in order to reduce the molecular energy. Conformational changes in the macromolecules have been followed to obtain the polymer pair correlation function. Radius of gyration and end-to-end distance distributions of the individual chains have been used as a quantitative measurement of their flexibility. There exists a correlation between flexibility of the molecular chains and the energy of adhesion between the polymer and the metal substrate. Close contacts between the two materials are established at certain points but every atom up to a certain distance from the interface contributes to the total value of the adhesion energy of the system

Computational models in plant-pathogen interactions: the case of Phytophthora infestans

<p>Abstract</p> <p>Background</p> <p><it>Phytophthora infestans </it>is a devastating oomycete pathogen of potato production worldwide. This review explores the use of computational models for studying the molecular interactions between <it>P. infestans </it>and one of its hosts, <it>Solanum tuberosum</it>.</p> <p>Modeling and conclusion</p> <p>Deterministic logistics models have been widely used to study pathogenicity mechanisms since the early 1950s, and have focused on processes at higher biological resolution levels. In recent years, owing to the availability of high throughput biological data and computational resources, interest in stochastic modeling of plant-pathogen interactions has grown. Stochastic models better reflect the behavior of biological systems. Most modern approaches to plant pathology modeling require molecular kinetics information. Unfortunately, this information is not available for many plant pathogens, including <it>P. infestans</it>. Boolean formalism has compensated for the lack of kinetics; this is especially the case where comparative genomics, protein-protein interactions and differential gene expression are the most common data resources.</p

Effect of Solvent Topography and Steric Hindrance on Crystal Morphology

Effect of steric hindrance resulting from solvent topography on the resultant crystal morphology was examined via cooling crystallization of various carboxylic acids in isomeric butyl and pentyl alcohols. Our experiments show that the magnitude of hindrance is related to the degree of branching at the substituted carbon, with hindrance increasing in the order of 1° < 2° < 3° alcohols. The resulting crystals displayed a trend of low, intermediate, and high aspect ratios, corresponding to 1°, 2°, and 3° alcohols, respectively. In particular, 3° alcohols have a tendency to yield significantly different crystal morphologies, compared to 1° and 2° alcohols. Hence, the position of the hydroxyl functional group plays a major role in enhancing or limiting solute–solvent hydrogen bonding interactions and thereby influencing the resultant crystal morphology. A simple molecular model, with succinic acid as test case, was used to demonstrate the extended hydrogen bonding network and surface chemistry binding at the dominant {100} face. This molecular-level exploration of solvent–carboxyl hydrogen bonding interaction at the crystal interface helped explain observed macroscopic morphological trends