Effect of Textural Properties and Surface Chemical Nature of Silica Nanoparticles from Different Silicon Sources on the Viscosity Reduction of Heavy Crude Oil

ACS Omega. 2020 Mar 3;5(10):5085-5097. doi: 10.1021/acsomega.9b04041. eCollection 2020 Mar 17.

Abstract

The main objective of this study is to evaluate the effect of the textural properties and surface chemical nature of silica nanoparticles obtained from different synthesis routes and silicon precursors, on their interactions with asphaltenes and further viscosity reduction of heavy crude oil (HO). Four different SiO2 nanoparticles were used, namely, commercial fumed silica nanoparticles (CSNs) and three in-house-synthesized nanoparticles (named based on the silicon source) modifying the silicon precursor: sodium silicate (SNSS), tetraethylorthosilicate (TEOS) (SNT), and rice husk (SNRH). The nanomaterials were characterized through dynamic light scattering (DLS), transmission electron microscopy (TEM), Fourier transform infrared (FTIR) spectroscopy, N2 physisorption (S BET), atomic force microscopy (AFM), and X-ray photoelectron (XP) spectroscopy (XPS). The adsorption of asphaltenes over the different nanoparticles was evaluated at a concentration of 1000 mg·L-1 in toluene. The asphaltene-nanoparticle interactions are closely related to several textural properties, such as roughness, surface area, and hydrodynamic diameter, as well as the surface chemical nature of the materials. The results in the textural characterization exhibited that the sizes of the nanoparticles from TEM ranged between 6.9 and 11.5 nm. Nevertheless, the standard deviation of the measurements showed that the sizes are statistically similar. Inversely, the hydrodynamic diameter changed, affecting the surface silanol group's availability due to a hindering effect on functional groups as the hydrodynamic size of the material increased. The rheological measurements were performed at a fixed nanoparticle dosage of 1000 mg·L-1 and showed that the trend of the degree of viscosity reduction (DVR) was CSN > SNT > SNSS > SNRH with the highest value yielding at 30%. The results of DVR are in accordance with the nanoparticles' adsorptive capacity as higher values were obtained with the material that leads to a higher amount of adsorbed asphaltenes. Also, the oxygen amount related to silanol groups, estimated by the XPS analysis, showed a direct relation regarding adsorption capacity and further HO viscosity reduction.