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Information × Registration Number 0225U001482, (0120U100202) , R & D reports Title Statistical theory of complex liquids: influence of spatial restriction and anisotropy, mesoscopic ordering and anomalous effects popup.stage_title Статистична теорія складних рідин: вплив просторового обмеження та анізотропії, мезоскопічне впорядкування та аномальні ефекти Head Holovko Myroslav F., Доктор фізико-математичних наукTokarchuk Mykhailo V., Доктор фізико-математичних наук Registration Date 01-02-2025 Organization Institute of Condensed Matter Physics of the National Academy of Sciences of Ukraine popup.description1 Development of methods of theoretical description and investigation of the effects of complex formation, self-association and phase behavior of aqueous solutions of proteins, colloidal systems with particles with heterogeneously functionalized surface and polymer molecules in a porous medium. popup.description2 The approaches of associative liquid theory are developed to describe solutions of ionic fluids, taking into account the non-spherical shape of particles and the effect of a disordered porous medium on phase transitions is investigated. The nematic ordering of dimerized spherocylindrical particles and the effect of the polydispersity of chain molecules in a porous matrix are studied. The method of Ornstein-Zernike integral equations is generalized for heterogeneously functionalized colloids with orientation-dependent interactions. A microscopic field theory is developed for systems with competing interactions under confinement. Systems of colloidal nanocrystals based on elliptical particles are studied in detail using computer simulations. A statistical approach is proposed to study non-equilibrium ion transport processes in a porous medium, and a theory is developed to describe the anomalous diffusion of cholesterol in blood vessels. Functionalized carbon nanotubes are investigated as nanocarriers using molecular dynamics simulations, and the aggregation phenomena of curcumin molecules in a mixture of aqueous and organic solvents are also explored. Machine learning approaches are developed to search for new chemical compounds, which are tested for the selection of protein inhibitors, representing significant fundamental and applied interest.  Product Description popup.authors Hvozd Taras V. Druchok Maksym Yu. Kaliuzhnyi Yurii V. Kravtsiv Ivan Ya. Melnyk Roman S. Patsahan Taras M. Trokhymchuk Andrii D. popup.nrat_date 2025-02-01 Close