Physicochemical properties of KOH-H<Subscript>2</Subscript>O<Subscript>2</Subscript>-H<Subscript>2</Subscript>O solutions

Density, viscosity, and surface tension of KOH-H₂O₂-H₂O solutions used to synthesize potassium superoxide in sprayer apparatus were studied with various relative amounts of the components in the temperature range 0–30°C. Analytical dependences of the above-mentioned quantities on temperature and solvent (water) content of the system were found.     

Fruits of two seabuckthorn varieties

Fruits of two varieties of Hippophae rhamnoides L. collected in Kyrgyzstan (I) and Uzbekistan (II) were investigated. Differences in their morphological and biochemical properties were demonstrated. Titrable acids, ascorbic acid, and protein dominated in the fruits of I. Pulp oil of II contained more free fatty acids (acid number 2.9 mg KOH) and carotinoids (419.3 mg%). The principal pulp acid was 16:1. Among the rarely encountered acids of pulp oil, an insignificant quantity of 16:2 was detected (1.2–4.6%). Seed oil of the studied specimens contained >40% of the essential acids 18:2 and 18:3 (vitamin F).Translated from Khimiya Prirodnykh Soedinenii, No. 6, pp. 435–437, November–December, 2004.This revised version was published online in April 2005 with a corrected cover date.     

Validated TLC-densitometry method for the simultaneous analysis of pyrethroid insecticides in agricultural and domestic products

ABSTRACT: BACKGROUND: Pyrethroids are widely used for the control of pests and insects, as pyrethroids are believed to pose very little risk to human health and environment. However, exposure to the pyrethroids exceeding the label directions might adversely affect human health and environment. Hence a careful selection of environment friendly household product is required that must contain exactly the label claimed pyrethroids amount. RESULTS: A sensitive and robust TLC-densitometric method for simultaneous quantification of commonly used synthetic pyrethroids including esbiothrin, alpha-cypermethrin and cis/trans permethrin in agricultural and domestic products has been developed and validated. TLC aluminum plates, precoated with 0.2 mm thick layer of silica gel 60 F-254, were used for chromatographic process. Densitometric analysis of chromatoplates was carried out in absorbance mode at corresponding lamdamax of each pyrethroid. Equally valid common mobile phase for all pyrethroids consisted of hexane-dichloromethane-ethylacetate-formic acid (8:1.5:0.4:0.1 v/v/v/v) which provided sharp and symmetrical peaks of esbiothrin, alpha-cypermethrin, trans-permethrin and cis-permethrin, at Rf 0.31, 0.53, 0.6 and 0.65, respectively. Linear regression data for respective calibration curves showed a good linearity for all pyrethroids with r = 0.991-0.996. Limits of detection (LOD) and limits of quantification (LOQ) for all pyrethroids were found in the range of 1.6-2.8 and 4.9-8.5 ng/spot, respectively. CONCLUSIONS: The developed method is applicable for separating the mixture of pyrethroids and at the same time, it is also valid for separating their isomers. The method is reproducible, precise and accurate for the quantitative determination of pyrethroids in agricultural and domestic products.     

Physicochemical Grounds for Application of Schungite for Neutralization of 1,1-Dimethylhydrazine in Wastewater

Chromatography–mass spectrometry and MALDI mass spectrometry have been used to study the interaction of 1,1-dimethylhydrazine and products of its oxidation with the surface of mineral schungite in aqueous solutions. A comparative analysis has been performed for organic compounds contained in initial and equilibrium 1,1-dimethylhydrazine solutions brought in contact with schungite, as well as compounds desorbed from the modified surfaces of the mineral and its inorganic components. The analysis has revealed the efficiency of schungite as a low-cost and environmentally friendly sorbent capable of catalyzing the profound oxidation of 1,1-dimethylhydrazine and products of its transformation to nontoxic low-molecularmass substances. Environmentally safe methods have been proposed for the regeneration of used schungite.     

Artificial neural network‐based modeling of snow properties using field data and hyperspectral imagery

This study attempts to model snow wetness and snow density of Himalayan snow cover using a combination of Hyperspectral image processing and Artificial Neural Network (ANN). Initially, a total of 300 spectral signature measurements, synchronized with snow wetness and snow density, were collected in the field. The spectral reflectance of snow was then modeled as a function of snow properties using ANN. Four snow wetness and three snow density models were developed. A strong correlation was observed in near‐infrared and shortwave‐infrared region. The correlation analysis of ANN modeled snow density and snow wetness showed a strong linear relationship with field‐based data values ranging from 0.87–0.90 and 0.88–0.91, respectively. Our results indicate that an Artificial Intelligence (AI) approach, using a combination of Hyperspectral image processing and ANN, can be efficiently used to predict snow properties (wetness and density) in the Himalayan region. Recommendations for resource managers

• Snow properties, such as snow wetness and snow density are mainly investigated through field‐based survey but rugged terrains, difficult weather conditions, and logistics management issues establish remote sensing as an efficient alternative to monitor snow properties, especially in the mountain environment.
• Although Hyperspectral remote sensing is a powerful tool to conduct the quantitative analysis of the physical properties of snow, only a few studies have used hyperspectral data for the estimation of snow density and wetness in the Himalayan region. This could be because of the lack of synchronized snow properties data with field‐based spectral acquisitions.
• In combination with Hyperspectral image processing, Artificial Neural Network (ANN) can be a useful tool for effective snow modeling because of its ability to capture and represent complex input‐output relationships.
• Further research into understanding the applicability of neural networks to determine snow properties is required to obtain results from large snow cover areas of the Himalayan region.

     

Determination of natural aromatic acids using supercritical fluid chromatography

Derivatives of benzoic and cinnamic acids are secondary metabolites of plants, widely distributed in nature and possessing biological activity. Supercritical fluid chromatography (SFC) is shown to provide a high performance separation of nine most important representatives of this class of compounds with selectivity, dramatically different from the reversed-phase high-performance liquid chromatography. The retention and chromatographic separation parameters of analytes for four stationary phases of different nature are compared and the effect of supercritical fluid parameters and the composition of the mobile phase onto the chromatographic separation are analyzed. The optimal separation is found to be achieved when using a silica-based sorbent with 2-ethylpyridinium linked groups. The proposed approach for determination of the above-mentioned compounds is based on the combination of chromatographic separation with multiwavelength spectrophotometric detection and provides the detection limits in the range of 13.0–51.3 μg/L and the analysis duration about 2.5 min. The approach is successfully tested on real objects, such as different kinds of wine.     

Offline estimation of 2D crystal lattice parameters by processing the electron diffraction image

Electron diffraction provides useful information about the internal composition of materials and has been in the use of material scientists for more than fifty years. In order to extract useful information from offline diffraction images, they are manually analyzed by using some photometric technique. Manual analysis is however a cumbersome, laborious and difficult task. To reduce the labors of material scientists one can employ image processing techniques to perform automated analysis, due to the well established popularity and clear evidence of widely used image processing techniques. In this work an image processing technique is being proposed for the extraction of 2D unit cell information from diffraction images on one hand and finding the 2D point group contained by the lattices on the other. The technique employs a morphological shrinking operation to find the center of each spot in the underlying preprocessed diffraction image. This is followed by the extraction of eight points with reference to the spot produced by the transmitted electron beam. The resultant nine points, i.e. the extracted eight plus the reference spot generated by the transmitted electron beam, are then subjected to symmetry operations, rotation symmetry and mirror symmetry, in polar coordinate system, to classify the point group of the lattice produced by the electron diffraction. One of the difficult task, even in manual analysis, is to ascertain the exact spot where the transmitted electron beam hit the sample at the time of realization of the image. This has been accurately and intuitively done by employing the notion that the transmitted spot must have greater number of pixels, with the highest gray value, among the diffracted spots. The proposed strategy has been applied to a sample set of various images and the results shows that the technique is efficient in determining the unit cell in 2D and classify the point group with good accuracy.