A statistical thermodynamic model with strong adaptability for liquid mixtures

How to accurately predict thermodynamic data of highly irregular liquid mixtures, such as Ca-Pb-Sb and Fe-C-Cr, is always an interesting but knotty problem no matter in the field of metallurgy or chemistry. Based on the same theory foundation (Scott's two-fluid theory and Scatchard-Hildebrand theory) but on different assumptions, a model with two different forms for excess Gibbs energy of liquid mixtures is derived from statistical thermodynamics, in which volume parameter and energy parameter are separated. Under the same conditions, compared with classical local composition models and sub-regular solution model (SRSM), this model not only has a significant increase (more than 30%) in predictive capability, but also shows stronger adaptability to various highly irregular systems, especially to super negative deviation systems (SNDS) whose activity can be as low as 10−11 and to low concentration saturated solution (LCSS) with strong asymmetry.