Rheological-based digital approach for gel curve analysis of alcohol ethoxylates

Abstract

Many surfactants, such as higher mole alcohol ethoxylates like C12-15–7EO and C9-11–8EO, when diluted in water, will form a gel at specific concentrations and temperatures. Gels can be highly viscous and semi-solid and should be avoided since they take time and energy to disperse once formed. Historically, the creation of gel diagrams or maps for our technical product brochures primarily has depended on visual observation, leading to variable interpretations and inconsistent results over time. Also, completing a gel map for one surfactant grade requires a minimum of one day, due to testing many samples across various concentrations and temperatures. To improve objectivity, consistency, and speed in gel mapping, oscillatory rheology was utilized to identify gels using viscoelastic properties by testing samples prepared at various concentrations. The digitization of the gel mapping technique provides two significant benefits. It offers a rheological-based approach giving a non-subjective, digital gel map and it is faster than our visual-based method. Furthermore, this digital method is consistent with our visual-based method giving good discrimination between surfactant grades and reproducibility within batches of the same grade. This work also demonstrates the promising potential of utilizing machine learning algorithms to model the rheological behavior of gel maps effectively. R and Python, programming languages widely used for data analysis, graphing, and machine learning, were employed. Overall, the new digital approach presented yields several benefits for surfactant gel behavior study, including a reduction in subjectivity, faster data generation, and increased efficiency in the gel map analysis process.