In this work, the conditions for cloud point extraction preconcentration of Mo(VI) in the form of a complex with perchlorate of 7,8-dihydroxy-2,4-diphenylbenzopyrylium into the micellar phase of the nonionic surfactant Triton X-100 were investigated and optimized. It was established that the addition of sodium salicylate to the system at pH 1.7 and a Triton X-100 concentration of 0.5 vol.% induces phase separation and micellar phase formation at room temperature, which simplifies the extraction procedure and avoids additional heating. Under the selected conditions, effective preconcentration of the analyte and stable spectrophotometric response were achieved. Based on these results, a spectrophotometric procedure was proposed for the determination of Mo(VI) after cloud point extraction. The calibration plot was linear in the concentration range of 8-320 mu g/L. The limits of detection and quantification were 2.4 and 8.0 mu g/L, respectively. The developed method was successfully applied to model solutions and a biologically active supplement, and the relative standard deviation did not exceed 5.0 %.
This study aimed to develop photo-crosslinkable hydrogel compositions based on polyvinyl alcohol (PVA), the photoinitiator benzophenone (BP) and functional acrylic monomers, namely acrylic acid (AA), 2-hydroxyethyl methacrylate (HEMA), methylene-bis-acrylamide (MBA). A gradual increase in the content of gel fraction with UV irradiation time was observed for all composite films, indicating progressive crosslinking. PVA compositions with BP and AA, demonstrated the lowest content and the slowest increase in the gel fraction. The addition of MBA resulted in rapid crosslinking and the highest gel fraction content, whereas HEMA-containing compositions required longer irradiation times to achieve comparable levels of crosslinking. Water absorbability depended on both film composition and gel fraction. Films containing BP, AA, and HEMA exhibited the highest water absorbability. Compositions containing BP, AA, and MBA demonstrated the best water resistance, the highest tensile strength and hardness, and the lowest elongation at break, indicating the formation of highly crosslinked hydrogel networks.
The presented work is dedicated to the study of the possibility of developing a new type of sweet product for restaurants - a multi-component mousse of increased biological value with the preservation of high organoleptic properties of the product. Given that the products of the sweet category with a foamy structure are popular among visitors, this scientific development is relevant and timely. Cherry gum (Ukraine), collagen preparation (Ukraine), grapes, orange, walnut, sugar, cocoa powder were used in the work. The study of the viscosity of cherry gum solutions of different concentrations (1-9 %) in an aqueous medium at different temperatures and duration of exposure was conducted, which indicates the relevance of using gum as a food thickener. The influence of the concentration of cherry gum on the structural characteristics of the food system, namely its foaming ability and stability of the obtained foamed structure and density, was determined. A mathematical model of the formulation of multicomponent mousse was obtained, which relates the content of biologically valuable substances and the degree of satisfaction of the daily human need for proteins, vitamin C and calcium and the mass concentration of its components. The microbiological stability of the developed product during storage was studied. The developed mousse is recommended to be stored within 5 days after production under the conditions of refrigerated storage. The technological scheme for the production of multi-component mousse to ensure production in the conditions of the restaurant industry is presented.
This article analyzes the strategic importance and practical aspects of incorporating artificial intelligence (AI) technologies into the training of chemical engineering students at higher education institutions. The study is based on a systematic scoping review of scientific publications from 2021 to 2025. Based on these sources, the author identifies six major applications of AI in education: modeling and optimizing technological processes with machine learning, using digital twins and augmented reality, incorporating generative AI into project-based learning, applying adaptive learning systems, enabling automated assessment, supporting academic integrity. The article provides empirical evidence illustrating the effectiveness of these approaches. Researchers report that machine learning methods can increase reactor design productivity by up to 60 %, and over 90 % of students positively evaluate the use of generative AI in project-based learning. The analysis emphasizes the role of digital twins in creating highly realistic and safe environments for developing professional skills, including those necessary for hazardous industrial settings. The author also examines how Ukrainian universities adopt AI amid wartime conditions and restricted access to in-person learning. Virtual laboratories and digital simulators help students compensate for limited access to specialized equipment. The article offers practical recommendations for educators, such as expanding computational infrastructure, utilizing digital simulators, and fostering collaboration with industry professionals. The author also shares their personal experience teaching with AI-driven adaptive learning. The conclusion emphasizes that, while AI can modernize chemical engineering education, it cannot replace critical thinking. Since AI-generated content often contains inaccuracies, students and instructors must carefully verify information. To reduce the risk of academic dishonesty, the author recommends increasing the proportion of in-person and oral assessments.
To synthesize a bifunctional catalyst on an Al₂O₃ support, the initially used adsorbent Al2O3 was regenerated at 600 °C for 360 minutes in the absence of atmospheric oxygen. A process model for synthesizing bifunctional catalysts on regenerated Al₂O₃ support was developed using Ni, Co, Mo, and P precursors. According to the elemental composition analysis of the regenerated adsorbent by XRF (X-ray fluorescence) method, it was determined to consist of 81.5 % Al2O3. Using the regenerated Al2O3 as the support, (NH4)6Mo7O24·4H2O, Ni A process model was developed to synthesize a bifunctional catalyst using salts such as Ni(NO3)2·6H2O, Co(NO3)2·6H2O, and H3PO4 acid, and based on this model, three types of catalyst samples were synthesized. To determine the morphological changes and textural properties of the surface of the obtained catalyst samples, analyses were performed using SEM (Scanning Electron Microscope), TEM (Transmission Electron Microscope), and BET (Brunauer-Emmett-Teller) model-based methods. These analyses were applied to evaluate morphological changes and textural characteristics of the prepared catalyst samples.
