Feasibility of application of conductometric biosensor based on acetylcholinesterase for the inhibitory analysis of toxic compounds of different nature

This study was aimed at the development of a conductometric biosensor based on acetylcholinesterase considering the feasibility of its application for the inhibitory analysis of various toxicants. In this paper, the optimum conditions for enzyme immobilization on the transducer surface are selected as well as the optimum concentration of substrate for inhibitory analysis. Sensitivity of the developed biosensor to different classes of toxic compounds (organophosphorus pesticides, heavy metal ions, surfactants, aflatoxin, glycoalkaloids) was tested. It is shown that the developed biosensor can be successfully used for the analysis of pesticides and mycotoxins, as well as for determination of total toxicity of the samples. A new method of biosensor analysis of toxic substances of different classes in complex multicomponent aqueous samples is proposed.     

Photoelastic analysis of edge residual stresses in glass by the automated tint plate method

Experimental Techniques - The analysis of residual stress in glass is usually carried out by means of photoelastic methods. This article considers the automation of the white light photoelastic...     

A review on machine learning algorithms for the ionic liquid chemical space

There are thousands of papers published every year investigating the properties and possible applications of ionic liquids. Industrial use of these exceptional fluids requires adequate understanding of their physical properties, in order to create the ionic liquid that will optimally suit the application. Computational property prediction arose from the urgent need to minimise the time and cost that would be required to experimentally test different combinations of ions. This review discusses the use of machine learning algorithms as property prediction tools for ionic liquids (either as standalone methods or in conjunction with molecular dynamics simulations), presents common problems of training datasets and proposes ways that could lead to more accurate and efficient models.

In this review article, the authors discuss the use of machine learning algorithms as tools for the prediction of physical and chemical properties of ionic liquids.     

Sorption of Nitric Oxides from Exhaust Nitrous Gases Using Different Reagents (A Review)

Russian Journal of General Chemistry - The main sources of emissions of industrial nitrogen oxides have been reviewed. A promising method for the absorption of nitrogen monoxide by a reusable...     

Synthesis of Fused Heterocycles Based on 1-Amino-1H-tetrazole-5-thiol and α,β-Unsaturated Aldehydes

Russian Journal of General Chemistry - 8-Phenyltetrazolo[5,1-b][1,3,4]thiadiazepine was obtained as a result of intermolecular cyclization of 1-amino-1H-tetrazole-5-thiol and 3-phenyl-2-propynal....