The Use of Neural Network Modeling Methods to Determine Regional Threshold Values of Hydrochemical Indicators in the Environmental Monitoring System of Waterbodies.

The regulation of the anthropogenic load on waterbodies is carried out based on water quality standards that are determined using the threshold values of hydrochemical indicators. These applied standards should be defined both geographically and differentially, taking into account the regional specifics of the formation of surface water compositions. However, there is currently no unified approach to defining these regional standards. It is, therefore. appropriate to develop regional water quality standards utilizing modern technologies for the mathematical purpose of methods analysis using both experimental data sources and information system technologies. As suggested by the use of sets of chemical analysis and neural network cluster analysis, both methods of analysis and an expert assessment could identify surface water types as well as define the official regional threshold values of hydrochemical system indicators, to improve the adequacy of assessments and ensure the mathematical justification of developed standards. The process for testing the proposed approach was carried out, using the surface water resource objects in the territory of the Republic of Tatarstan as our example, in addition to using the results of long-term systematic measurements of informative hydrochemical indicators. In the first stage, typing was performed on surface waters using the neural network clustering method. Clustering was performed based on sets of determined hydrochemical parameters in Kohonen's self-organizing neural network. To assess the uniformity of data, groups in each of the selected clusters were represented by specialists in this subject area's region. To determine the regional threshold values of hydrochemical indicators, statistical data for the corresponding clusters were calculated, and the ranges of these values were used. The results of testing this proposed approach allowed us to recommend it for identifying surface water types, as well as to define the threshold values of hydrochemical indicators in the territory of any region with different surface water compositions.

A Composite Membrane Based on Sulfonated Polystyrene Implanted in a Stretched PTFE Film for Vanadium Flow Batteries.

The quality of ion-selective membranes determines the efficiency of Vanadium Flow Batteries (VFBs), and alternatives to expensive Nafion™ materials are actively being searched for. One of the membrane architecture approaches is to imitate the Nafion™ structure with two separate phases: a conductive sulfonated polymer and an inner matrix. We introduce a new composite material based on sulfonated styrene polymerized inside the pores of a stretched PTFE matrix. Variation of polystyrene content and a sulfonation degree allowed to obtain membranes with IEC from to 0.96 to 1.84 mmol/g. Balanced vanadium permeability (ca. 5.5 ⋅ 10-6  cm2 /min) and proton conductivity (ca. 50 mS/cm) were achieved for the material with 21-23 % polystyrene content and a sulfonation degree up to 94 %. Membranes showed stable cycling with 81 % energy efficiency in a single-cell VFB. This work contributes to the existing knowledge of Nafion alternatives by providing a cheap and scalable method of membrane production.