Vol 55, No 14 (2019)

An approach to analysis of advanced sensor materials based on cobalt oxide modified with zinc or nickel oxides is developed using X-ray fluorescence analysis (XRF) and inductively coupled plasma mass spectrometry (ICP-MS). It is shown that determination of Ni, Zn, and Co in novel materials based on cobalt oxide using ICP-MS in solutions is possible, the standard deviation being 0.06, 0.06, and 0.05, respectively. The results of the ICP-MS determination of the elements in solutions are used to certify the results obtained by the XRF method without sample preparation. It is shown that Ni_xCo_{3 –x}O_{4 – δ} samples can be correctly analyzed without decomposition using X-ray fluorescence analysis. The results of the determination match theoretically calculated values for the samples obtained both from nitrates and from oxalates of nickel and cobalt. However, calibration based on the ICP-MS results is necessary for X-ray fluorescence analysis of Zn_xCo_{3 –x}O₄ samples. It is shown that the zinc content in the samples exceeds the theoretical determined value by 10–30% because of incomplete precipitation of cobalt from the solution upon synthesis.

Analysis of biomarkers in wastewater is increasingly seen as an important tool for valuation of health, nutrition and use of various substances by humans. Some biomarkers are used for population estimates, since the actual values of population size in cities can differ significantly from official counts, which inevitably leads to errors in assessing the impact of various factors to people calculated per capita. 5-hydroxyindole-3-acetic acid (5-HIAA) is the main metabolite of serotonin, which is excreted by urine and can be used as a biomarker for population estimates. The determination of this metabolite is more preferred than the detection of serotonin itself in terms of correctness of the results. A technique of extraction and subsequent quantitative determination of 5-HIAA in wastewater by high-performance liquid chromatography in combination with tandem mass spectrometric detection was developed. Poroshell Hilic column was used as a stationary phase which has an alternative selectivity compared to traditional C18 columns. Developed procedure is characterized by low detection limits (0.2 μg/L) and good selectivity. The conditions for liquid–liquid extraction of 5-HIAA from wastewater were selected. This technique provides reliable estimation of the concentration of 5-HIAA in wastewater.

A technique for analyzing new corrosion-resistant ruthenium-doped titanium alloys using inductively coupled plasma atomic emission spectrometry (ICP-AES) with microwave sample preparation is reported. The composition of a mixture of acids and the temperature and time parameters of sample preparation of titanium alloys under microwave heating in an autoclave ensuring quantitative conversion of the sample into a convenient analytical form without loss of volatile components for subsequent ICP-AES analysis are substantiated. Optimal conditions for the excitation of the analytical signal have been found, and analytical lines of elements without spectral noise have been selected. Samples of experimental melts of industrial titanium alloys of various classes, volumetrically doped with ruthenium, which are under development and are not yet commercially produced in the Russian Federation (alloy PT-7M + Ru, PT-3B + Ru, 5B + Ru, 37 + Ru, VT-22 + Ru) have been studied. The alloy samples contained the following alloying elements (wt %): Al (1.8–6.3); V (1.0–5.5); Mo (0.7–5.5); Zr (0.2–3.0); Cr (0.5–1.5); Fe (0.5–1.5); Ru (0.05–0.15). The correctness of the determination of alloying elements has been confirmed by analyzing standard samples by varying their mass and, in the case of ruthenium, by the “introduced–found” method. The developed method significantly reduces the time of analysis by combining the multielement ICP-AES method with microwave sample preparation, expands the list of detectable elements in titanium alloys, and improves the precision of the results of analysis.

An original method for wave-exposed sample preparation of commercial stable gel-containing water-in-oil emulsions is developed to separate aqueous and oil phases that are present in the composition for their subsequent analysis. Real samples of commercial stable water-in-oil emulsions differing in composition (different contents of water, gel, iron sulfide, and mechanical impurities) are studied. The effect of the intensity and duration of exposure to waves of different nature on the completeness of phase separation in real samples of commercial emulsions of different composition are studied. The possibility in principal to separate oil and aqueous phases from the composition of stable water-in-oil emulsions stabilized by gel-like associates under exposure to waves (permanent magnetic field, electromagnetic field, and ultrasonic vibrations) is shown. When a water-in-oil emulsion is exposed to a permanent magnetic field with a induction in the range of 0.1–0.57 T for 1–3 min, the degree of isolation of water from the emulsion samples under study varies in the range from 48 to 71%, depending on the composition of the emulsion under study. Similar results are obtained under direct current and alternating current electromagnetic fields with a magnetic induction of 0.1–1.0 T. For complete separation of aqueous and oil phases from gel-containing water-in-oil emulsions, we propose to use ultrasonic treatment combined with the addition of a suspension of aluminum oxide nanopowder in acetonitrile. In this case, the complete destruction of the gel and 100% separation of the aqueous and oil phases are observed.

Amperometric monoamine oxidase biosensors based on screen-printed graphite electrodes modified with nanostructured reduced graphene oxide (RGO) composites and cobalt nanoparticles (CoNPs) were developed to determine antidepressant drug substances (tianeptine, thioridazine, and fluoxetine). Carbon nanomaterials with metal nanoparticles (nanocomposites) allow individual components to retain their properties but also ensure better quality of devices owing to joint contribution of constituents. The nanomodifier was applied to the surface of screen-printed graphite electrodes via dropwise evaporation. The RGO fastening on the surface is due to electrostatic interaction between RGO carboxyl groups and amine groups of the amine derivative on the platform of polyester polyol (H20–NH₂). Cobalt nanoparticles were obtained using the electrochemical chronoamperometry method at a potential E = –1.0 V and different times of their accumulation (50 and 60 s) on the electrode surface. According to atomic force microscopy data, the CoNP size varies with the time of electrochemical deposition of NPs, achieving predominately (40 ± 2) and (78 ± 8) nm. The electrochemical impedance spectroscopy reveals the lowest values of the charge transfer resistance for RGO-chitosan/CoNP nanocomposites and RGO-amine derivative on the polyester polyol (H20–NH₂)/CoNPs platform. The use of these nanocomposites in the electrode surface modification was found to significantly improve the analytical characteristics of biosensors, extending the operating concentration range from 1 × 10^–4 to 5 × 10^–9 mol/L, increasing the sensitivity and correlation coefficients, and decreasing the detectable concentration limit. Biosensors were shown to be promising in the quality control of antidepressants upon the determination of the main active substance in medicinal drugs and biological fluids. The lower limit of detectable concentrations of (7–9) × 10^–10 mol/L is attained by using tyramine as a substrate for the determination of fluoxetine, thioridazine, and tianeptine.

This work investigates in analytical capacities of arc atomic emission analysis (AAEA) in determination of As, Bi, Sb, Cu, and Te in rare earth metals (REM) and their oxides after preliminary group concentration using S,N-containing heterochain polymer sorbent. The investigations have been carried out using a Grand-Extra high-resolution spectrometer developed by VMK-Optoelektronika (Russia). Sorption kinetics and impurity extraction rate as a function of solution acidity have been studied for a selection of conditions of sorption concentration. Aiming at optimization of arc atomic emission detection of As, Bi, Sb, Cu, and Te, various flowcharts of their sorption concentration and subsequent treatment of obtained concentrated product have been considered with addition of a collector at various sorption stages. Powdered graphite has been used in this work as a collector in analysis of rare earth oxides, which is attributed to the relative simplicity of its emission spectrum and versatility. Analytical conditions and spectrometer parameters affecting the analytical signal (sample weight and composition, shape and size of electrodes, current intensity and operating mode of generator, interelectrode distance, wavelengths of analytical lines) have been selected. The evaporation curves of detected impurities have been analyzed, and the exposure time of As, Bi, Sb, Cu, and Te in the obtained sorption concentrate has been determined. Accuracy of the results has been evaluated using reference samples and by comparisons between methods. The results have been used for development of arc chemical atomic emission analysis of yttrium, gadolinium, neodymium, europium, scandium, and their oxides in the concentration range of n × (10^–5–10^–2) wt %.

The literature on the determination of nonmetallic inclusions in metal alloys by the method of atomic emission spectroscopy with single-spark spectra registration is reviewed. The main advantage of this method is its high rapidity (~1 min per measurement), which makes it useful for direct production control. When the spark discharge hits a nonmetallic inclusion, it leads to a high peak (outlier) in the intensity of spectral lines of the elements contained in the inclusion, since the content of these elements in the metal matrix is usually much lower. The intensity distribution of the spectral line of the element obtained from several thousand single-spark spectra consists of two parts: (i) the Gaussian function corresponding to the content of the element in the dissolved form, and (ii) an asymmetric residue in the region of high intensity values due to the inclusions. The quantitative determination of inclusions is based on the assumption that the intensity of the spectral line of an element in the single-spark spectrum is proportional to the content of this element in the mass of the substance ablated by the spark discharge. Thus, according to the calibration curve, which is obtained using the samples with the certified total content of the element, it is possible to determine not only the fractions of the dissolved and undissolved element but also the size of individual inclusions. However, the determination of the sizes is limited to a range of 1 to 20 μm. Furthermore, at present, only Al-containing inclusions can be quantitatively determined. Difficulties arise both with elements that are practically insoluble in steels (O, Ca, Mg, S), and with those whose content in the dissolved form is usually high (Si, Mn). Also, it is still not possible to determine carbide and nitride inclusions in steel using the spectral lines of C and N. The use of time-resolved spectroscopy can reduce the detection limits of the inclusions containing Si and, possibly, Mn. The use of an internal standard in the determination of inclusions also reduces the detection limits, but may distort the results. The use of solid-state linear radiation detectors instead of photomultipliers made it possible to develop a more reliable internal standard based on the background in the neighborhood of the spectral line. Verification of the results of the analysis is difficult owing to the lack of inclusion content reference samples. Further studies can expand the method’s detectable inclusion types list.

Despite the fact that the global market for medicinal plants amounts to hundreds of billions of dollars, there is almost no government control over the quality of such pharmaceuticals in most countries of the world. This is partly attributed to the complex composition of plant materials: traditional analytical methodology is based on the use of standard reference samples for each analyte. In this case, preparations based on medicinal plants may contain tens and hundreds of physiologically active components. Isolation of those compounds in a pure form in practice is carried out using preparative chromatography, which leads to their high cost. Moreover, variation of the chemical composition of medicinal plants depending on the geographical origin of the raw materials interferes with prescribing strict ranges of permissible contents for all physiologically active components. The combination of the above factors limits the possibilities of using traditional approaches to analysis requiring strict standardization, the list of compounds for each type of plant, levels of contents, and the availability of reference materials and standards of comparison. This led to the study of the possibility of introducing various mathematical approaches as an auxiliary methodology. Unlike traditional methodologies, machine learning approaches are based on the correct collection of the data samples. Such a sample should contain groups of samples that correspond to the states of the object which the developed algorithm must distinguish: authentic/fake, pure/containing impurities, effective/not containing a certain level of active components, etc. This review is devoted to consideration of the application of machine learning techniques to the problems of chemical analysis and production control of raw materials of medicinal plants and preparations on their basis for the last 15 years.