Controlling fluid-fluid interfaces by nanostructured materials: a multiscale molecular simulation study

• Grant number: 22/03716-6
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: May 01, 2022
• End date: August 31, 2022
• Field of knowledge: Physical Sciences and Mathematics - Physics - Condensed Matter Physics
• Principal Investigator: Caetano Rodrigues Miranda
• Grantee: Paulo Henrique Ribeiro Amaral
• Host Institution: Instituto de Física (IF). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Associated research grant: 17/02317-2 - Interfaces in materials: electronic, magnetic, structural and transport properties, AP.TEM

Abstract

The central aim of this postdoctoral research proposal is to determine the thermodynamics and kinetics properties of adsorption of nanoparticles within fluid-fluid (oil-brine) interfaces by multiscale computational approaches. Our objective is to understand and control fluid-fluid interfaces by using functionalized magnetic nanoparticles (MNPs). Mainly, wettability and transport phenomena at the nanoscale with and without applied magnetic fields will be addressed, focusing on oil mitigation processes. We will apply advanced computational techniques to determine the thermodynamic, kinetic, and transport properties of core-shell MNPs using multiscale methodologies involving first principles calculations, molecular dynamics, and Lattice Boltzmann methods. We expected to characterize: I) model the functionalized magnetic nanoparticles stability within the oil-brine interface as a function of temperature, pressure, salinity and magnetic field; II) determine the mobility and structuring of molecules in these interfaces with and without MNPs; III) characterize the wettability and interfacial properties according to the functional groups and IV) the effects of both wettability and magnetic field at the nanoscale and microscale. Based on this knowledge, we can search for optimal MNPs for oil mitigation applications. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

AMARAL, PAULO H. R.; TORREZ-BAPTISTA, ALVARO D.; DIONISIO, DAWANY; LOPES, THIAGO; MENEGHINI, JULIO R.; MIRANDA, CAETANO R.. A Machine Learning Model for Adsorption Energies of Chemical Species Applied to CO2 Electroreduction. Journal of the Electrochemical Society, v. 169, n. 11, p. 8-pg., 2022-11-01. (17/02317-2, 14/22130-6, 14/50279-4, 17/15304-6, 20/15230-5, 22/03716-6)