Interactions of Sarin with Polyelectrolyte Membranes: A Molecular Dynamics Simulation Study

Using Molecular Simulations to Understand the Effect of Dodecyl Sulfate on the Calcium-Binding Ability of Polystyrene Sulfonate

We demonstrate the potential to tune the binding of calcium ions with polystyrene sulfonate (PSS) in the presence of dodecyl sulfate (DS). This can aid the design of surfactant-responsive water-softening agents for applications in detergency. We use molecular dynamics simulations to study the effect of the concentration of DS ions and the degree of sulfonation on the propensity of calcium ions toward PSS. We observe that the presence of DS ions increases the propensity of calcium ions toward 100% sulfonated PSS. The above phenomenon is due to the hydrophobic attraction between PSS and DS at low DS concentrations and the formation of calcium ion bridges between sulfonate and sulfate groups at moderate to high DS concentrations. We also observe the formation of calcium ion bridges between the sulfonate groups at high DS concentrations. The presence of DS ions also increases the propensity of calcium ions toward 20% sulfonated PSS. This is mainly due to the hydrophobic attraction between PSS and DS ions. The calcium ion bridges between sulfonate and sulfate groups are less prevalent than those of 100% sulfonated PSS. We do not observe calcium ion bridges between sulfonate groups of 20% sulfonated PSS. We use the above-mentioned insights to suggest potential strategies for designing an anionic polyelectrolyte having a suitable calcium-binding ability at a given concentration of the anionic surfactant. Finally, strong PSS-DS affinity is detrimental to the activity of surfactants because less surfactant ions are available for detergency. Our results also indicate the possibility of altering the PSS-DS affinity by changing the degree of sulfonation.

Ab initio and force field molecular dynamics study of bulk organophosphorus and organochlorine liquid structures

We performed ab initio molecular dynamics (AIMD) simulations to benchmark bulk liquid structures and to evaluate results from all-atom force field molecular dynamics (FFMD) simulations with the generalized Amber force field (GAFF) for organophosphorus (OP) and organochlorine (OC) compounds. Our work also addresses the current and important topic of force field validation, applied here to a set of nonaqueous organic liquids. Our approach differs from standard treatments, which validate force fields based on thermodynamic data. Utilizing radial distribution functions (RDFs), our results show that GAFF reproduces the AIMD-predicted asymmetric liquid structures moderately well for OP compounds that contain bulky alkyl groups. Among the OCs, RDFs obtained from FFMD overlap well with AIMD results, with some offsets in position and peak structuring. However, re-parameterization of GAFF for some OCs is needed to reproduce fully the liquid structures predicted by AIMD. The offsets between AIMD and FFMD peak positions suggest inconsistencies in the developed force fields, but, in general, GAFF is able to capture short-ranged and long-ranged interactions of OPs and OCs observed in AIMD. Along with the local coordination structure, we also compared enthalpies of vaporization. Overall, calculated bulk properties from FFMD compared reasonably well with experimental values, suggesting that small improvements within the FF should focus on parameters that adjust the bulk liquid structures of these compounds.

Improving Biochemical Named Entity Recognition Using PSO Classifier Selection and Bayesian Combination Methods

Named Entity Recognition (NER) is a basic step for large number of consequent text mining tasks in the biochemical domain. Increasing the performance of such recognition systems is of high importance and always poses a challenge. In this study, a new community based decision making system is proposed which aims at increasing the efficiency of NER systems in the chemical/drug name context. Particle Swarm Optimization (PSO) algorithm is chosen as the expert selection strategy along with the Bayesian combination method to merge the outputs of the selected classifiers as well as evaluate the fitness of the selected candidates. The proposed system performs in two steps. The first step focuses on creating various numbers of baseline classifiers for NER with different features sets using the Conditional Random Fields (CRFs). The second step involves the selection and efficient combination of the classifiers using PSO and Bayesisan combination. Two comprehensive corpora from BioCreative events, namely ChemDNER and CEMP, are used for the experiments conducted. Results show that the ensemble of classifiers selected by means of the proposed approach perform better than the single best classifier as well as ensembles formed using other popular selection/combination strategies for both corpora. Furthermore, the proposed method outperforms the best performing system at the Biocreative IV ChemDNER track by achieving an F-score of 87.95 percent.

In Situ Growth and Characterization of Metal Oxide Nanoparticles within Polyelectrolyte Membranes

This study describes a novel approach for the in situ synthesis of metal oxide-polyelectrolyte nanocomposites formed via impregnation of hydrated polyelectrolyte films with binary water/alcohol solutions of metal salts and consecutive reactions that convert metal cations into oxide nanoparticles embedded within the polymer matrix. The method is demonstrated drawing on the example of Nafion membranes and a variety of metal oxides with an emphasis placed on zinc oxide. The in situ formation of nanoparticles is controlled by changing the solvent composition and conditions of synthesis that for the first time allows one to tailor not only the size, but also the nanoparticle shape, giving a preference to growth of a particular crystal facet. The high-resolution TEM, SEM/EDX, UV-vis and XRD studies confirmed the homogeneous distribution of crystalline nanoparticles of circa 4 nm and their aggregates of 10-20 nm. The produced nanocomposite films are flexible, mechanically robust and have a potential to be employed in sensing, optoelectronics and catalysis.