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4/2019
vol. 100 abstract:
RESEARCH PAPERS
Modeling of the phospholipid vesicular nanostructure formation process
Lesia Avdieieva
1
,
Oleksandr Chunikhin
2
BioTechnologia vol. 100 (4) C pp. 387–395 C 2019
Online publish date: 2019/12/25
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This study presents the results of experimental studies and mathematical modeling of the process of vesicular structure formation from phospholipids under the influence of mechanisms of the discrete impulse input of energy (DIIE). The possibility of using this method for increasing the productivity of the process of obtaining vesicles from phospholipids is shown. Moreover, the use of the properties of lipid nanostructures obtained by the DIIE method for the composition of products of special therapeutic nutrition is proposed. The DIIE effect was realized in a flowing rotary-pulsation apparatus of a cylindrical type. The effectiveness of using this type of equipment and the high level of mechanical and physicochemical effects on the dispersed system with phospholipids are established. The results of the studies of the effect of certain regime parameters (such as initial temperature, material concentration, and angular velocity of the rotor) on the analysis of the aqueous suspension of phospholipids on the size distribution of the formed particles are presented. The heat-technological parameters of the process were selected for obtaining particles having an average diameter of up to 500 nm. The possibility of predicting the properties of the formed phospholipids structures obtained by the proposed treatment in a wide range of regime parameters is shown. For the simulation, a hybrid functional Petri net was used, which made it possible to combine the initial thermal technological conditions of the process (such as temperature and material concentration) and the characteristics of the process equipment (such as type of DIIE activator and rotor speed) in the mathematical model.
keywords:
discrete-pulse input of energy, dispersion, lipid vesicular nanostructures, mathematical modeling, hybrid functional Petri nets |