Coarse-grain molecular dynamics simulation framework to unravel the interactions of surfactants on silica surfaces for oil recovery

resumo

A coarse-grained molecular dynamics (CG-MD) framework, based on the MARTINI 3.0 model, was developed to characterise the interactions between surfactants and oil-silica substrates to resemble chemical enhanced oil recovery (EOR) processes. Previous computational studies, at the atomistic scale, addressed interactions between surfactants and oil over diverse surfaces. Even though simulations provided significant information involved throughout different stages of oil extraction from surfaces, atomistic scale simulations fail when approaching the time and size scale required to address the surfactant phase behaviour that can also impact the oil detachment. Our coarse-grained model aims to overcome the lack of computer approaches that can tackle the surfactant self assembly and the formation of ordered structures in the removal of oil from silica substrates. A new MARTINI 3.0 coarse-grain framework to model silica surfaces and aqueous solutions of CiEj and C16TAB surfactants is presented. Coarse-grained simulations entailing a silica surface, covered by dodecane or eicosane were brought in contact with aqueous solutions of C16TAB and four nonionic CiEj (C8E6, C8E12, C12E6, C16E12) surfactants to resemble EOR processes with a size/time scale several orders of magnitude larger than previous simulations. The impact of concentration and hydrophilic-lipophilic balance (HLB) of surfactants on the detachment of dodecane and eicosane from the silica surface was evaluated by visual inspection of the simulation snapshots and the evolution of the solvent accessible surface areas (SASA). In contrast with previous atomistic simulations, nonionic surfactants seem the best candidates for an optimal oil removal from silica-based surfaces whereas the presence of charged moieties hinders the process. Diluted nonionic CE aqueous solutions were shown to be the most effective solutions, unlike more concentrated ones. When compared with dodecane, eicosane was less prone to be removed from the silica surface due to the increased alkyl chain length. Our study demonstrates that not only the surfactant nature but also the phase behaviour, clearly impact the detachment of oil from silica surfaces. This is an important aspect to consider for a proper choice of surfactants in EOR processes, that is only attainable through a coarse-grained framework.

palavras-chave

NONIONIC SURFACTANT; PHASE-BEHAVIOR; POLYETHYLENE-GLYCOL; TEMPLATED SYNTHESIS; MESOPOROUS SILICA; FORCE-FIELD; WATER; MICELLE; EQUILIBRIA; MODEL

categoria

Chemistry

autores

Perez-Sánchez, G; Costa, FM; Silva, GMC; Piñeiro, MM; Coutinho, JAP

Grupos

G4 - Materiais Renováveis e Economia Circular
G6 - Materiais Virtuais e Inteligência Artificial

Projectos

CICECO - Aveiro Institute of Materials (UIDB/50011/2020)

CICECO - Aveiro Institute of Materials (UIDP/50011/2020)

Collaboratory for Emerging Technologies, CoLab (EMERGING TECHNOLOGIES)

Associated Laboratory CICECO-Aveiro Institute of Materials (LA/P/0006/2020)

agradecimentos

This work was developed within the scope of the project CICECO-Aveiro Institute of Materials, UIDB/50011/2020, UIDP/50011/2020 & amp; LA/P/0006/2020, financed by national funds through the FCT/MEC (PIDDAC) . G. PerezSanchez acknowledges the national funds (OE) , through FCT - Fundacao para a Ciencia e a Tecnologia, I.P., in the scope of the framework contract foreseen in the numbers 4, 5 and 6 of the article 23, of the Decree-Law 57/2016, of August 29th, changed by Law 57/2017, of July 19th. Simulations were performed at the OBLIVIONSupercomputer managed by the HPC Centre of the University of Evora and acquired under the "Enabling Green E-science for the SKA Research Infrastructure (ENGAGE SKA) ", reference POCI-01-0145-FEDER-022217, funded by COMPETE 2020 and Foundation for Science and Technology (FCT) , Portugal. The simulations were funded by the FCT program "Concurso de Projetos de Computacao Avancada-2 Edicao" with the project reference CPCA/A1/394985/2021.r Supercomputer managed by the HPC Centre of the University of 'Evora and acquired under the "Enabling Green E-science for the SKA Research Infrastructure (ENGAGE SKA) ", reference POCI-01-0145-FEDER-022217, funded by COMPETE 2020 and Foundation for Science and Technology (FCT) , Portugal. The simulations were funded by the FCT program "Concurso de Projetos de Computacao Avancada-2 Edicao" with the project reference CPCA/A1/394985/2021.