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Title: Multi-scale modelling of thermodynamic equilibrium of solute partitioning in multiphase complex product formulations
Author: Turchi, Mattia
ISNI:       0000 0004 8503 1825
Awarding Body: University of Surrey
Current Institution: University of Surrey
Date of Award: 2019
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Multiphase complex fluids such as micelles, microemulsions and dispersions are ubiquitous in product formulations of food, pharmaceuticals, cosmetics, and fine chemicals. Quantifying how active solutes partition in the microstructure of such multiphase fluids is necessary for designing formulations that can optimally deliver the benefits of functional actives. In this thesis, in-silico methods are employed for the prediction of solutes partition coefficient in complex microstructures. Recently, the method that combines molecular dynamics (MD) and the Conductor like Screening Model for Micellar systems (COSMOmic) has been reported for modelling complex fluid structures. In this research work, at first the MD/COSMOmic approach is tested for predicting the partition coefficients of neutral solutes in simple octanol/water system and compared with other in-silico methods including EPI-suite, COSMOtherm and UNIFAC. After validating the MD/COSMOmic for the octanol/water system, the approach is further used for predicting the partition coefficient of neutral solutes in sodium dodecyl sulfate (SDS)/heptane/butanol in water microemulsion. The MD/COSMOmic approach is also tested for predicting the partition coefficients of both neutral and charged solutes in mixed micelles of sodium laureth ether sulfate (SLES) and fatty acids. While the combined MD/COSMOmic shows good accuracy in predicting the partition coefficient of neutral solutes, it fails in predicting the partition coefficients of charged solutes. In order to obtain more accurate prediction for the charged solutes, the method that combines steered molecular dynamic (SMD) simulation and umbrella sampling (US) was employed using the latest polarizable force field (CHARMM-Drude). This approach shows to accurately predict the partition coefficients of the charged solutes.
Supervisor: Cai, Qiong ; Lian, Guoping Sponsor: European Union's Horizon 2020 Programme
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral