Department of Chemistry, Faculty of Science, Imam Khomeini International University, Qazvin, Iran.
Department of Chemistry, Faculty of Science, Imam Khomeini International University, Qazvin, Iran.
Spectrochim Acta A Mol Biomol Spectrosc. 2021 Jul 5;255:119697. doi: 10.1016/j.saa.2021.119697. Epub 2021 Mar 17.
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).
在当前的研究中，提出了一种使用衰减全反射傅里叶变换红外（ATR-FTIR）光谱和化学计量学方法定量测定阴离子表面活性剂溶液在亲水性二氧化硅纳米颗粒存在下的表面张力的分析方法。表面活性剂溶液的表面张力行为通过添加具有不同粒径的二氧化硅纳米颗粒而发生很大变化。使用两种基于支持向量机回归（SVM-R）作为非线性算法和偏最小二乘回归（PLS-R）作为线性算法的校准方法，利用溶液的光谱数据预测表面张力。对于数据的预处理，还应用了基线校正和标准正态变量（SNV）。在 SVM-R 和 PLS-R 方法中，预测均方根误差（RMSEP）分别为 4.203 和 4.507。考虑到样品的复杂性，发现 SVM-R 模型是可靠的。该方法快速简便，无需进行任何样品制备步骤，可用于测量化学强化采油（C-EOR）中表面活性剂溶液的表面张力。
