Extractants selection for hyphenated electrothermal atomic absorption method

The theoretical approach for extractants selection during hyphenated atomic absorption determination of highly volatile metals (Pb, Cd), hydride- (Sb, Bi) and carbide-forming (Cr) metals using thermodynamic modelling was proposed. The calculated parameter — maximum pyrolysis temperature — can be used for selection of the extractants and chemical modifiers. Using this approach the antimony extract with Brilliant green was modified for which the 1.8-fold sensitivity and 2.6-fold repeatability decreasing while extract sampling in graphite furnace was observed. A chemical modifier — mixture of hydrazine nitrate with the palladium(II) complex with Xylenol orange — was theoretically selected and experimentally tested, which allowed to enhance the maximum pyrolysis temperature on 200°C, to eliminate extract matrix influence. Using this modifier two-fold increasing in sensitivity and 3-fold decreasing in relative standard deviation was achieved while extraction atomic absorption determination.      

Selective Benzoylation of 1-(β-D-Xylopyranosyl)-3,5-dimethylpyrazole

Abstract

Selective benzoylation of l-(β-d-xylopyranosyl)-3,5-dimethylpyrazole (1) has been performed to give 2,3,4-tri- (2), 3,4-di- (3), 2,4-di- (4), 3- (5) and 4-benzoate (6). The O-acetyl derivatives of compounds 3 and 4 (7, 8), di-O-acetyl of 5 and 6 (9, 10) and O-methanesulphonyl of 3 and 4 (11, 12) have been obtained. The relative reactivity of the hydroxyl groups of 1 was HO-4 ≥ HO-3 ? HO-2. The analysis of ¹ H NMR and ¹³C NMR spectra of 1-12 is presented.     

On solving the maximum clique problem

The Maximum Clique Problem (MCP) is regarded here as the maximization of an indefinite quadratic form over the canonical simplex. For solving MCP an algorithm based upon Global Optimality Conditions (GOC) is applied. Furthermore, each step of the algorithm is analytically investigated and tested. The computational results for the proposed algorithm are compared with other Global Search approaches.     

A Self‐Reporting Tetrazole‐Based Linker for the Biofunctionalization of Gold Nanorods

A photochemical approach based on nitrile imine‐mediated tetrazole‐ene cycloaddition is introduced to functionalize gold nanorods with biomolecules. For this purpose, a bifunctional, photoreactive linker containing thioctic acid as the Au anchoring group and a tetrazole moiety for the light‐induced reaction with maleimide‐capped DNA was prepared. The tetrazole‐based reaction on the nanoparticles’ surface results in a fluorescent pyrazoline product allowing for the spectroscopic monitoring of the reaction. This first example of nitrile imine‐mediated tetrazole‐ene cycloaddition (NITEC)‐mediated biofunctionalization of Au nanorods paves the way for the attachment of sensitive biomolecules, such as antibodies and other proteins, under mild conditions and expands the toolbox for the tailoring of nanomaterials.     

SNARE derived peptide mimic inducing membrane fusion

SNARE proteins mediate membrane fusion between synaptic vesicles and the plasma membrane. A minimized peptide SNARE model system with reduced complexity was introduced combining the native SNARE transmembrane (TMD) and linker domains with artificial coiled-coil forming peptides. Specific membrane fusion initiated by coiled-coil recognition was shown by lipid and content mixing vesicle assays.     

Composition and antimicrobial activity of Achillea distans essential oil

The essential oil of Achillea distans W. et K. flower heads was analyzed by GC and GC-MS. Altogether 43 components in concentrations more than 0.1% were identified representing 93.5% of the oil composition. The main constituents were 1,8-cineole (16.8%), trans-thujone (9.8%), sabinene (8.2%), borneol (7.5%), beta-pinene (6.5%), and camphor (5.8%). The oil showed moderate activity against Staphylococcus aureus and Candida albicans, and weak activity against Salmonella typhimurium, Proteus vulgaris, and Escherichia coli.     

Ionisationskonstanten von 5- und 6-substituierten Benzothiazolonen

In the present work the ionization constants of benzothiazolone and its 5- and 6-substituted derivatives in 20 volume % ethanol are determined spectrophotometrically. The influence of the polar effects of the substituents on the ionization of the benzothiazolone is due mainly to the induction effect, confirmed by the correlation between the experimental pK _a values and theTaft ⁰-constants of the substituents.

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Predicting physical-chemical properties of compounds from molecular structures by recursive neural networks

In this paper, we report on the potential of a recently developed neural network for structures applied to the prediction of physical chemical properties of compounds. The proposed recursive neural network (RecNN) model is able to directly take as input a structured representation of the molecule and to model a direct and adaptive relationship between the molecular structure and target property. Therefore, it combines in a learning system the flexibility and general advantages of a neural network model with the representational power of a structured domain. As a result, a completely new approach to quantitative structure-activity relationship/quantitative structure-property relationship (QSPR/QSAR) analysis is obtained. An original representation of the molecular structures has been developed accounting for both the occurrence of specific atoms/groups and the topological relationships among them. Gibbs free energy of solvation in water, Delta(solv)G degrees , has been chosen as a benchmark for the model. The different approaches proposed in the literature for the prediction of this property have been reconsidered from a general perspective. The advantages of RecNN as a suitable tool for the automatization of fundamental parts of the QSPR/QSAR analysis have been highlighted. The RecNN model has been applied to the analysis of the Delta(solv)G degrees in water of 138 monofunctional acyclic organic compounds and tested on an external data set of 33 compounds. As a result of the statistical analysis, we obtained, for the predictive accuracy estimated on the test set, correlation coefficient R = 0.9985, standard deviation S = 0.68 kJ mol(-1), and mean absolute error MAE = 0.46 kJ mol(-1). The inherent ability of RecNN to abstract chemical knowledge through the adaptive learning process has been investigated by principal components analysis of the internal representations computed by the network. It has been found that the model recognizes the chemical compounds on the basis of a nontrivial combination of their chemical structure and target property.