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.

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.

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.

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).

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.

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).

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.