Computational methods for graphite delamination using anionic surfactants to produce graphene (Revista de Investigaciones Universidad del Quindío, 2019)
Today, using thermal and chemical reduction and solubility, graphene oxide is produced in large scale. Since there are various methods for producing graphene, each of which allocates properties to the produced graphene, the purpose of this research is to investigate graphite delamination using anionic surfactants and produce graphene by means of computational methods. The research method was applied in order to perform molecular dynamics analysis, first, (the minimum) force that each atom imposes to other atoms was calculated. This is the total gradient of the system’s energy according to the coordinates of the related atom. A Bayesian method was used for dynamic modeling, which, on average, uses dynamic parameters instead of their estimates. The Gaussian Process Dynamic Model (GPDM) was completely defined by a set of low-level data representations and was observed by both dynamics and modeling of GP regression (Gaussian process regression). Then, using the Gaussian software, along with empirical results or just using this software, the molecular state and reactions and their mechanisms were simulated. The results indicated that the presence of benzene, ether and carbo xyl groups in the optimal structure facilitates the entry of surfactants into the sheets and that the agent to start the separation of the graphene sheets adhered to each other by comparing the results of this study between the two surfactants, it was found that the gap to change by separation layers between the graphene plates is different for two surfactants. Besides, the difference in the polarity of the surfactants resulted in the final polarization of the surfactant and graphene system. Therefore, the difference in the polarity causes the difference in the solubility.
Keywords: Anionic surfactants; Graphene; Gaussian; Graphite delamination