Beiträge zur Kenntnis der Konservierung des Grünfutters

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Electron ionization and methane chemical ionization mass spectrometry study of some 2,2-disubstituted 1,3-dioxolanes, 1,3-dithiolanes, 1,3-oxathiolanes and 1,3-dioxanes. Gas-phase lactone and thiolactone formation

The electron ionization and methane chemical ionization mass spectra of some 2,2-disubstituted 1,3-dioxolanes, 1,3-dithiolanes and 1,3-oxathiolanes were studied. Especially, the effect of the length of the side chain in ring position 2 of these compounds and the ease of formation of possible lactone/thiolactone ion as a fragmentation product were examined. In addition, two 2,2-disubstituted 1,3-dioxanes were studied to see the effect of the bigger ring size. The formation of lactone ions was more favorable under methane chemical ionization than under electron ionization conditions. The structures of fragment ions and the ions generated from model compounds were carefully studied using both high- and low-energy collision-induced dissociation. Also ab initio molecular orbital calculations up to the HF/6–31G** level of theory for protonated 2-methyl-2-propanoic acid ethyl ester of 1,3-dioxolane, 1,3-oxathiolane and 1,3-dithiolane and for two isomeric bicyclic ions were carried out. The theoretical results obtained favor the formation of the lactone ion. Copyright © 2005 John Wiley & Sons, Ltd.     

Experimental study on the efficiency and accuracy of a chance-constrained programming algorithm

The CHAPS algorithm (CHAPS = Chance-Constrained Programming System) has proved to be an efficient and accurate method for solving linear optimization problems which have several random variables distributed normally and independently of each other. The CHAPS algorithm is based on the separation, linearization and iterative adjusting of linearization of chance-constrained deterministic equivalents by using the simplex method.According to test results the solution time of the algorithm is directly proportional to the second power of the number of constraints of a linearized model corresponding to the chance-constrained model. The positive result is partly due to the fact that the linearized model is very sparse. The algorithm requires six to eight CHAPS iteration runs in order to achieve sufficient accuracy in practice (10^?5 –10^?6). The algorithm converges linearly and its asymptotic error constant is $\text{1}\text{4}$.     

An experimental 1H NMR study of the structural deformation of benzene and the dipolar CH splitting of methane in various nematic liquid crystals and in smectic HAB

The structure of benzene dissolved in various thermotropic nematic liquid crystals has been studied. ¹³C-methane was used as an internal “deformation reference”. The results indicate that the HH distance ratios obtained front the direct dipole-dipole coupling constants correspond to the hexagon values in cases where benzene is dissolved in mixtures of nematic liquid crystals which produce negligible dipolar CH splittings for methane. The ratio ^rCH/^r CC for benzene is in good accord with the value obtained by other spectroscopic means. The proton structure of benzene in the smectic and nematic phases of p,p′-di-n-heptylazoxybenzene (HAB), however, does not follow the general trend observed in the purely nematic phases. But the dipolar CH splitting of methane also behaves inconsistently.     

Application of quantitative artificial neural network analysis to 2D NMR spectra of hydrocarbon mixtures

Understanding relationships between the structure and composition of molecular mixtures and their chemical properties is a main industrial aim. One central field of research is oil chemistry where the key question is how the molecular characteristics of composite hydrocarbon mixtures can be associated with the macroscopic properties of the oil products. Apparently these relationships are complex and often nonlinear and therefore call for advanced spectroscopic techniques. An informative and an increasingly used approach is two-dimensional nuclear magnetic resonance (2D NMR) spectroscopy. In the case of composite hydrocarbons the application of 2D NMR methodologies in a quantitative manner pose many technical difficulties, and, in any case, the resulting spectra contain many overlapping resonances that challenge the analytical work. Here, we present a general methodology, based on quantitative artificial neural network (ANN) analysis, to resolve overlapping information in 2D NMR spectra and to simultaneously assess the relative importance of multiple spectral variables on the sample properties. The results in a set of 2D NMR spectra of oil samples illustrate, first, that use of ANN analysis for quantitative purposes is feasible also in 2D and, second, that this methodology offers an intrinsic opportunity to assess the complex and nonlinear relationships between the molecular composition and sample properties. The presented ANN methodology is not limited to the analysis of NMR spectra but can also be applied in a manner similar to other (multidimensional) spectroscopic data.     

Rotation Planar Extraction and Medium-Pressure Solid—Liquid Extraction of Onion (Allium cepa)

JPC – Journal of Planar Chromatography – Modern TLC - Extraction of onion (Allium cepa L.) with 80:20 (v/v) methanol—water in water by rotation planar extraction (RPE) and medium-...