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A model applying surfactant self-assembly theory and classical thermodynamics has been developed to aid in the prediction of solid surface cleaning by aqueous surfactant solutions. Information gained from a combination of surfactant self-assembly behavior and cleaning system parameters, such as oil species, surfactant type, temperature, alkalinity, and solid surface type has been shown to provide insight into surface cleaning. The model combines minimization of free energy, pertinent component distribution mechanisms, and surfactant self-assembly processes to provide a methodology for the predicting of oil droplet contact angles. Such predictive capabilities will allow for the development of beneficial environmental and economic changes to industrial and commercial surface cleaning and degreasing processes. Results from the model will be compared to experimental data to verify the capability of the theory to account for the effect of solutions parameters on oil droplet behavior. The model, while approximate in nature, has shown a remarkable quantitative predictive ability.
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| http://dx.doi.org/10.1016/j.jcis.2003.08.006 | DOI Listing |
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