Prediction of surface tension of solution in the presence of hydrophilic silica nanoparticle and anionic surfactant by ATR-FTIR spectroscopy and chemometric methods.

In the current research, an analytical method was proposed for the quantitative determination of surface tension of anionic surfactant solutions in the presence of hydrophilic silica nanoparticles using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy and chemometric methods. The surface tension behavior of anionic surfactant solutions considerably changes by the addition of silica nanoparticles with different particle size. The spectral data of solutions were used for prediction of surface tension using two calibration methods based on support vector machine regression (SVM-R) as a non-linear algorithm and partial least squares regression (PLS-R) as a linear algorithm. For preprocessing of data, baseline correction and standard normal variate (SNV) were also applied. Root mean square error of prediction (RMSEP) in SVM-R and PLS-R methods were 4.203 and 4.507, respectively. Considering the complexity of the samples, the SVM-R model was found to be reliable. The proposed method is fast and easy for measurement of the surface tension of surfactant solutions without any sample preparation step in chemical enhanced oil recovery (C-EOR).