New molecular and macroscopic understandings of novel green chemicals based on Xanthan Gum and bio-surfactants for enhanced oil recovery.

This research investigates the interactions between a novel environmentally friendly chemical fluid consisting of Xanthan gum and bio-based surfactants, and crude oil. The surfactants, derived from various leaves using the spray drying technique, were characterized using Fourier-transform infrared (FTIR) spectroscopy, zeta potential analysis, Dynamic light scattering, and evaluation of critical micelle concentration. Static emulsion tests were conducted to explore the emulsification between crude oil and the polymer-surfactant solution. Analysis of the bulk oil FTIR spectra revealed that saturated hydrocarbons and light aromatic hydrocarbons exhibited a higher tendency to adsorb onto the emulsion phase. Furthermore, the increased presence of polar hydrocarbons in emulsion phases generated by polar surfactants confirmed the activation of electrostatic forces in fluid-fluid interactions. Nuclear magnetic resonance spectroscopy showed that the xanthan solution without surfactants had a greater potential to adsorb asphaltenes with highly fused aromatic rings, while the presence of bio-based surfactants reduced the solution's ability to adsorb asphaltenes with larger cores. Microfluidic tests demonstrated that incorporating surfactants derived from Morus nigra and Aloevera leaves into the xanthan solution enhanced oil recovery. While injection of the xanthan solution resulted in a 49.8% recovery rate, the addition of Morus nigra and Aloevera leaf-derived surfactants to the xanthan solution increased oil recovery to 58.1% and 55.8%, respectively.

Evaluation of Interfacial Structure of Self-Assembled Nanoparticle Layers: Use of Standard Deviation between Calculated and Experimental Drop Profiles as a Novel Method.

The self-assembly of nanoparticles (NPs) at interfaces is currently a topic of increasing interest due to numerous applications in food technology, pharmaceuticals, cosmetology, and oil recovery. It is possible to create tunable interfacial structures with desired characteristics using tailored nanoparticles that can be precisely controlled with respect to shape, size, and surface chemistry. To address these functionalities, it is essential to develop techniques to study the properties of the underlying structure. In this work, we propose an experimental approach utilizing the standard deviation of drop profiles calculated by the Laplace equation from experimental drop profiles (STD), as an alternative to the Langmuir trough or precise microscopic methods, to detect the initiation of closely packed conditions and the collapse of the adsorbed layers of CTAB-nanosilica complexes. The experiments consist of dynamic surface/interfacial tension measurements using drop profile analysis tensiometry (PAT) and large-amplitude drop surface area compression/expansion cycles. The results demonstrate significant changes in STD values at the onset of the closely packed state of nanoparticle-surfactant complexes and the monolayer collapse. The STD trend was explained in detail and shown to be a powerful tool for analyzing the adsorption and interfacial structuring of nanoparticles. Different collapse mechanisms were reported for NP monolayers at the liquid/liquid and air/liquid interfaces. We show that the interfacial tension (IFT) is solely dependent on the extent of interfacial coverage by nanoparticles, while the surfactants regulate only the hydrophobicity of the self-assembled complexes. Also, the irreversible adsorption of nanoparticles and the increasing number of adsorbed complexes after the collapse were observed by performing consecutive drop surface compression/expansion cycles. In addition to a qualitative characterization of adsorption layers, the potential of a quantitative calculation of the parameter STD such as the number of adsorbed nanoparticles at the interface and the distance between them at different states of the interfacial layer was discussed.

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).

Genetic algorithm based support vector machine regression for prediction of SARA analysis in crude oil samples using ATR-FTIR spectroscopy.

In the current research, an analytical method was proposed for rapid quantitative determination of saturates, aromatics, resins and asphaltenes (SARA) fractions of crude oil samples. Rapid assessments of SARA analysis of crude oil samples are of substantial value in the oil industry. The conventional SARA analysis procedures were determined with the standards established by the American Society for Testing and Materials (ASTM). However, the standard test methods are time consuming, environmental nonfriendly, expensive, and require large amounts of the crude oil samples to be analyzed. Thus, it be would useful to approve some supportive approaches for rapid evaluation of the crude oils. The attenuated total reflection Fourier-transform infrared spectroscopy ATR-FTIR coupled with chemometric methods could be used as analytical method for crude oil analysis. A hybrid of genetic algorithm (GA) and support vector machine regression (SVM-R) model was applied to predict SARA analysis of crude oil samples from different Iranian oil field using ATR-FTIR spectroscopy. The result of GA-SVM-R model were compared with genetic algorithm-partial least square regression (GA-PLS-R) model. Correlation coefficient (R2) and root mean square error (RMSE) for calibration and prediction of samples were also calculated, in order to evaluate the calibration models for each component of SARA analysis in crude oil samples. The performance of GA-SVM-R is found to be reliably superior, so that it can be successfully applied as an alternative approach for the quantitative determination of the SARA analysis of crude oil samples.

Rapid determination and classification of crude oils by ATR-FTIR spectroscopy and chemometric methods.

Classification based on °API gravity is very important to estimate the parameters related to the extraction, purification, toxicity, and pricing of crude oils. Spectroscopy methods show some advantages over ASTM and API methods for crude oil analysis. The attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy coupled with chemometric methods has been applied as a quick and non-destructive method for crude oil analysis. In this work, a new analytical method using ATR-FTIR spectroscopy associated with chemometric methods were proposed for adressing regression and classification tasks for crude oils analysis based on °API gravity values. The designed methods are rapid, economic, and nondestructive ways in production process of oil industry. The spectral data were used for estimation of °API gravity using two approaches according to PLS-R and SVM-R algorithm, separately. The ATR-FTIR spectral data were also analyzed by classification method using the partial least squares-discriminant analysis (PLS-DA) for crude oil classification. The samples were classified into three classes based on their °API gravity values. The SVM-R model showed better results than PLS-R for °API gravity values using the F-test at 95% of confidence. The result of classification, showed about 100% accuracy and a zero classification error for calibration and prediction samples in PLS-DA algorithm.

Influence of hydrophilic silica nanoparticles on the adsorption layer properties of non-ionic surfactants at water/heptane interface.

There is a notable paucity of studies investigating the impact of charged nanoparticles on the interfacial behavior of nonionic surfactants, assuming that the interactions are negligible in the absence of electrostatic forces. Here, we argue about our observations and the existence of a complex interfacial behavior in such systems depending on the type and chemical structure of surfactant. This study set out to investigate the effects of interactions between hydrophilic silica nanoparticles (NP) and non-ionic surfactants on water/heptane dynamic interfacial properties using drop profile analysis tensiometry (PAT). Three surfactants were studied, namely Triton X-100 (significantly soluble in water phase), C12DMPO (well soluble in both phases) and SPAN 80 (oil-soluble). The different chemical structures and partition coefficients of the surfactants enabled us to cover possible interactions and differentiate between bulk and interfacial interactions. We observed that hydrophilic silica NPs had a negligible effect on the interfacial behavior of Triton X-100, that they increased the surface activity of C12DMPO when both compounds are initially in the aqueous phase. Most interestingly is that the added NPs generated unstable interfacial NP-surfactant complexes and reduced the pseudo-equilibrium interfacial tension of oil-soluble surfactant, Span 80, even though NPs and surfactants were in different bulk phases.

The Role of Electrostatic Repulsion on Increasing Surface Activity of Anionic Surfactants in the Presence of Hydrophilic Silica Nanoparticles.

Hydrophilic silica nanoparticles alone are not surface active. They, however, develop a strong electrostatic interaction with ionic surfactants and consequently affect their surface behavior. We report the interfacial behavior of n-heptane/anionic-surfactant-solutions in the presence of hydrophilic silica nanoparticles. The surfactants are sodium dodecyl sulfate (SDS) and dodecyl benzene sulfonic acid (DBSA), and the diameters of the used particles are 9 and 30 nm. Using experimental tensiometry, we show that nanoparticles retain their non-surface-active nature in the presence of surfactants and the surface activity of surfactant directly increases with the concentration of nanoparticles. This fact was attributed to the electrostatic repulsive interaction between the negatively charged nanoparticles and the anionic surfactant molecules. The role of electrostatic repulsion on increasing surface activity of the surfactant has been discussed. Further investigations have been performed for screening the double layer charge of the nanoparticles in the presence of salt. Moreover, the hydrolysis of SDS molecules in the presence of silica nanoparticles and the interaction of nanoparticles with SDS inherent impurities have been studied. According to our experimental observations, silica nanoparticles alleviate the effects of dodecanol, formed by SDS hydrolysis, on the interfacial properties of SDS solution.