Mass spectra of some 2-methyl-3-(o- and p;-R-phenyl)-3,3a,4,5,6,7-hexahydro-2H-indazoles

The mass spectra of twelve 2-methyl-3-(o- and p-R-phenyl)-3,3a,4,5,6,7-hexahydro-2H-indazoles which have potentially useful pharmacological properties are presented.     

Tandem on-line continuous separation and determination of arsenic by inductively coupled plasma atomic emission spectrometry

The principle of tandem on-line continuous separation techniques as an alternative means of introducing samples into plasmas was applied to the development of a sensitive, selective and convenient method for the determination of arsenic by inductively coupled plasma atomic emission spectrometry (ICP-AES). Arsenic is continuously extracted as AsI₃ into xylene from the sample dissolved in 0.1 M potassium iodide solution in 7.2 M hydrochloric acid. The xylene phase (containing the analyte) is continuously mixed on-line with NaBH₄ in dimethylformamide and acetic acid solutions. Arsine is thus continuously generated directly from the organic phase and is separated in a gas—liquid separation device which prevents most of the xylene phase vapour from reaching the ICP. The system was optimized for the continuous extraction of AsI₃, the direct generation of arsine from xylene and the final ICP determination of arsenic. Finally, the tandem on-line continuous separation ICP detection system was applied to the determination of arsenic in real samples (white metal, cast iron, cupro-nickel and orchard leaves standard materials). Very good agreement between the experimental results and the certified values was obtained.     

P, p, T Surfaces of real gases and the Riemann-Hugoniot catastrophe

Analysing experimentally determined P, ?, T data of N₂, ethylene, propene, CO₂ and Xe by a new mathematical technique, we present evidence that their isothermal pressure-density dependence exhibits behaviour characteristic of a Riemann-Hugoniot catastrophe, in analogy to a van der Waals gas for above and sub critical isotherms.     

Dehydrogenation of cyclohexane over supported Pd catalysts, II. Influence of the support and reduction temperature

We have studied the activity of Pd/sepiolite and Pd/SiO₂–AlPO₄ catalysts in cyclohexane aromatization. The catalysts were characterized by H₂ chemisorption measurements. The influence of support and reduction temperature on the metal surface properties and activity of the catalysts is discussed.     

The loge theory as a starting point for variational calculations. I. General formalism

It is shown that any expectation value of any observable associated with a molecule is the sum of loge contributions and of loge pair contributions. This result provides a rigorous theoretical basis for the study of additive properties of molecules. It is demonstrated that molecular wave functions (exact or approximate) can be expressed as a sum of functions corresponding to the various electronic events. Furthermore any of these event functions can be expressed in terms of correlated loge functions. This expression suggests many kinds of variational procedures of calculating wave functions (known methods and new ones). The case in which noncorrelated completely localized loge functions are used is discussed. If continuous functions are used the variational equation reduces to a sum of independent variational equations, each one corresponding to a particular electronic event. This is not so when discontinuous functions are used or when a delocalized function is added to replace the correlation interloge function. The noncorrelated completely localized loge model is analyzed in more detail. It is seen that local spin operators can be introduced and that each event density operator is the product of the loge density operators. Therefore that model is an independent loge model. The corresponding generalized self-consistent field equations are derived. This treatment helps us to understand how a localized state of a molecule can produce an ion containing a delocalized region, a phenomenon which is sometimes at the origin of some misunderstanding in photoelectron spectroscopy. Finally it is seen how virtual loge functions can be introduced to describe excited states.     

Classification of wheat varieties: use of two-dimensional gel electrophoresis for varieties that can not be classified by matrix assisted laser desorpiton/ionization-time of flight-mass spectrometry and an artificial neural network

Analyzing a gliadin extract by matrix assisted laser desorption/ionization-time of flight-mass spectrometry (MALDI-TOF-MS) combined with an artificial neural network (ANN) is a suitable method for identification of wheat varieties. However, the ANN can not distinguish between all different wheat varieties. Two-dimensional polyacrylamide gel electrophoresis (2-D PAGE) was applied to three pairs of wheat varieties, which can not be classified correctly by ANN. By 2-D PAGE the varieties in the three pairs can be discriminated and these six wheat varieties can be separated from each other, which could not be separated by MALDI-TOF-MS and NN.     

Solubility of Triethylamine in Tetraethylammonium Chloride Aqueous Solutions from 20 to 35°C

Solubilities of triethylamine in aqueous tetraethylammonium chloride solutions were measured at 20, 25, and 35°C. The molalities in Et₄NCl of the aqueous solvents ranged from 0.03 to 1 mol-kg^–1. The data were evaluated from density measurements using a vibrating-tube densimeter. At each temperature, least-squares method was used to fit experimental density data points to double polynomial equations of various degrees. Triethylamine molalities of the saturated aqueous phases were estimated by extrapolation from those equations. Experimental data were interpreted in terms of hydrophobic and electrostatic perturbed domains in the hydration shells of the noneleceory and of the cation of the salt, as a function of temperature and salt concentration. The conclusions obtained are consistent with previous volumetric studies.     

Optimization of artificial neural networks used for retention modelling in ion chromatography

The aim of this work is the development of an artificial neural network model, which can be generalized and used in a variety of applications for retention modelling in ion chromatography. Influences of eluent flow-rate and concentration of eluent anion (OH-) on separation of seven inorganic anions (fluoride, chloride, nitrite, sulfate, bromide, nitrate, and phosphate) were investigated. Parallel prediction of retention times of seven inorganic anions by using one artificial neural network was applied. MATLAB Neural Networks ToolBox was not adequate for application to retention modelling in this particular case. Therefore the authors adopted it for retention modelling by programming in MATLAB metalanguage. The following routines were written; the division of experimental data set on training and test set; selection of data for training and test set; Dixon's outlier test; retraining procedure routine; calculations of relative error. A three-layer feed forward neural network trained with a Levenberg-Marquardt batch error back propagation algorithm has been used to model ion chromatographic retention mechanisms. The advantage of applied batch training methodology is the significant increase in speed of calculation of algorithms in comparison with delta rule training methodology. The technique of experimental data selection for training set was used allowing improvement of artificial neural network prediction power. Experimental design space was divided into 8-32 subspaces depending on number of experimental data points used for training set. The number of hidden layer nodes, the number of iteration steps and the number of experimental data points used for training set were optimized. This study presents the very fast (300 iteration steps) and very accurate (relative error of 0.88%) retention model, obtained by using a small amount of experimental data (16 experimental data points in training set). This indicates that the method of choice for retention modelling in ion chromatography is the artificial neural network.